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A neural computational framework for face processing in the human temporal lobe

Current Biology 2025年第8期35卷 1765-1778.e6页

作者： Cao, Runnan Zhang, Jie Zheng, Jie Wang, Yue Brunner, Peter Willie, Jon T. Wang, Shuo Department of Radiology Washington University in St. Louis St. Louis 63110 MO United States Department of Biomedical Engineering University of California Davis Davis 95618 CA United States Department of Neurosurgery Washington University in St. Louis St. Louis 63110 MO United States

A key question in cognitive neuroscience is how unified identity representations emerge from visual inputs. Here, we recorded intracranial electroencephalography (iEEG) from the human ventral temporal cortex (VTC) and medial temporal lobe (MTL), as well as single-neuron activity in the MTL, to demonstrate how dense feature-based representations in the VTC are translated into sparse identity-based representations in the MTL. First, we characterized the spatiotemporal neural dynamics of face coding in the VTC and MTL. The VTC, particularly the fusiform gyrus, exhibits robust axis-based feature coding. Remarkably, MTL neurons encode a receptive field within the VTC neural feature space, constructed using VTC neural axes, thereby bridging dense feature and sparse identity representations. We further validated our findings using recordings from a macaque. Lastly, inter-areal interactions between the VTC and MTL provide the physiological basis of this computational framework. Together, we reveal the neurophysiological underpinnings of a computational framework that explains how perceptual information is translated into face identities. © 2025 The Author(s)

关键词： amygdala face fusiform gyrus hippocampus human single-neuron recordings iEEG inferior temporal gyrus inter-areal interaction medial temporal lobe neural coding

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Toward in vivo nanoscale communication networks: utilizing an active network architecture

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中国计算机科学前沿 2011年第3期5卷 316-326页

作者： Stephen F. BUSH GE Global Research One Research Circle Niskayuna NY 12309 USA

A safe and reliable in vivo nanoscale communication network will be of great benefit for medical diagnosis and monitoring as well as medical implant *** review article provides a brief introduction to nanoscale and molecular networking in general and provides opinions on the role of active networking for in vivo nanoscale information *** there are many in vivo communication mechanisms that can be leveraged,for example,forms of cell signaling,gap junctions,calcium and ion signaling,and circulatory borne communication,this review examines two in particular:molecular motor transport and neuronal information *** motors transport molecules representing information and neural coding operates by means of the action potential; these mechanisms are reviewed within the theoretical framework of an active *** review suggests that an active networking paradigm is necessary at the nanoscale along with a new communication constraint,namely,minimizing the communication impact upon the living *** goal is to assemble efficient nanoscale and molecular communication channels while minimizing disruption to the host organism.

关键词： nanoscale network molecular communication in vivo network active network molecular motor neural coding

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On the variability of cortical neural responses: a statistical interpretation

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NEUROCOMPUTING 2005年第SPEC. ISS.期65卷 409-414页

作者： Li, S Wu, S Univ Sussex Dept Informat Brighton BN1 9QH E Sussex England

neural responses in the cerebral cortex exhibit tremendous variability. Understanding the origin and the functional meaning of this variability is of critical importance for our understanding of neural coding. The present study investigates the neural response variability from the view of statistical inference. We will show that high variability can also arise due to the inferential sensitivity in neural coding. This view is supported by the simulation on the representation of nature images. (c) 2004 Elsevier B.V. All rights reserved.

关键词： neural coding neural response variability statistical inference

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Change-point detection in neuronal spike train activity

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NEUROCOMPUTING 2003年 52-4卷 849-855页

作者： Ratnam, R Goense, JBM Nelson, ME Univ Illinois Beckman Inst Adv Sci & Technol Urbana IL 61801 USA Univ Illinois Ctr Biophys & Computat Biol Urbana IL 61801 USA Univ Illinois Dept Mol & Integrat Physiol Urbana IL 61801 USA

Animals respond to changes in their environment based on the information encoded in neuronal spike activity. One key issue is to determine how quickly and reliably the system can detect that a behaviorally relevant change has taken place. What are the neural mechanisms and computational principles that allow fast, reliable detection of changes in spike activity? Here we present an optimal statistical signal-processing algorithm for change-point detection, known as the cumulative sum (CUSUM) algorithm. We then show that the performance of a simple neuron model with leaky-integrate-and-fire dynamics can approach theoretically optimal performance limits under certain conditions. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： spike train analysis signal detection change-point detection CUSUM neural coding

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A path integral approach to the Hodgkin-Huxley model

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2017年 486卷 986-999页

作者： Baravalle, Roman Rosso, Osvaldo A. Montani, Fernando Consejo Nacl Invest Cient & Tecn IFLYSIB Calle 59-789 RA-1900 La Plata Buenos Aires Argentina Univ Nacl La Plata Calle 59-789 RA-1900 La Plata Buenos Aires Argentina Univ Nacl La Plata Fac Ciencias Exactas Dept Fis Calle 49 & 115 CC 67 RA-1900 La Plata Buenos Aires Argentina Hosp Italiano Buenos Aires Dept Informat Salud C1199ABB Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn C1199ABB Buenos Aires DF Argentina Univ Fed Alagoas UFAL Inst Fis BR-57072900 Maceio Brazil Univ Los Andes Complex Syst Grp Fac Ingn & Ciencias Aplicadas Santiago 12455 Chile

To understand how single neurons process sensory information, it is necessary to develop suitable stochastic models to describe the response variability of the recorded spike trains. Spikes in a given neuron are produced by the synergistic action of sodium and potassium of the voltage-dependent channels that open or close the gates. Hodgkin and Huxley (HH) equations describe the ionic mechanisms underlying the initiation and propagation of action potentials, through a set of nonlinear ordinary differential equations that approximate the electrical characteristics of the excitable cell. Path integral provides an adequate approach to compute quantities such as transition probabilities, and any stochastic system can be expressed in terms of this methodology. We use the technique of path integrals to determine the analytical solution driven by a non-Gaussian colored noise when considering the HH equations as a stochastic system. The different neuronal dynamics are investigated by estimating the path integral solutions driven by a non-Gaussian colored noise q. More specifically we take into account the correlational structures of the complex neuronal signals not just by estimating the transition probability associated to the Gaussian approach of the stochastic HH equations, but instead considering much more subtle processes accounting for the non-Gaussian noise that could be induced by the surrounding neural network and by feedforward correlations. This allows us to investigate the underlying dynamics of the neural system when different scenarios of noise correlations are considered. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Neuronal model Path integrals Stochastic processes Spiking output neural coding

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Computing information in neuronal spikes

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neural PROCESSING LETTERS 2006年第2期23卷 183-199页

作者： Aur, D Connolly, CI Jog, MS Movement Disorders Program Dept Clin Neurol Sci London ON Canada SRI Int Menlo Pk CA 94025 USA

This paper provides new insights regarding the transfer of information between input signal and the output of neurons. Simulations of the Hodgkin-Huxley (HH) model combined with computational techniques are used to estimate this transfer of information. Our analysis shows that comparatively, mutual information (MI) between input signal and sodium flux is about two times that between input signal and output spikes during each spike within a millisecond-level time domain. This higher transfer of information provided by ionic fluxes extends the working frequency domain of neural cells beyond those accessible to information transfer within spikes alone.

关键词： entropy information theory informational resonance mutual information neural coding

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Integrated Intelligence from Distributed Brain Activity

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TRENDS IN COGNITIVE SCIENCES 2020年第10期24卷 838-852页

作者： Duncan, John Assem, Moataz Shashidhara, Sneha Univ Cambridge MRC Cognit & Brain Sci Unit Cambridge CB2 7EF England Univ Oxford Dept Expt Psychol Oxford OX2 6GG England

How does organized cognition arise from distributed brain activity? Recent analyses of fluid intelligence suggest a core process of cognitive focus and integration, organizing the components of a cognitive operation into the required computational structure. A cortical 'multiple-demand' (MD) system is closely linked to fluid intelligence, and recent imaging data define nine specific MD patches distributed across frontal, parietal, and occipitotemporal cortex. Wide cortical distribution, relative functional specialization, and strong connectivity suggest a basis for cognitive integration, matching electrophysiological evidence for binding of cognitive operations to their contents. Though still only in broad outline, these data suggest how distributed brain activity can build complex, organized cognition.

关键词： intelligence attention cognitive control brain networks neural coding

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Effects of stimulus transformations on estimates of sensory neuron selectivity

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JOURNAL OF COMPUTATIONAL NEUROSCIENCE 2006年第3期20卷 265-283页

作者： Dimitrov, Alexander G. Gedeon, Tomas Montana State Univ Ctr Computat Biol Bozeman MT 59717 USA Montana State Univ Dept Math Sci Bozeman MT 59717 USA

Stimulus selectivity of sensory systems is often characterized by analyzing response-conditioned stimulus ensembles. However, in many cases these response-triggered stimulus sets have structure that is more complex than assumed. If not taken into account, when present it will bias the estimates of many simple statistics, and distort the estimated stimulus selectivity of a neural sensory system. We present an approach that mitigates these problems by modeling some of the response-conditioned stimulus structure as being generated by a set of transformations acting on a simple stimulus distribution. This approach corrects the estimates of key statistics and counters biases introduced by the transformations. In cases involving temporal spike jitter or spatial jitter of images, the main observed effects of transformations are blurring of the conditional mean and introduction of artefacts in the spectral decomposition of the conditional covariance matrix. We illustrate this approach by analyzing and correcting a set of model stimuli perturbed by temporal and spatial jitter. We apply the approach to neurophysiological data from the cricket cercal sensory system to correct the effects of temporal jitter.

关键词： stimulus transformation sensory systems neural coding dejitter

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An introductory review of information theory in the context of computational neuroscience

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BIOLOGICAL CYBERNETICS 2011年第1期105卷 55-70页

作者： McDonnell, Mark D. Ikeda, Shiro Manton, Jonathan H. Inst Stat Math Tokyo 1908562 Japan Univ S Australia Inst Telecommun Res Mawson Lakes SA 5095 Australia Univ Melbourne Melbourne Vic 3010 Australia

This article introduces several fundamental concepts in information theory from the perspective of their origins in engineering. Understanding such concepts is important in neuroscience for two reasons. Simply applying formulae from information theory without understanding the assumptions behind their definitions can lead to erroneous results and conclusions. Furthermore, this century will see a convergence of information theory and neuroscience;information theory will expand its foundations to incorporate more comprehensively biological processes thereby helping reveal how neuronal networks achieve their remarkable information processing abilities.

关键词： Information theory Neuroscience neural coding Channel capacity

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Examining methods for estimating mutual information in spiking neural systems

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NEUROCOMPUTING 2005年第SPEC. ISS.期65卷 429-434页

作者： Rozell, CJ Johnson, DH Rice Univ Dept Elect & Comp Engn Houston TX 77251 USA

Mutual information enjoys wide use in the computational neuroscience community for analyzing spiking neural systems. Its direct calculation is difficult because estimating the joint stimulus-response distribution requires a prohibitive amount of data. Consequently, several techniques have appeared for bounding mutual information that rely on less data. We examine two upper bound techniques and find that they are unreliable and can introduce strong assumptions about the neural code. We also examine two lower bounds, showing that they can be very loose and possibly bear little relation to the mutual information's actual value. (c) 2004 Elsevier B.V. All rights reserved.

关键词： mutual information neural coding information theory

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