Principles of neural coding by Rodrigo Quian Quiroga

By Rodrigo Quian Quiroga

Part I tools Physiological Foundations of Neural indications Kevin Whittingstall and Nikos okay. Logothetis Biophysics of Extracellular Spikes Costas A. Anastassiou, Gyorgy Buzsaki, and Christof Koch neighborhood box Potentials: Biophysical foundation and research Gaute T. Einevoll, Henrik Linden, Tom Tetzlaff, Szymon Leski, and Klas H. Pettersen Spike Sorting Juan Martinez and Rodrigo Quian Quiroga Spike-Train research Ines Samengo, Daniel Elijah, and Marcelo A. Montemurro Synchronization Measures Thomas Kreuz function of Correlations in inhabitants Coding Peter E. Latham and Yasser Roudi deciphering and data thought in Neuroscience Rodrigo Quian Quiroga and Stefano Panzeri part II Experimental effects Neural Coding of visible items Charles E. Connor Coding within the Auditory process Jan Schnupp Coding within the Whisker Sensory procedure Mathew E. Diamond and Ehsan Arabzadeh Neural Coding within the Olfactory method Ron A. Jortner Coding throughout Sensory Modalities: Integrating the Dynamic Face with the Voice Chandramouli Chandrasekaran and Asif A. Ghazanfar inhabitants Coding by means of position Cells and Grid Cells Jill ok. Leutgeb, Emily A. Mankin, and Stefan Leutgeb Coding of circulate Intentions Hansjorg Scherberger, Rodrigo Quian Quiroga, and Richard A. Andersen Neural Coding of non permanent reminiscence Stefanie Liebe and Gregor Rainer position of Temporal Spike styles in Neural Codes Rasmus S. Petersen variation and Sensory Coding Miguel Maravall Sparse and specific Neural Coding Peter Foldiak info Coding by means of Cortical Populations Kenneth D. Harris details content material of neighborhood box Potentials: Experiments and versions Alberto Mazzoni, Nikos ok. Logothetis, and Stefano Panzeri rules of Neural Coding from EEG signs Fernando H. Lopes da Silva Gamma-Band Synchronization and knowledge Transmission Martin Vinck, Thilo Womelsdorf, and Pascal Fries interpreting details from fMRI indications Jakob Heinzle and John-Dylan Haynes part III Theoretical and In Silico methods Dynamics of Neural Networks Nicolas Brunel studying and Coding in Neural Networks Timothee Masquelier and Gustavo Deco Ising types for Inferring community constitution from Spike info John A. Hertz, Yasser Roudi, and Joanna Tyrcha Vocal studying with Inverse types Richard H. R. Hahnloser and Surya Ganguli Computational types of visible item attractiveness Gabriel Kreiman Coding in Neuromorphic VLSI Networks Giacomo Indiveri Open-Source software program for learning Neural Codes Robin A. A. Ince

Table of Contents

Section I Methods

Physiological Foundations of Neural Signals

Kevin Whittingstall and Nikos okay. Logothetis

Biophysics of Extracellular Spikes

Costas A. Anastassiou, György Buzsáki, and Christof Koch

Local box Potentials: Biophysical beginning and Analysis

Gaute T. Einevoll, Henrik Lindén, Tom Tetzlaff, Szymon Łęski,

and Klas H. Pettersen

Spike Sorting

Juan Martínez and Rodrigo Quian Quiroga

Spike-Train Analysis

Inés Samengo, Daniel Elijah, and Marcelo A. Montemurro

Synchronization Measures

Thomas Kreuz

Role of Correlations in inhabitants Coding

Peter E. Latham and Yasser Roudi

Decoding and knowledge thought in Neuroscience

Rodrigo Quian Quiroga and Stefano Panzeri

Section II Experimental Results

Neural Coding of visible Objects

Charles E. Connor

Coding within the Auditory System

Jan Schnupp

Coding within the Whisker Sensory System

Mathew E. Diamond and Ehsan Arabzadeh

Neural Coding within the Olfactory System

Ron A. Jortner

Coding throughout Sensory Modalities: Integrating the Dynamic Face with the Voice

Chandramouli Chandrasekaran and Asif A. Ghazanfar

Population Coding through position Cells and Grid Cells

Jill okay. Leutgeb, Emily A. Mankin, and Stefan Leutgeb

Coding of move Intentions

Hansjörg Scherberger, Rodrigo Quian Quiroga, and Richard A. Andersen

Neural Coding of temporary Memory

Stefanie Liebe and Gregor Rainer

Role of Temporal Spike styles in Neural Codes

Rasmus S. Petersen

Adaptation and Sensory Coding

Miguel Maravall

Sparse and particular Neural Coding

Peter Földiák

Information Coding via Cortical Populations

Kenneth D. Harris

Information content material of neighborhood box Potentials: Experiments and Models

Alberto Mazzoni, Nikos okay. Logothetis, and Stefano Panzeri

Principles of Neural Coding from EEG Signals

Fernando H. Lopes da Silva

Gamma-Band Synchronization and data Transmission

Martin Vinck, Thilo Womelsdorf, and Pascal Fries

Decoding details from fMRI Signals

Jakob Heinzle and John-Dylan Haynes

Section III Theoretical and In Silico Approaches

Dynamics of Neural Networks

Nicolas Brunel

Learning and Coding in Neural Networks

Timothée Masquelier and Gustavo Deco

Ising types for Inferring community constitution from Spike Data

John A. Hertz, Yasser Roudi, and Joanna Tyrcha

Vocal studying with Inverse Models

Richard H. R. Hahnloser and Surya Ganguli

Computational versions of visible item Recognition

Gabriel Kreiman

Coding in Neuromorphic VLSI Networks

Giacomo Indiveri

Open-Source software program for learning Neural Codes

Robin A. A. Ince

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Because extracellular recording electrodes are typically many micrometers away from neurons, the LSA has also been used to calculate extracellular voltages arising from transmembrane currents. Nevertheless, it needs to be pointed out that the point-source approximation and the LSA become identical for fine enough discretization of the cable equation. While the LSA is more adequate for cable-like structures as dendrites or axons it fails to account for point-like processes such as synaptic currents.

Huang CM, Buchwald JS. 1977. Interpretation of the vertex short-latency acoustic response: A study of single neurons in the brain stem. Brain Res 137:291–303. Hubel DH, Wiesel TN. 1962. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol (Lond) 160:106–154. Humphrey DR, Corrie WS. 1978. Properties of pyramidal tract neuron system within a functionally defined subregion of primate motor cortex. J Neurophysiol 41:216–243. Jefferys JGR, Traub RD, Whittington MA.

7) with Vrev given by the Nernst’s equation for each species. 7 is a phenomenological model that captures neither the underlying microscopic processes involved nor the inherently stochastic nature of these processes. The conductance G(V(t)) is described through a maximum conductance G multiplied by the fraction of open channels. In particular, the 20 Principles of Neural Coding so-called “gating” particles are introduced to emulate the dynamics of each conductance. These gating particles can be either open or closed depending on time and V(t) and contain the kinetic characteristics of a specific conductance.

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