Neuroscience › Cognitive Neuroscience

Neural dynamics and brain function

Works Count: 112706

Wikipedia

Description

This cluster of papers explores the dynamics of neuronal oscillations, synchronization, and neural activity in cortical networks. It investigates the role of gamma rhythms, interneurons, and sensory processing, as well as their implications for working memory, neural synchrony, and cognitive functions.

Keywords

Neuronal Oscillations; Cortical Networks; Synchronization; Gamma Rhythms; Neural Activity; Interneurons; Sensory Processing; Working Memory; Neural Synchrony; Cognitive Functions

The brainweb: Phase synchronization and large-scale integration

2001-04-01

Francisco J. Varela , Jean-Philippe Lachaux , Eugenio Rodríguez +1 more

Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex

1982-01-01

EL Bienenstock , L N Cooper , PW Munro

The development of stimulus selectivity in the primary sensory cortex of higher vertebrates is considered in a general mathematical framework. A synaptic evolution scheme of a new kind is proposed … The development of stimulus selectivity in the primary sensory cortex of higher vertebrates is considered in a general mathematical framework. A synaptic evolution scheme of a new kind is proposed in which incoming patterns rather than converging afferents compete. The change in the efficacy of a given synapse depends not only on instantaneous pre- and postsynaptic activities but also on a slowly varying time-averaged value of the postsynaptic activity. Assuming an appropriate nonlinear form for this dependence, development of selectivity is obtained under quite general conditions on the sensory environment. One does not require nonlinearity of the neuron's integrative power nor does one need to assume any particular form for intracortical circuitry. This is first illustrated in simple cases, e.g., when the environment consists of only two different stimuli presented alternately in a random manner. The following formal statement then holds: the state of the system converges with probability 1 to points of maximum selectivity in the state space. We next consider the problem of early development of orientation selectivity and binocular interaction in primary visual cortex. Giving the environment an appropriate form, we obtain orientation tuning curves and ocular dominance comparable to what is observed in normally reared adult cats or monkeys. Simulations with binocular input and various types of normal or altered environments show good agreement with the relevant experimental data. Experiments are suggested that could test our theory further.

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The capacity of visual working memory for features and conjunctions

1997-11-01

Steven J. Luck , Edward K. Vogel

The human brain is intrinsically organized into dynamic, anticorrelated functional networks

2005-06-23

Michael Fox , Abraham Z. Snyder , Justin L. Vincent +3 more

During performance of attention-demanding cognitive tasks, certain regions of the brain routinely increase activity, whereas others routinely decrease activity. In this study, we investigate the extent to which this task-related … During performance of attention-demanding cognitive tasks, certain regions of the brain routinely increase activity, whereas others routinely decrease activity. In this study, we investigate the extent to which this task-related dichotomy is represented intrinsically in the resting human brain through examination of spontaneous fluctuations in the functional MRI blood oxygen level-dependent signal. We identify two diametrically opposed, widely distributed brain networks on the basis of both spontaneous correlations within each network and anticorrelations between networks. One network consists of regions routinely exhibiting task-related activations and the other of regions routinely exhibiting task-related deactivations. This intrinsic organization, featuring the presence of anticorrelated networks in the absence of overt task performance, provides a critical context in which to understand brain function. We suggest that both task-driven neuronal responses and behavior are reflections of this dynamic, ongoing, functional organization of the brain.

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PsychoPy—Psychophysics software in Python

2007-01-24

Jonathan W. Peirce

The vast majority of studies into visual processing are conducted using computer display technology. The current paper describes a new free suite of software tools designed to make this task … The vast majority of studies into visual processing are conducted using computer display technology. The current paper describes a new free suite of software tools designed to make this task easier, using the latest advances in hardware and software. PsychoPy is a platform-independent experimental control system written in the Python interpreted language using entirely free libraries. PsychoPy scripts are designed to be extremely easy to read and write, while retaining complete power for the user to customize the stimuli and environment. Tools are provided within the package to allow everything from stimulus presentation and response collection (from a wide range of devices) to simple data analysis such as psychometric function fitting. Most importantly, PsychoPy is highly extensible and the whole system can evolve via user contributions. If a user wants to add support for a particular stimulus, analysis or hardware device they can look at the code for existing examples, modify them and submit the modifications back into the package so that the whole community benefits.

The discovery of processing stages: Extensions of Donders' method

1969-01-01

Saul Sternberg

The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes

2012-05-18

György Buzsáki , Costas A. Anastassiou , Christof Koch

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Shaping Functional Architecture by Oscillatory Alpha Activity: Gating by Inhibition

2010-01-01

Ole Jensen , Ali Mazaheri

In order to understand the working brain as a network, it is essential to identify the mechanisms by which information is gated between regions. We here propose that information is … In order to understand the working brain as a network, it is essential to identify the mechanisms by which information is gated between regions. We here propose that information is gated by inhibiting task-irrelevant regions, thus routing information to task-relevant regions. The functional inhibition is reflected in oscillatory activity in the alpha band (8-13 Hz). From a physiological perspective the alpha activity provides pulsed inhibition reducing the processing capabilities of a given area. Active processing in the engaged areas is reflected by neuronal synchronization in the gamma band (30-100 Hz) accompanied by an alpha band decrease. According to this framework the brain could be studied as a network by investigating cross-frequency interactions between gamma and alpha activity. Specifically the framework predicts that optimal task performance will correlate with alpha activity in task-irrelevant areas. In this review we will discuss the empirical support for this framework. Given that alpha activity is by far the strongest signal recorded by EEG and MEG, we propose that a major part of the electrophysiological activity detected from the working brain reflects gating by inhibition.

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Circular analysis in systems neuroscience: the dangers of double dipping

2009-04-26

Nikolaus Kriegeskorte , W. Kyle Simmons , Patrick S.F. Bellgowan +1 more

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Evoked-Potential Correlates of Stimulus Uncertainty

1965-11-26

Samuel Sutton , Margery Braren , Joseph Zubin +1 more

The average evoked-potential waveforms to sound and light stimuli recorded from scalp in awake human subjects show differences as a function of the subject's degree of uncertainty with respect to … The average evoked-potential waveforms to sound and light stimuli recorded from scalp in awake human subjects show differences as a function of the subject's degree of uncertainty with respect to the sensory modality of the stimulus to be presented. Differences are also found in the evoked potential as a function of whether or not the sensorymodality of the stimulus was anticipated correctly. The major waveform alteration is in the amplitude of a positive-going component which reaches peak amplitude at about 300 milliseconds.

Interneurons of the neocortical inhibitory system

2004-09-20

Henry Markram , Maria Toledo‐Rodriguez , Yun Wang +3 more

Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain

1994-10-01

Roberto D. Pascual‐Marqui , Christoph M. Michel , Dietrich Lehmann

An Energy Budget for Signaling in the Grey Matter of the Brain

2001-10-01

David Attwell , Simon B. Laughlin

Anatomic and physiologic data are used to analyze the energy expenditure on different components of excitatory signaling in the grey matter of rodent brain. Action potentials and postsynaptic effects of … Anatomic and physiologic data are used to analyze the energy expenditure on different components of excitatory signaling in the grey matter of rodent brain. Action potentials and postsynaptic effects of glutamate are predicted to consume much of the energy (47% and 34%, respectively), with the resting potential consuming a smaller amount (13%), and glutamate recycling using only 3%. Energy usage depends strongly on action potential rate--an increase in activity of 1 action potential/cortical neuron/s will raise oxygen consumption by 145 mL/100 g grey matter/h. The energy expended on signaling is a large fraction of the total energy used by the brain; this favors the use of energy efficient neural codes and wiring patterns. Our estimates of energy usage predict the use of distributed codes, with <or=15% of neurons simultaneously active, to reduce energy consumption and allow greater computing power from a fixed number of neurons. Functional magnetic resonance imaging signals are likely to be dominated by changes in energy usage associated with synaptic currents and action potential propagation.

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A Learning Algorithm for Boltzmann Machines*

1985-01-01

David H. Ackley , Geoffrey E. Hinton , Terrence J. Sejnowski

The computational power of massively parallel networks of simple processing elements resides in the communication bandwidth provided by the hardware connections between elements. These connections can allow a significant fraction … The computational power of massively parallel networks of simple processing elements resides in the communication bandwidth provided by the hardware connections between elements. These connections can allow a significant fraction of the knowledge of the system to be applied to an instance of a problem in a very short time. One kind of computation for which massively parallel networks appear to be well suited is large constraint satisfaction searches, but to use the connections efficiently two conditions must be met: First, a search technique that is suitable for parallel networks must be found. Second, there must be some way of choosing internal representations which allow the preexisting hardware connections to be used efficiently for encoding the constraints in the domain being searched. We describe a general parallel search method, based on statistical mechanics, and we show how it leads to a general learning rule for modifying the connection strengths so as to incorporate knowledge about a task domain in an efficient way. We describe some simple examples in which the learning algorithm creates internal representations that are demonstrably the most efficient way of using the preexisting connectivity structure.

Alpha-band oscillations, attention, and controlled access to stored information

2012-11-07

Wolfgang Klimesch

Alpha-band oscillations are the dominant oscillations in the human brain and recent evidence suggests that they have an inhibitory function. Nonetheless, there is little doubt that alpha-band oscillations also play … Alpha-band oscillations are the dominant oscillations in the human brain and recent evidence suggests that they have an inhibitory function. Nonetheless, there is little doubt that alpha-band oscillations also play an active role in information processing. In this article, I suggest that alpha-band oscillations have two roles (inhibition and timing) that are closely linked to two fundamental functions of attention (suppression and selection), which enable controlled knowledge access and semantic orientation (the ability to be consciously oriented in time, space, and context). As such, alpha-band oscillations reflect one of the most basic cognitive processes and can also be shown to play a key role in the coalescence of brain activity in different frequencies.

Dynamic predictions: Oscillations and synchrony in top–down processing

2001-10-01

Andreas K. Engel , Pascal Fries , Wolf Singer

Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties

1989-03-01

Charles M. Gray , Peter König , Andreas K. Engel +1 more

Speed of processing in the human visual system

1996-06-01

Simon J. Thorpe , Denis Fize , Catherine Marlot

EEG alpha oscillations: The inhibition–timing hypothesis

2006-08-02

Wolfgang Klimesch , Paul Sauseng , Simon Hanslmayr

Distributed Hierarchical Processing in the Primate Cerebral Cortex

1991-01-01

D.J. Felleman , David C. Van Essen

Journal Article Preface: Cerebral Cortex Has Come of Age Get access Patricia S. Goldman-Rakic, Patricia S. Goldman-Rakic Search for other works by this author on: Oxford Academic PubMed Google Scholar … Journal Article Preface: Cerebral Cortex Has Come of Age Get access Patricia S. Goldman-Rakic, Patricia S. Goldman-Rakic Search for other works by this author on: Oxford Academic PubMed Google Scholar Pasko Rakic Pasko Rakic Search for other works by this author on: Oxford Academic PubMed Google Scholar Cerebral Cortex, Volume 1, Issue 1, January 1991, Page 1, https://doi.org/10.1093/cercor/1.1.1 Published: 01 January 1991

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Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position

1980-04-01

Kunihiko Fukushima

Receptive fields of single neurones in the cat's striate cortex

1959-10-01

David H. Hubel , T. N. Wiesel

A mechanism for cognitive dynamics: neuronal communication through neuronal coherence

2005-09-17

Pascal Fries

Sparse coding with an overcomplete basis set: A strategy employed by V1?

1997-12-01

Bruno A. Olshausen , David J. Field

The spatial receptive fields of simple cells in mammalian striate cortex have been reasonably well described physiologically and can be characterized as being localized, oriented, and bandpass, comparable with the … The spatial receptive fields of simple cells in mammalian striate cortex have been reasonably well described physiologically and can be characterized as being localized, oriented, and bandpass, comparable with the basis functions of wavelet transforms. Previously, we have shown that these receptive field properties may be accounted for in terms of a strategy for producing a sparse distribution of output activity in response to natural images. Here, in addition to describing this work in a more expansive fashion, we examine the neurobiological implications of sparse coding. Of particular interest is the case when the code is overcomplete--i.e., when the number of code elements is greater than the effective dimensionality of the input space. Because the basis functions are non-orthogonal and not linearly independent of each other, sparsifying the code will recruit only those basis functions necessary for representing a given input, and so the input-output function will deviate from being purely linear. These deviations from linearity provide a potential explanation for the weak forms of non-linearity observed in the response properties of cortical simple cells, and they further make predictions about the expected interactions among units in response to naturalistic stimuli.

Regional differences in synaptogenesis in human cerebral cortex

1997-10-20

Peter R. Huttenlocher , Arun S. Dabholkar

The formation of synaptic contacts in human cerebral cortex was compared in two cortical regions: auditory cortex (Heschl's gyrus) and prefrontal cortex (middle frontal gyrus). Synapse formation in both cortical … The formation of synaptic contacts in human cerebral cortex was compared in two cortical regions: auditory cortex (Heschl's gyrus) and prefrontal cortex (middle frontal gyrus). Synapse formation in both cortical regions begins in the fetus, before conceptual age 27 weeks. Synaptic density increases more rapidly in auditory cortex, where the maximum is reached near postnatal age 3 months. Maximum synaptic density in middle frontal gyrus is not reached until after age 15 months. Synaptogenesis occurs concurrently with dendritic and axonal growth and with myelination of the subcortical white matter. A phase of net synapse elimination occurs late in childhood, earlier in auditory cortex, where it has ended by age 12 years, than in prefrontal cortex, where it extends to midadolescence. Synaptogenesis and synapse elimination in humans appear to be heterochronous in different cortical regions and, in that respect, appears to differ from the rhesus monkey, where they are concurrent. In other respects, including overproduction of synaptic contacts in infancy, persistence of high levels of synaptic density to late childhood or adolescence, the absolute values of maximum and adult synaptic density, and layer specific differences, findings in the human resemble those in rhesus monkeys. J. Comp. Neurol. 387:167–178, 1997. © 1997 Wiley-Liss, Inc.

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A sensorimotor account of vision and visual consciousness

2001-10-01

J. Kevin Ο’Regan , Alva Noë

Many current neurophysiological, psychophysical, and psychological approaches to vision rest on the idea that when we see, the brain produces an internal representation of the world. The activation of this … Many current neurophysiological, psychophysical, and psychological approaches to vision rest on the idea that when we see, the brain produces an internal representation of the world. The activation of this internal representation is assumed to give rise to the experience of seeing. The problem with this kind of approach is that it leaves unexplained how the existence of such a detailed internal representation might produce visual consciousness. An alternative proposal is made here. We propose that seeing is a way of acting. It is a particular way of exploring the environment. Activity in internal representations does not generate the experience of seeing. The outside world serves as its own, external, representation. The experience of seeing occurs when the organism masters what we call the governing laws of sensorimotor contingency. The advantage of this approach is that it provides a natural and principled way of accounting for visual consciousness, and for the differences in the perceived quality of sensory experience in the different sensory modalities. Several lines of empirical evidence are brought forward in support of the theory, in particular: evidence from experiments in sensorimotor adaptation, visual “filling in,” visual stability despite eye movements, change blindness, sensory substitution, and color perception.

Neurons with graded response have collective computational properties like those of two-state neurons.

1984-05-01

J. J. Hopfield

A model for a large network of "neurons" with a graded response (or sigmoid input-output relation) is studied. This deterministic system has collective properties in very close correspondence with the … A model for a large network of "neurons" with a graded response (or sigmoid input-output relation) is studied. This deterministic system has collective properties in very close correspondence with the earlier stochastic model based on McCulloch - Pitts neurons. The content- addressable memory and other emergent collective properties of the original model also are present in the graded response model. The idea that such collective properties are used in biological systems is given added credence by the continued presence of such properties for more nearly biological "neurons." Collective analog electrical circuits of the kind described will certainly function. The collective states of the two models have a simple correspondence. The original model will continue to be useful for simulations, because its connection to graded response systems is established. Equations that include the effect of action potentials in the graded response system are also developed.

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Neurophysiological investigation of the basis of the fMRI signal

2001-07-12

Nikos K. Logothetis , J Pauls , M Augath +2 more

AN INTEGRATIVE THEORY OF LOCUS COERULEUS-NOREPINEPHRINE FUNCTION: Adaptive Gain and Optimal Performance

2005-07-20

Gary Aston‐Jones , Jonathan D. Cohen

Historically, the locus coeruleus-norepinephrine (LC-NE) system has been implicated in arousal, but recent findings suggest that this system plays a more complex and specific role in the control of behavior … Historically, the locus coeruleus-norepinephrine (LC-NE) system has been implicated in arousal, but recent findings suggest that this system plays a more complex and specific role in the control of behavior than investigators previously thought. We review neurophysiological and modeling studies in monkey that support a new theory of LC-NE function. LC neurons exhibit two modes of activity, phasic and tonic. Phasic LC activation is driven by the outcome of task-related decision processes and is proposed to facilitate ensuing behaviors and to help optimize task performance (exploitation). When utility in the task wanes, LC neurons exhibit a tonic activity mode, associated with disengagement from the current task and a search for alternative behaviors (exploration). Monkey LC receives prominent, direct inputs from the anterior cingulate (ACC) and orbitofrontal cortices (OFC), both of which are thought to monitor task-related utility. We propose that these frontal areas produce the above patterns of LC activity to optimize utility on both short and long timescales.

Measuring phase synchrony in brain signals

1999-01-01

Jean-Philippe Lachaux , Eugenio Rodríguez , Jacques Martinerie +1 more

This article presents, for the first time, a practical method for the direct quantification of frequency-specific synchronization (i.e., transient phase-locking) between two neuroelectric signals. The motivation for its development is … This article presents, for the first time, a practical method for the direct quantification of frequency-specific synchronization (i.e., transient phase-locking) between two neuroelectric signals. The motivation for its development is to be able to examine the role of neural synchronies as a putative mechanism for long-range neural integration during cognitive tasks. The method, called phase-locking statistics (PLS), measures the significance of the phase covariance between two signals with a reasonable time-resolution (<100 ms). Unlike the more traditional method of spectral coherence, PLS separates the phase and amplitude components and can be directly interpreted in the framework of neural integration. To validate synchrony values against background fluctuations, PLS uses surrogate data and thus makes no a priori assumptions on the nature of the experimental data. We also apply PLS to investigate intracortical recordings from an epileptic patient performing a visual discrimination task. We find large-scale synchronies in the gamma band (45 Hz), e.g., between hippocampus and frontal gyrus, and local synchronies, within a limbic region, a few cm apart. We argue that whereas long-scale effects do reflect cognitive processing, short-scale synchronies are likely to be due to volume conduction. We discuss ways to separate such conduction effects from true signal synchrony. Hum Brain Mapping 8:194–208, 1999. © 1999 Wiley-Liss, Inc.

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Event-related EEG/MEG synchronization and desynchronization: basic principles

1999-11-01

G. Pfurtscheller , F.H. Lopes da Silva

The NEURON Simulation Environment

1997-08-01

Michael L. Hines , Nicholas T. Carnevale

The moment-to-moment processing of information by the nervous system involves the propagation and interaction of electrical and chemical signals that are distributed in space and time. Biologically realistic modeling is … The moment-to-moment processing of information by the nervous system involves the propagation and interaction of electrical and chemical signals that are distributed in space and time. Biologically realistic modeling is needed to test hypotheses about the mechanisms that govern these signals and how nervous system function emerges from the operation of these mechanisms. The NEURON simulation program provides a powerful and flexible environment for implementing such models of individual neurons and small networks of neurons. It is particularly useful when membrane potential is nonuniform and membrane currents are complex. We present the basic ideas that would help informed users make the most efficient use of NEURON.

A theory of cortical responses

2005-04-29

Karl Friston

This article concerns the nature of evoked brain responses and the principles underlying their generation. We start with the premise that the sensory brain has evolved to represent or infer … This article concerns the nature of evoked brain responses and the principles underlying their generation. We start with the premise that the sensory brain has evolved to represent or infer the causes of changes in its sensory inputs. The problem of inference is well formulated in statistical terms. The statistical fundaments of inference may therefore afford important constraints on neuronal implementation. By formulating the original ideas of Helmholtz on perception, in terms of modern-day statistical theories, one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. It turns out that the problems of inferring the causes of sensory input (perceptual inference) and learning the relationship between input and cause (perceptual learning) can be resolved using exactly the same principle. Specifically, both inference and learning rest on minimizing the brain's free energy, as defined in statistical physics. Furthermore, inference and learning can proceed in a biologically plausible fashion. Cortical responses can be seen as the brain’s attempt to minimize the free energy induced by a stimulus and thereby encode the most likely cause of that stimulus. Similarly, learning emerges from changes in synaptic efficacy that minimize the free energy, averaged over all stimuli encountered. The underlying scheme rests on empirical Bayes and hierarchical models of how sensory input is caused. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of cortical organization and responses. The aim of this article is to encompass many apparently unrelated anatomical, physiological and psychophysical attributes of the brain within a single theoretical perspective. In terms of cortical architectures, the theoretical treatment predicts that sensory cortex should be arranged hierarchically, that connections should be reciprocal and that forward and backward connections should show a functional asymmetry (forward connections are driving, whereas backward connections are both driving and modulatory). In terms of synaptic physiology, it predicts associative plasticity and, for dynamic models, spike-timing-dependent plasticity. In terms of electrophysiology, it accounts for classical and extra classical receptive field effects and long-latency or endogenous components of evoked cortical responses. It predicts the attenuation of responses encoding prediction error with perceptual learning and explains many phenomena such as repetition suppression, mismatch negativity (MMN) and the P300 in electroencephalography. In psychophysical terms, it accounts for the behavioural correlates of these physiological phenomena, for example, priming and global precedence. The final focus of this article is on perceptual learning as measured with the MMN and the implications for empirical studies of coupling among cortical areas using evoked sensory responses.

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Neural networks and physical systems with emergent collective computational abilities.

1982-04-01

J. J. Hopfield

Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). … Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.

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Saliency, switching, attention and control: a network model of insula function

2010-05-28

Vinod Menon , Lucina Q. Uddin

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Noise in the nervous system

2008-03-05

A. Aldo Faisal , Luc P. J. Selen , Daniel M. Wolpert

Neuronal Oscillations in Cortical Networks

2004-06-24

György Buzsáki , Andreas Draguhn

Clocks tick, bridges and skyscrapers vibrate, neuronal networks oscillate. Are neuronal oscillations an inevitable by-product, similar to bridge vibrations, or an essential part of the brain's design? Mammalian cortical neurons … Clocks tick, bridges and skyscrapers vibrate, neuronal networks oscillate. Are neuronal oscillations an inevitable by-product, similar to bridge vibrations, or an essential part of the brain's design? Mammalian cortical neurons form behavior-dependent oscillating networks of various sizes, which span five orders of magnitude in frequency. These oscillations are phylogenetically preserved, suggesting that they are functionally relevant. Recent findings indicate that network oscillations bias input selection, temporally link neurons into assemblies, and facilitate synaptic plasticity, mechanisms that cooperatively support temporal representation and long-term consolidation of information.

Human-level control through deep reinforcement learning

2015-02-24

Volodymyr Mnih , Koray Kavukcuoglu , David Silver +7 more

Emergence of simple-cell receptive field properties by learning a sparse code for natural images

1996-06-01

Bruno A. Olshausen , David J. Field

The free-energy principle: a unified brain theory?

2010-01-13

Karl Friston

The polyvagal perspective

2006-10-18

Stephen W. Porges

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Neuronal Synchrony: A Versatile Code for the Definition of Relations?

1999-09-01

Wolf Singer

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Simple model of spiking neurons

2003-11-01

Eugene M. Izhikevich

A model is presented that reproduces spiking and bursting behavior of known types of cortical neurons. The model combines the biologically plausibility of Hodgkin-Huxley-type dynamics and the computational efficiency of … A model is presented that reproduces spiking and bursting behavior of known types of cortical neurons. The model combines the biologically plausibility of Hodgkin-Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons. Using this model, one can simulate tens of thousands of spiking cortical neurons in real time (1 ms resolution) using a desktop PC.

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FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data

2010-12-23

Robert Oostenveld , Pascal Fries , Eric Maris +1 more

This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox … This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages.

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Relations between the statistics of natural images and the response properties of cortical cells

1987-12-01

David Field

The relative efficiency of any particular image-coding scheme should be defined only in relation to the class of images that the code is likely to encounter. To understand the representation … The relative efficiency of any particular image-coding scheme should be defined only in relation to the class of images that the code is likely to encounter. To understand the representation of images by the mammalian visual system, it might therefore be useful to consider the statistics of images from the natural environment (i.e., images with trees, rocks, bushes, etc). In this study, various coding schemes are compared in relation to how they represent the information in such natural images. The coefficients of such codes are represented by arrays of mechanisms that respond to local regions of space, spatial frequency, and orientation (Gabor-like transforms). For many classes of image, such codes will not be an efficient means of representing information. However, the results obtained with six natural images suggest that the orientation and the spatial-frequency tuning of mammalian simple cells are well suited for coding the information in such images if the goal of the code is to convert higher-order redundancy (e.g., correlation between the intensities of neighboring pixels) into first-order redundancy (i.e., the response distribution of the coefficients). Such coding produces a relatively high signal-to-noise ratio and permits information to be transmitted with only a subset of the total number of cells. These results support Barlow's theory that the goal of natural vision is to represent the information in the natural environment with minimal redundancy.

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Modulation of Oscillatory Neuronal Synchronization by Selective Visual Attention

2001-02-23

Pascal Fries , John H. Reynolds , Alan E. Rorie +1 more

In crowded visual scenes, attention is needed to select relevant stimuli. To study the underlying mechanisms, we recorded neurons in cortical area V4 while macaque monkeys attended to behaviorally relevant … In crowded visual scenes, attention is needed to select relevant stimuli. To study the underlying mechanisms, we recorded neurons in cortical area V4 while macaque monkeys attended to behaviorally relevant stimuli and ignored distracters. Neurons activated by the attended stimulus showed increased gamma-frequency (35 to 90 hertz) synchronization but reduced low-frequency (<17 hertz) synchronization compared with neurons at nearby V4 sites activated by distracters. Because postsynaptic integration times are short, these localized changes in synchronization may serve to amplify behaviorally relevant signals in the cortex.

PsychoPy2: Experiments in behavior made easy

2019-02-01

Jonathan W. Peirce , Jeremy Gray , Sol Simpson +5 more

PsychoPy is an application for the creation of experiments in behavioral science (psychology, neuroscience, linguistics, etc.) with precise spatial control and timing of stimuli. It now provides a choice of … PsychoPy is an application for the creation of experiments in behavioral science (psychology, neuroscience, linguistics, etc.) with precise spatial control and timing of stimuli. It now provides a choice of interface; users can write scripts in Python if they choose, while those who prefer to construct experiments graphically can use the new Builder interface. Here we describe the features that have been added over the last 10 years of its development. The most notable addition has been that Builder interface, allowing users to create studies with minimal or no programming, while also allowing the insertion of Python code for maximal flexibility. We also present some of the other new features, including further stimulus options, asynchronous time-stamped hardware polling, and better support for open science and reproducibility. Tens of thousands of users now launch PsychoPy every month, and more than 90 people have contributed to the code. We discuss the current state of the project, as well as plans for the future.

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Abstract rule learning promotes cognitive flexibility in complex environments across species

2025-06-25

Florian Bähner , Tzvetan Popov , Nico Boehme +7 more | Nature Communications

Testing Sensorimotor Timing in a Living Laboratory: Behavioral Signatures of a Neural Oscillator

2025-06-25

H Serre , Keith Harrigian , Se‐Woong Park +1 more | bioRxiv (Cold Spring Harbor Laboratory)

Rhythmic ability has been studied for more than a century in laboratory settings testing timed finger taps. While robust results emerged, it remains unclear whether these findings reflect behavioral limitations … Rhythmic ability has been studied for more than a century in laboratory settings testing timed finger taps. While robust results emerged, it remains unclear whether these findings reflect behavioral limitations in realistic scenarios. This study tested the synchronization-continuation task in a museum with 455 visitors of a wide variety of ages (5-74yrs), musical experiences (0-40yrs) and educational and cultural backgrounds. Adopting a dynamic systems perspective, three metronome pacing periods were anchored around each individuals preferred tempo, and 20% faster and 20% slower. Key laboratory findings were replicated and extended: timing error and variability decreased during childhood and increased in older adults and were lower, even with moderate musical experience. Consistent with an oscillator perspective, timing at non-preferred tempi drifted toward their preferred rate. Overall, these findings demonstrate that timing limitations may reflect attractor properties of a neural oscillator and its signature is still present even in noisy, naturalistic settings.

State modulation in spatial networks with three interneuron subtypes

2025-06-25

Madeline M. Edwards , Jonathan Rubin , Chengcheng Huang | Science Advances

Several inhibitory interneuron subtypes have been identified as critical in regulating sensory responses. However, the specific contribution of each interneuron subtype remains uncertain. In this work, we explore the contributions … Several inhibitory interneuron subtypes have been identified as critical in regulating sensory responses. However, the specific contribution of each interneuron subtype remains uncertain. In this work, we explore the contributions of cell type-specific activity and synaptic connections to the dynamics of a spatially organized spiking neuron network. We find that the firing rates of the somatostatin (SOM) interneurons align closely with the level of network synchrony irrespective of the target of modulatory input. Further analysis reveals that inhibition from SOM to parvalbumin interneurons must be limited to allow gradual transitions from asynchrony to synchrony and that the strength of recurrent excitation onto SOM neurons determines the level of synchrony achievable in the network. Our results are consistent with recent experimental findings on cell type-specific manipulations. Overall, our results highlight common dynamic regimes achieved across modulations of different cell populations and identify SOM cells as the main driver of network synchrony.

Sensory population activity reveals downstream confidence computations in the primate visual system

2025-06-25

Zoe M. Boundy-Singer , Corey M. Ziemba , Robbe L. T. Goris | Proceedings of the National Academy of Sciences

Perception is fallible. Humans know this, and so do some nonhuman animals like macaque monkeys. When monkeys report more confidence in a perceptual decision, that decision is more likely to … Perception is fallible. Humans know this, and so do some nonhuman animals like macaque monkeys. When monkeys report more confidence in a perceptual decision, that decision is more likely to be correct. It is not known how neural circuits in the primate brain assess the quality of perceptual decisions. Here, we test two hypotheses. First, that decision confidence is related to the structure of population activity in the sensory cortex. And second, that this relation differs from the one between sensory activity and decision content. We trained macaque monkeys to judge the orientation of ambiguous stimuli and additionally report their confidence in these judgments. We recorded population activity in the primary visual cortex and used decoders to expose the relationship between this activity and the choice-confidence reports. Our analysis validated both hypotheses and suggests that perceptual decisions arise from a neural computation downstream of visual cortex that estimates the most likely interpretation of a sensory response, while decision confidence instead reflects a computation that evaluates whether this sensory response will produce a reliable decision. Our work establishes a direct link between neural population activity in the sensory cortex and the metacognitive ability to introspect about the quality of perceptual decisions.

The dynamics and geometry of choice in the premotor cortex

2025-06-25

Mikhail Genkin , Krishna V. Shenoy , Chandramouli Chandrasekaran +1 more | Nature

Abstract The brain represents sensory variables in the coordinated activity of neural populations, in which tuning curves of single neurons define the geometry of the population code 1,2 . Whether … Abstract The brain represents sensory variables in the coordinated activity of neural populations, in which tuning curves of single neurons define the geometry of the population code 1,2 . Whether the same coding principle holds for dynamic cognitive variables remains unknown because internal cognitive processes unfold with a unique time course on single trials observed only in the irregular spiking of heterogeneous neural populations 3–8 . Here we show the existence of such a population code for the dynamics of choice formation in the primate premotor cortex. We developed an approach to simultaneously infer population dynamics and tuning functions of single neurons to the population state. Applied to spike data recorded during decision-making, our model revealed that populations of neurons encoded the same dynamic variable predicting choices, and heterogeneous firing rates resulted from the diverse tuning of single neurons to this decision variable. The inferred dynamics indicated an attractor mechanism for decision computation. Our results reveal a unifying geometric principle for neural encoding of sensory and dynamic cognitive variables.

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New Neurons, Fewer Seizures: Environmental Enrichment Strengthens Brain Circuits

2025-06-25

Bin Gu | Epiliepsy currents/Epilepsy currents

Enriched Environment Reduces Seizure Susceptibility via EC Circuit Augmented Adult Neurogenesis Li Z, Chen L, Fei F, Wang W, Yang L, Wang Y, Cheng H, Xu Y, Xu C, Wang … Enriched Environment Reduces Seizure Susceptibility via EC Circuit Augmented Adult Neurogenesis Li Z, Chen L, Fei F, Wang W, Yang L, Wang Y, Cheng H, Xu Y, Xu C, Wang S, Gu Y, Han F, Chen Z, Wang Y. Adv. Sci. 2024; 11: 2410927. doi: 10.1002/advs.202410927. Enriched environment (EE), characterized by multi-sensory stimulation, represents a non-invasive alternative for alleviating epileptic seizures. However, the mechanism by which EE exerts its therapeutic impact remains incompletely understood. Here, it is elucidated that EE mitigates seizure susceptibility through the augmentation of adult neurogenesis within the EC circuit. A substantial upregulation of adult hippocampal neurogenesis concomitant with a notable reduction in seizure susceptibility has been found following exposure to EE. EE-enhanced adult-born dentate granule cells (abDGCs) are functionally activated during seizure events. Importantly, the selective activation of abDGCs mimics the anti-seizure effects observed with EE, while their inhibition negates these effects. Further, whole-brain c-Fos mapping demonstrates increased activity in DG-projecting EC CaMKIIα+ neurons in response to EE. Crucially, EC CaMKIIα+ neurons exert bidirectional modulation over the proliferation and maturation of abDGCs that can activate local GABAergic interneurons; thus, they are essential components for the anti-seizure effects mediated by EE. Collectively, this study provides compelling evidence regarding the circuit mechanisms underlying the effects of EE treatment on epileptic seizures, shedding light on the involvement of the EC-DG circuit in augmenting the functionality of abDGCs. This may help for the translational application of EE for epilepsy management.

Low frequency oscillations – neural correlates of stability and flexibility in cognition

2025-06-25

Julia Ericson , J N Ibánez , Mikael Lundqvist +1 more | Nature Communications

Confirmation bias through selective readout of information encoded in human parietal cortex

2025-06-25

Hame Park , Ayelet Arazi , Bharath Chandra Talluri +4 more | Nature Communications

Purkinje cell spatial correlation dynamics are key to cerebellar cortical contributions to behavior

2025-06-24

Martha L. Streng , Russell E. Carter , B.W. Kottke +6 more | Journal of Neuroscience

A major challenge in cerebellar physiology is determining how the stereotypic, conserved circuitry of the cerebellar cortex, with its dominant parasagittal and transverse architectures, underlies its fundamental computations and contributions … A major challenge in cerebellar physiology is determining how the stereotypic, conserved circuitry of the cerebellar cortex, with its dominant parasagittal and transverse architectures, underlies its fundamental computations and contributions to behavior. Recent advances have allowed for the resolution of Purkinje cell dendritic activity at large scales, but the full roles of these Purkinje cell dynamics during behavior remain undetermined. To interrogate Purkinje cell dynamics at the population level during behavior, we implemented a novel approach for awake, chronic, wide-field Ca 2+ imaging of the cerebellar cortex. We performed wide-field cerebellar recordings in mice of both sexes exhibiting sparse expression of the Ca 2+ indicator GCaMP6s, which importantly allowed for the resolution of both dendritic and somatic Purkinje cell activity. Blind source separation of wide-field dynamics using spatial independent component analysis (sICA) extracts components consisting of either Purkinje cell dendrites or somata, with distinct activity and spatial properties. These independent components (ICs) tend to be either parasagittally organized and likely reflective of dendritic activity, or more spatially distributed populations of Purkinje cell somata. We observe broad, bilateral activation of both these dendritic and somatic ICs during behavior, but they exhibit distinct and divergent patterns of spatial correlations occurring primarily along the parasagittal and transverse directions, consistent with the main geometry of the cerebellar cortex. Somatic correlation dynamics are robustly modulated by prediction errors and reflect ultimate behavioral outcomes. These results provide a novel link between cerebellar structure and function, with the correlation dynamics of Purkinje cell activity a key feature during behavior. Significance statement The cerebellar cortex exhibits highly conserved, elegant cytoarchitecture, but a full understanding of how this organization contributes to cerebellar processing is limited. We performed wide-field Ca 2+ recordings of the primary output neurons of the cerebellar cortex, Purkinje cells, and find that they are organized into distinct networks, which are either parasagittally organized or distributed populations of somatic activity. While both networks are highly engaged during behavior, they exhibit distinct spatial correlation dynamics consistent with the main geometry of the cerebellar cortex, with somatic correlation dynamics conveying information about prediction error and behavioral outcomes. Together, these results provide new insights into the functional organization of Purkinje cells and implicate somatic network correlation dynamics as a key feature of cerebellar processing.

Activity-dependent development of synaptic circuits mediates direction selectivity in an axis-specific manner

2025-06-24

Karina Bistrong , Rachana Deven Somaiya , Eugene Y. Liang +2 more | Cell Reports

Collective behaviors of some functional map neurons

2025-06-24

Guoping Sun , Zhao Yao , Ya Wang +2 more | The European Physical Journal Special Topics

Dimensions underlying the representational alignment of deep neural networks with humans

2025-06-23

Florian P Mahner , Lukas Muttenthaler , Umut Güçlü +1 more | Nature Machine Intelligence

Determining the similarities and differences between humans and artificial intelligence (AI) is an important goal in both computational cognitive neuroscience and machine learning, promising a deeper understanding of human cognition … Determining the similarities and differences between humans and artificial intelligence (AI) is an important goal in both computational cognitive neuroscience and machine learning, promising a deeper understanding of human cognition and safer, more reliable AI systems. Much previous work comparing representations in humans and AI has relied on global, scalar measures to quantify their alignment. However, without explicit hypotheses, these measures only inform us about the degree of alignment, not the factors that determine it. To address this challenge, we propose a generic framework to compare human and AI representations, based on identifying latent representational dimensions underlying the same behaviour in both domains. Applying this framework to humans and a deep neural network (DNN) model of natural images revealed a low-dimensional DNN embedding of both visual and semantic dimensions. In contrast to humans, DNNs exhibited a clear dominance of visual over semantic properties, indicating divergent strategies for representing images. Although in silico experiments showed seemingly consistent interpretability of DNN dimensions, a direct comparison between human and DNN representations revealed substantial differences in how they process images. By making representations directly comparable, our results reveal important challenges for representational alignment and offer a means for improving their comparability.

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Theta-gamma coupling: nonlinearity as a universal cross-frequency coupling mechanism

2025-06-23

A. Sheremet , Yu Qin | Frontiers in Behavioral Neuroscience

The Cross Frequency Coupling (CFC) phenomenon is defined as a statistical correlation between characteristic parameters neural oscillations. This study demonstrates and analyzes the nonlinear mechanism of the CFC, with a … The Cross Frequency Coupling (CFC) phenomenon is defined as a statistical correlation between characteristic parameters neural oscillations. This study demonstrates and analyzes the nonlinear mechanism of the CFC, with a focus on the coupling between slow and fast oscillations, as a model for theta-gamma coupling. We first discuss the usage of the spectrum/bispectrum CFC measure using experimental data. As a physical paradigm, we propose the concept of a Class II neural population at low activity: neurons fire intermittently, and the time spent in the subthreshold regime is much larger that the duration of an action potential. We verify the emergence of fast oscillations (gamma) using a direct numerical simulations (DNS) of a population of Hodgkin-Huxley neurons forced by a slow theta oscillation. To deconstruct the mechanism, we derive a mean field approximation based on a reduction of the Hodgkin-Huxley model to a two-equation leaky-integrate-and-fire (LIF) model. Under theta forcing, mean field model generates gamma oscillations; the solutions exhibit spectrum/bispectrum CFC patterns that agree qualitatively with both the DNS model and experimental data. For the theta-gamma coupling problem, the mean field model may be linearized using an asymptotic expansion. The analytical solution of the linear system describes theta-gamma interaction as a gamma stabilization/destabilization cycle and provides explicit expressions of the gamma amplitude and frequency modulation by theta. The results demonstrate that nonlinearity as a universal/unifying mechanism of all CFC types.

Rethinking excitation/inhibition balance in the human brain

2025-06-23

Roi Cohen Kadosh | Nature reviews. Neuroscience

Distinct oscillatory mechanisms in low and high alpha-band activities for screening and potential treatment of Schizophrenia

2025-06-23

Chuanliang Han , Bin Wang , Xing Peng +7 more | Translational Psychiatry

Schizophrenia (ScZ) is characterized by prominent perceptual abnormalities. A deeper understanding of the neural mechanisms underlying these abnormalities is crucial for developing precise treatment strategies. Our study aimed to address … Schizophrenia (ScZ) is characterized by prominent perceptual abnormalities. A deeper understanding of the neural mechanisms underlying these abnormalities is crucial for developing precise treatment strategies. Our study aimed to address the following primary questions. First, the functional role of various sub-oscillations within the alpha band remains unclear. Second, we aimed to identify biomarkers for the diagnostic purposes of ScZ. Third, the broader question of whether the diagnostic biomarker can also function as a treatment biomarker remains unknown. Resting-state EEG data from 55 ScZ patients and 61 healthy controls were analyzed to compare different sub-oscillations in the alpha band and their correlation with clinical symptoms (as measured by the general psychopathology scale). We discovered that distinct topographic patterns in low (~8 Hz) and high (~12 Hz) alpha may serve specific diagnostic and evaluative purposes respectively. Moreover, a pronounced gender bias was also observed. Low-alpha-band activity appeared to have more diagnostic relevance in females. On the other hand, the high-alpha difference was more relevant for evaluating the severity of symptoms in ScZ males. Our research has brought new insights into the neural oscillation mechanism of schizophrenia, which could substantially assist the formulating diagnosis of ScZ and the development of its treatment strategies.

The geometry and dimensionality of brain-wide activity

2025-06-23

Zezhen Wang , Weihao Mai , Yuming Chai +7 more | eLife

Understanding neural activity organization is vital for deciphering brain function. By recording whole-brain calcium activity in larval zebrafish during hunting and spontaneous behaviors, we find that the shape of the … Understanding neural activity organization is vital for deciphering brain function. By recording whole-brain calcium activity in larval zebrafish during hunting and spontaneous behaviors, we find that the shape of the neural activity space, described by the neural covariance spectrum, is scale-invariant: a smaller, randomly sampled cell assembly resembles the entire brain. This phenomenon can be explained by Euclidean Random Matrix theory, where neurons are reorganized from anatomical to functional positions based on their correlations. Three factors contribute to the observed scale invariance: slow neural correlation decay, higher functional space dimension, and neural activity heterogeneity. In addition to matching data from zebrafish and mice, our theory and analysis demonstrate how the geometry of neural activity space evolves with population sizes and sampling methods, thus revealing an organizing principle of brain-wide activity.

Protocol for phase-amplitude coupling analysis in local field potentials from macaque monkeys to investigate neural oscillation dynamics

2025-06-23

Mohammad Aliramezani , Ashkan Farrokhi , Christos Constantinidis +1 more | STAR Protocols

Oddball evoked deviant responses reflect complex context dependent expectations in mouse V1

2025-06-23

Scott G Knudstrup , Catalina Martinez Reyes , Cambria M. Jensen +3 more | Journal of Neuroscience

Evoked responses in the mouse primary visual cortex can be modulated by the temporal context in which visual inputs are presented. Oddball stimuli embedded in a sequence of regularly repeated … Evoked responses in the mouse primary visual cortex can be modulated by the temporal context in which visual inputs are presented. Oddball stimuli embedded in a sequence of regularly repeated visual elements have been shown to drive relatively large deviant responses, a finding that is generally consistent with the theory that cortical circuits implement a form of predictive coding. These results can be confounded by short-term adaptation effects, however, that make interpretation difficult. Here we use various forms of the oddball paradigm to disentangle temporal and ordinal components of the deviant response, showing that it is a complex phenomenon affected by temporal structure, ordinal expectation, and event frequency. Specifically, we use visually evoked potentials to show that deviant responses occur over a large range of time in male and female mice, cannot be explained by a simple adaptation model, scale with predictability, and are modulated by violations of both first and second-order sequential expectations. We also show that visual sequences can lead to long-term plasticity in some circumstances.Significance Statement Visual experience and temporal context can modulate evoked responses in mouse V1. There remains disagreement about whether this reflects predictive coding in visual circuits and whether visual mismatched negativity, which has important cross-over implications for human clinical work, constitutes evidence supporting this theory or reflects simple neural adaptation. This work strongly supports the former interpretation by demonstrating complex experience-dependent deviant responses that cannot be easily explained by a simple adaptation model. We use statistically rigorous analysis of the local field potential to show that oddball evoked deviance signals reflect relative timing, event frequency, 1st and 2nd order sequence expectations and scale as a function of event probability.

Optimal inhibitory-to-excitatory ratio governs slow and fast oscillations for enhanced neural communication

2025-06-23

Jung Young Kim , Sang Wan Lee , Demian Battaglia +2 more

Neural oscillations at distinct frequency bands facilitate communication within and between neural populations. While single-frequency oscillations are well-characterized, the simultaneous emergence of slow (beta) and fast (gamma) oscillations within the … Neural oscillations at distinct frequency bands facilitate communication within and between neural populations. While single-frequency oscillations are well-characterized, the simultaneous emergence of slow (beta) and fast (gamma) oscillations within the same network remains unclear. Here, we demonstrate that multi-frequency oscillations naturally arise when the ratio of inhibitory-to-excitatory synaptic strength falls within a specific regime using a biologically plausible Izhikevich model. We show that this regime maximizes both information capacity and transmission efficiency, suggesting an optimal balance for neural communication. Deviations from this range lead to single-frequency oscillations and reduced communication efficiency, mirroring disruptions observed in neurological disorders. These findings provide mechanistic insight into how the brain leverages multiple oscillatory frequencies for efficient information processing and suggest a potential biomarker for impaired neural communication.

A direct neural signature of serial dependence in working memory

2025-06-23

Cora Fischer , Jochen Kaiser , Christoph Bledowski | eLife

Serial dependence describes the phenomenon that current object representations are attracted to previously encoded and reported representations. While attractive biases have been observed reliably in behavior, a direct neural correlate … Serial dependence describes the phenomenon that current object representations are attracted to previously encoded and reported representations. While attractive biases have been observed reliably in behavior, a direct neural correlate has not been established. Previous studies have either shown a reactivation of past information without observing a neural signal related to the bias of the current information, or a repulsive distortion of current neural representations contrasting the behavioral bias. The present study recorded neural signals with magnetoencephalography (MEG) during a working memory task to identify neural correlates of serial dependence. Participants encoded and memorized two sequentially presented motion directions per trial, one of which was later retro-cued for report. Multivariate analyses provided reliable reconstructions of both motion directions. Importantly, the reconstructed directions in the current trial were attractively shifted toward the target direction of the previous trial. This neural bias mirrored the behavioral attractive bias, thus reflecting a direct neural signature of serial dependence. The use of a retro-cue task in combination with MEG allowed us to determine that this neural bias emerged at later, post-encoding time points. This timing suggests that serial dependence in working memory affects memorized information during read-out and reactivation processes that happen after the initial encoding.

Neural Correlates of Flight Acceleration in Pigeons: Gamma-Band Activity and Local Functional Network Dynamics in the AID Region

2025-06-23

Suchen Li , Zhuo Tang , Mengmeng Li +2 more | Animals

Flight behavior in pigeons is governed by intricate neural mechanisms that regulate movement patterns and flight dynamics. Among various kinematic parameters, flight acceleration provides critical information for the brain to … Flight behavior in pigeons is governed by intricate neural mechanisms that regulate movement patterns and flight dynamics. Among various kinematic parameters, flight acceleration provides critical information for the brain to modulate movement intensity, speed, and direction. However, the neural representation mechanisms underlying flight acceleration remain insufficiently understood. To address this, we conducted outdoor free-flight experiments in homing pigeons, during which GPS data, flight posture, and eight-channel local field potentials (LFPs) were synchronously recorded. Our analysis revealed that gamma-band activity in the dorsal intermediate arcopallium (AId) region was more prominent during behaviorally demanding phases of flight. In parallel, local functional network analysis showed that the clustering coefficient of gamma-band activity in the AId followed a nonlinear, U-shaped relationship with flight acceleration—exhibiting the strongest and most widespread connectivity during deceleration, moderate connectivity during acceleration, and the weakest network coupling during steady flight. This pattern likely reflects the increased neural demands associated with flight phase transitions, where greater cognitive and sensorimotor integration is required. Furthermore, using LFP signals from five distinct frequency bands as input, machine learning models were developed to decode flight acceleration, further confirming the role of gamma-band dynamics in motor regulation during natural flight. This study provides the first evidence that gamma-band activity in the avian AId region encodes flight acceleration, offering new insights into the neural representation of motor states in natural flight and implications for bio-inspired flight control systems.

Large-scale high-density brain-wide neural recording in nonhuman primates

2025-06-23

Eric M. Trautmann , Janis K. Hesse , Gabriel M Stine +7 more | Nature Neuroscience

Beta-band desynchronization in the human hippocampus during movement preparation in a delayed reach task

2025-06-23

Xiecheng Shao , Ryan S. Chung , Shivani Sundaram +7 more | Experimental Brain Research

Dynamic encoding of valence by the anterior cingulate cortex in mice

2025-06-23

Priyodarshan Goswamee , Nilanjana Saferin , Kamalika Ganguly +5 more

Fear is a double-edged sword: it supports survival based on learned associations between environmental cues and potential threats, but its dysregulation can lead to anxiety disorders and PTSD. Many studies … Fear is a double-edged sword: it supports survival based on learned associations between environmental cues and potential threats, but its dysregulation can lead to anxiety disorders and PTSD. Many studies have addressed the roles of the hippocampus and basolateral amygdala in the storage of the fear engram, but the role of the anterior cingulate cortex (ACC), especially during and immediately after fear acquisition, remains poorly defined. To address this gap, we longitudinally recorded ACC neuronal activity using single-photon calcium imaging in freely behaving adult male mice subjected to fear conditioning. Subjects acquired a conditioned freezing response to a tone cue (conditioned stimulus, CS) paired with light foot shocks (unconditioned stimulus, US), and ACC activity was monitored during cue pre-exposure, fear acquisition, fear recall, and fear extinction. Consistent with known functions of the ACC, neuronal responses were modulated by the US and by the novelty of the CS and US. Critically, both the number of CS-responsive neurons and the CS-associated population activity rose during acquisition, peaked during recall, and decreased throughout extinction. Neuronal populations responsive to the CS overlapped at a rate consistent with chance, suggesting that the ACC operates as a flexible integrative hub rather than containing stable engrams. Together, these findings indicate that ACC neuronal populations, but not engrams, represent novelty, pain, and the dynamic valence of a CS. Our findings are consistent with a model in which the ACC plays a role in threat appraisal and provides a learning signal that dynamically updates fear representations in other regions.

Dynamic cholinergic signaling differentially desynchronizes cortical microcircuits dependent on modulation rate and network topology

2025-06-22

S.Pasunkili Pandian , Scott Rich | bioRxiv (Cold Spring Harbor Laboratory)

Acetylcholine (ACh) affects both the intrinsic properties of individual neurons and the oscillatory tendencies of neuronal microcircuits by modulating the muscarinic-receptor gated m-current. However, despite contemporary experimental evidence of ACh … Acetylcholine (ACh) affects both the intrinsic properties of individual neurons and the oscillatory tendencies of neuronal microcircuits by modulating the muscarinic-receptor gated m-current. However, despite contemporary experimental evidence of ACh concentrations changing at millisecond timescales, computational studies traditionally model ACh solely as a tonic neuromodulator. How time-varying, dynamic cholinergic modulation of the m-current affects the dynamics of neuronal microcircuits therefore remains an open question. Using a new implementation of a time-varying cholinergic signal in computational excitatory-inhibitory (E-I) spiking neuronal networks, we here delineate how the interaction between dynamic cholinergic modulation and network topology influences the oscillatory tendencies of these systems. While the dynamics of networks with dominant inter-connectivity (strong E-to-I and I-to-E synaptic weights) are minimally affected, networks with dominant intra-connectivity (strong E-to-E and I-to-I synaptic weights) exhibit dynamics heavily dependent upon dynamic cholinergic signaling. Further investigation of these latter type of networks reveals that their firing patterns are sensitive to the timecourse of cholinergic modulation and that relatively minor changes to the E-I connectivity strength promote distinct desynchronization mechanisms. Our results indicate that network topology plays a paramount role in dictating the modulatory effects of time-varying cholinergic signals, a finding of broad relevance to our understanding of cholinergic modulation and potentially impactful in the design of neurostimulation therapies believed to act through cholinergic pathways.

The role of dopamine-sensitive motor cortical circuits in the development and execution of skilled forelimb movements

2025-06-21

Martyna Gorkowska-Nosal , Gniewosz Drwięga , Łukasz Szumiec +2 more | bioRxiv (Cold Spring Harbor Laboratory)

Dopamine signalling in the motor cortex is crucial for motor skill learning. Here we resolve the spatiotemporal dopamine dynamics and the activity of local dopaminoceptive circuits during the formation and … Dopamine signalling in the motor cortex is crucial for motor skill learning. Here we resolve the spatiotemporal dopamine dynamics and the activity of local dopaminoceptive circuits during the formation and execution of motor skills. We trained head-fixed mice to perform skilled forelimb movements with a joystick to collect water rewards, while simultaneously monitoring dopamine release and calcium dynamics in the forelimb area of motor cortex. We found that dopamine release events and calcium transients were temporally linked to joystick movements and reward consumption. Dopamine dynamics and population level activity of dopamine-receptive neurons scaled with the vigor of forelimb movements and tracked the relationship between actions and their consequences. Optogenetic photoinhibition of cortical dopaminoceptive circuits reduced the number of rewarded joystick movements. Our findings show how phasic dopamine signals in the motor cortex facilitate reinforcement motor learning of skilled behavior.

Pre-stimulus beta power mediates explicit and implicit perceptual biases in distinct cortical areas

2025-06-21

Carina Forster , Tilman Stephani , Martin Grund +5 more | Communications Psychology

Abstract Perception is biased by expectations and previous actions. Pre-stimulus brain oscillations are a potential candidate for implementing biases in the brain. In two EEG studies (43 and 39 participants) … Abstract Perception is biased by expectations and previous actions. Pre-stimulus brain oscillations are a potential candidate for implementing biases in the brain. In two EEG studies (43 and 39 participants) on somatosensory near-threshold detection, we investigated the pre-stimulus neural correlates of an (implicit) previous choice bias and an explicit bias. The explicit bias was introduced by informing participants about stimulus probability on a single-trial level (volatile context) or block-wise (stable context). Behavioural analysis confirmed adjustments in the decision criterion and confidence ratings according to the cued probabilities and previous choice-induced biases. Pre-stimulus beta power with distinct sources in sensory and higher-order cortical areas predicted explicit and implicit biases, respectively, on a single subject level and partially mediated the impact of previous choice and stimulus probability on the detection response. We suggest pre-stimulus beta oscillations in distinct brain areas as a neural correlate of explicit and implicit biases in somatosensory perception.

Modulation of neural complexity and consciousness in temporal lobe seizures: Effects of high‐frequency pulvinar stimulation

2025-06-21

Ionuț‐Flavius Bratu , Nada El Youssef , Stanislas Lagarde +1 more | Epileptic Disorders

Abstract Background Loss of consciousness/awareness during temporal lobe seizures significantly affects quality of life and is closely linked to pathological thalamocortical synchronization and loss of cortical signal complexity. The medial … Abstract Background Loss of consciousness/awareness during temporal lobe seizures significantly affects quality of life and is closely linked to pathological thalamocortical synchronization and loss of cortical signal complexity. The medial pulvinar nucleus (PUM) contributes to seizure propagation and awareness impairment, making it a potential target for neuromodulation. Acute high‐frequency PUM stimulation has previously been shown to reduce seizure severity and improve awareness, potentially by disrupting excessive synchrony. Aims In this study, we investigated the effects of PUM stimulation on signal complexity and its relationship to ictal awareness using permutation entropy (PE) in SEEG recordings. Materials and methods Eight patients with focal drug‐resistant temporal epilepsy underwent hippocampus‐induced seizures with and without additional high‐frequency PUM stimulation. SEEG complexity changes were quantified using PE and ictal awareness was assessed using the Consciousness Seizure Scale (CSS). Results Our results showed that PUM stimulation attenuated entropy reductions during seizures, suggesting a preservation of neural complexity. Moreover, reduced entropy alterations correlated with improved CSS scores. Discussion These findings support the role of PUM stimulation in mitigating pathological neural complexity alterations, with potential implications for preserving both consciousness and cognitive function in epilepsy. Conclusion Further studies are needed to confirm these findings in larger cohorts and to explore the long‐term effects of thalamic deep brain stimualtion in drug‐resistant epilepsy as well as the interaction between neural complexity, awareness, and cognition in this context.

Tracking visual rhythms: a concert of sensory and motor simulation

2025-06-21

Aaron Kaltenmaier , Quirin Gehmacher , Peter Kok +2 more | bioRxiv (Cold Spring Harbor Laboratory)

Neural oscillations have been proposed to model external temporal structure by phase-coupling to environmental rhythms, thereby supporting adaptive perception. However, there is little evidence supporting these theories, particularly in the … Neural oscillations have been proposed to model external temporal structure by phase-coupling to environmental rhythms, thereby supporting adaptive perception. However, there is little evidence supporting these theories, particularly in the visual domain, and the underlying mechanisms remain unclear. Using MEG and a new empirical approach we addressed this issue. Participants attended 1.3 and 2 Hz visual displays of rotating Gabors and judged either the timing or content of these events. We show behaviourally-relevant rate-specific phase-coupling in motor structures to - and beyond - the visual rhythm specifically when judging temporal features of the display. We subsequently devised a rate-specific decoding measure to show that visual structures track anticipated, temporally-precise content regardless of task. This sensory simulation predicted the temporal tracking in motor structures. We consequently propose a mechanism by which automatic, temporally-specific sensory simulation yields an information envelope read out by motor areas when estimating temporal characteristics in our environment.

Horizontally Tiled Network of Cortico-Basal Ganglia Modules Performs Reinforcement Learning

2025-06-21

Kojiro Hirokane , Ryo Takehara , Takashi Kita +3 more | bioRxiv (Cold Spring Harbor Laboratory)

The neocortex and basal ganglia nuclei are connected along regions that share the same topography and are arranged side-by-side. Inspired by the anatomical characteristics of the cerebrum, we developed a … The neocortex and basal ganglia nuclei are connected along regions that share the same topography and are arranged side-by-side. Inspired by the anatomical characteristics of the cerebrum, we developed a network in which the modules of the neocortex-basal ganglia unit were arranged in a horizontally tiled manner. By applying this network to reinforcement learning tasks, we demonstrated that reinforcement learning can be achieved through horizontal signals passing between modules. Each module not only performs its calculation but also provides signals to adjacent modules. This lateral transmission takes advantage of the differences in the projection ranges of the three basal ganglia pathways, the direct, indirect, and hyperdirect pathways, which have been examined in physiologic studies. We found that these differences enabled temporal-difference-error computations. This study proposes a novel strategy for information processing based on neocortical-basal ganglia circuits, highlighting the computational significance of their anatomically and physiologically clarified features.

Biomechanical simplification of the motor control of whisking

2025-06-21

Chris S. Bresee , Yifu Luo , Jasmine Alade'Fa +5 more | bioRxiv (Cold Spring Harbor Laboratory)

Animal nervous systems must coordinate the sequence and timing of numerous muscles - a challenging control problem. The challenge is particularly acute for highly mobile sensing structures with many degrees … Animal nervous systems must coordinate the sequence and timing of numerous muscles - a challenging control problem. The challenge is particularly acute for highly mobile sensing structures with many degrees of freedom, such as eyes, pinnae, hands, forepaws, and whiskers, because these low-mass, distal sensors require complex muscle coordination. This work examines how the geometry of the rat whisker array simplifies coordination required for "whisking" behavior [1-3]. During whisking, 33 intrinsic ("sling") muscles are the primary drivers [4-12] of the rapid, rhythmic protractions of the large mystacial vibrissae (whiskers), which vary more than sixfold in length and threefold in base diameter [13-16]. Although whisking is a rhythmic, centrally-patterned behavior [17-24], rodents can change the position, shape, and size of the whisker array, indicating considerable voluntary control [25-34]. To begin quantifying how the array's biomechanics contribute to whisking movements, we used three-dimensional anatomical reconstructions of follicle and sling muscle geometry to simulate the movement resulting from a "uniform motor command," defined as equal firing rates across all sling muscle motor neurons. This simulation provides a baseline profile of protraction under anatomically realistic but uniformly driven conditions. It does not isolate neural from biomechanical contributions but helps identify deviations that suggest active control. Simulations reveal that all follicles rotate through approximately equal angles, regardless of size. The angular fanning of the whiskers at their bases increases monotonically throughout protraction, while maximum distance between whisker tips occurs at approximately 90% of resting muscle length, after which whisker tips converge and sensing resolution increases monotonically.

Cortical sculpting of a rhythmic motor program

2025-06-21

Eric A. Kirk , Kunyan Cai , Britton Sauerbrei | bioRxiv (Cold Spring Harbor Laboratory)

Motor cortex is the principal driver of discrete, voluntary movements like reaching. Correspondingly, current theories describe muscle activity as a function of cortical dynamics. Tasks like speech and locomotion, however, … Motor cortex is the principal driver of discrete, voluntary movements like reaching. Correspondingly, current theories describe muscle activity as a function of cortical dynamics. Tasks like speech and locomotion, however, require the integration of voluntary commands with ongoing movements orchestrated by largely independent subcortical centers. In such cases, motor cortex must receive inputs representing the state of the environment and the state of subcortical networks, then transform these inputs into commands that modulate the rhythmic motor pattern. Here, we study this transformation in mice performing an obstacle traversal task, which combines a spinal locomotor pattern with voluntary cortical adjustments. Cortical dynamics contain a prominent representation of motor preparation that is linked to obstacle proximity and robust to removal of somatosensory or visual input, and also maintain a representation of the state of the spinal pattern generator. Readout signals resembling commands for obstacle traversal are consistent across trials, but small in amplitude. Using computational modeling, we identify a simple algorithm that generates the appropriate commands through phase-dependent gating. Together, these results reveal a regime in which motor cortex does not fully specify muscle activity, but must sculpt an ongoing, spinally-generated program to flexibly control behavior in a complex and changing environment.

Evidence from pupillometry, fMRI, and RNN modelling shows that gain neuromodulation mediates task-relevant perceptual switches

2025-06-20

Gabriel Wainstein , Christopher J. Whyte , Kaylena A. Ehgoetz Martens +7 more | eLife

Perceptual updating has been hypothesised to rely on a network reset modulated by bursts of ascending neuromodulatory neurotransmitters, such as noradrenaline, abruptly altering the brain’s susceptibility to changing sensory activity. … Perceptual updating has been hypothesised to rely on a network reset modulated by bursts of ascending neuromodulatory neurotransmitters, such as noradrenaline, abruptly altering the brain’s susceptibility to changing sensory activity. To test this hypothesis at a large-scale, we analysed an ambiguous figures task using pupillometry and functional magnetic resonance imaging (fMRI). Behaviourally, qualitative shifts in the perceptual interpretation of an ambiguous image were associated with peaks in pupil diameter, an indirect readout of phasic bursts in neuromodulatory tone. We further hypothesised that stimulus ambiguity drives neuromodulatory tone, leading to heightened neural gain, hastening perceptual switches. To explore this hypothesis computationally, we trained a recurrent neural network (RNN) on an analogous perceptual categorisation task, allowing gain to change dynamically with classification uncertainty. As predicted, higher gain accelerated perceptual switching by transiently destabilising the network’s dynamical regime in periods of maximal uncertainty. We leveraged a low-dimensional readout of the RNN dynamics to develop two novel macroscale predictions: perceptual switches should occur with peaks in low-dimensional brain state velocity and with a flattened egocentric energy landscape. Using fMRI, we confirmed these predictions, highlighting the role of the neuromodulatory system in the large-scale network reconfigurations mediating adaptive perceptual updates.

Computations that sustain neural feature selectivity across processing stages

2025-06-20

Ryan J. Rowekamp , Tatyana O. Sharpee | PLoS Computational Biology

Biological visual systems are celebrated for their ability to reliably and precisely recognize objects. However, the specific neural mechanisms responsible for this capability remain largely elusive. In this study, we … Biological visual systems are celebrated for their ability to reliably and precisely recognize objects. However, the specific neural mechanisms responsible for this capability remain largely elusive. In this study, we investigate neural responses in the visual areas V1, V2, and V4 of the brain to natural stimuli using a framework that includes quadratic computations in order to capture local recurrent interactions, both excitatory and suppressive. We find that these quadratic computations and specific coordination between their elements strongly increase both the predictive power of the model and the neural selectivity to natural stimuli. Particularly important were (i) coordination between excitatory and suppressive features to represent mutually exclusive hypotheses regarding incoming stimuli, such as orthogonal orientations or opposing motion directions in area V4, (ii) balance in the contribution of excitatory and suppressive components and its maintenance at similar levels across stages of processing, and (iii) refinement of feature selectivity between stages, with earlier stages representing broader category of inputs. Overall, this work describes how the brain could use multiple nonlinear mechanisms to increase selectivity of neural responses to natural stimuli.

Interactions between homeostatic plasticity and statistical learning: A role for inhibition

2025-06-20

Elisa Galliano , Tara Keck | Current Opinion in Neurobiology

Statistical learning, sensory-driven unsupervised learning of repeating patterns, must coexist with ongoing homeostatic plasticity that is responsible for the necessary balance of activity in the brain; however, the mechanisms that … Statistical learning, sensory-driven unsupervised learning of repeating patterns, must coexist with ongoing homeostatic plasticity that is responsible for the necessary balance of activity in the brain; however, the mechanisms that facilitate these interactions are not clear. While models of both statistical learning, a form of associative plasticity, and homeostatic plasticity have primarily focused on excitatory cells and their synaptic changes, inhibition may play a key role in facilitating the balance between homeostatic plasticity and statistical learning. Here, we review the inhibitory synaptic, cellular, and network mechanisms underlying homeostatic and associative plasticity in rodents and propose a model in which localized inhibition, provided by diverse interneuron types, supports both statistical learning and homeostatic plasticity, as well as the interactions between them.

Modeling the Role of the Alpha Rhythm in Attentional Processing during Distractor Suppression

2025-06-20

Mauro Ursino | Journal of Cognitive Neuroscience

Abstract Recent experimental results suggest that alpha oscillations in brain neuroelectrical activity do not merely represent an idling phenomenon but actively participate in attention to suppress distractors and reduce cognitive … Abstract Recent experimental results suggest that alpha oscillations in brain neuroelectrical activity do not merely represent an idling phenomenon but actively participate in attention to suppress distractors and reduce cognitive workload. However, the exact mechanism responsible for this attentional processing is still a matter of research. In this work, we propose a simple mechanism for distractor suppression using a neural mass model of oscillating, interconnected cortical regions, based on alpha oscillations and their interaction with the gamma rhythm. Essentially, the model distinguishes between certain “sensory” areas, where stimuli are coded and represented via gamma oscillations, a downstream “detection” area dedicated to processing these stimuli, and a “control” region that generates the alpha rhythm. Unattended stimuli in a sensory area can be suppressed by simply imposing an alpha rhythm that is out of phase compared with the detection layer. A sensitivity analysis performed on a simple paradigmatic model emphasizes the robustness of the proposed mechanism versus parameter changes. Moreover, a more complex example (concerning spatial attention, where objects are represented through a Gestalt proximity rule) supports the capacity of the mechanism to suppress distractors in multi-unit networks. The model aligns with several experimental results and can be further utilized to investigate cognitive alterations in pathological conditions, such as schizophrenia, characterized by dysfunction in the gamma rhythm.

Microfluidic chip platform provides non-invasive method for zebrafish behavioral studies

2025-06-20

Ben Ikenson | Scilight

Method may form foundation for future research on cognitive dynamics. Method may form foundation for future research on cognitive dynamics.

Imaging-based whole brain-level neural circuit visualization

2025-06-20

Chentao Jin , Xiaoyun Luo , Yan Zhong +5 more | European Journal of Nuclear Medicine and Molecular Imaging

Periodic and aperiodic neural activity contribute to the microgenesis of learning from mistakes

2025-06-19

Shiwei Jia , D G Liu , Yangming Yue +3 more | NeuroImage

The ability to adapt behavior to situational changes is a central aspect of human cognitive control or executive functioning. Through trial and error, individuals can then infer the correct rules … The ability to adapt behavior to situational changes is a central aspect of human cognitive control or executive functioning. Through trial and error, individuals can then infer the correct rules and adapt their behavior or response strategy accordingly. Prior research has mostly linked feedback-related negativity (FRN) and theta band activity to feedback-related behavioral adaptation. Based on neurophysiological and cognitive science considerations and using an extended sample of N = 226 healthy individuals, we asked whether other neural activities (i.e., aperiodic activity and alpha band activity) are important elements enabling adaptive behavior. In an EEG-based Wisconsin Card Sorting Task, we examined the chain of processes triggered by the feedback presentation up to the next trial to see how people adjusted. Our findings highlight the distinctive role of alpha band activity, particularly after adjusting for aperiodic activity. Alpha activity, modulated throughout feedback stages, increased following negative feedback and strongly predicted improved performance in subsequent trials. This predictive relationship emerged after controlling for aperiodic activity, revealing alpha's role in inhibitory gating and categorization processes critical for adaptive behavior, especially under conditions requiring suppression of task-irrelevant information. Although not directly predictive of performance, aperiodic activity influenced alpha processes during feedback, suggesting a metacontrol mechanism that supports feedback-related behavioral adaptation. These findings integrate alpha and aperiodic activity with established FRN and theta band processes, offering novel insights into the neural basis of behavioral adaptation.