Transmission of population-coded information.

Abstract:

:As neural activity is transmitted through the nervous system, neuronal noise degrades the encoded information and limits performance. It is therefore important to know how information loss can be prevented. We study this question in the context of neural population codes. Using Fisher information, we show how information loss in a layered network depends on the connectivity between the layers. We introduce an algorithm, reminiscent of the water filling algorithm for Shannon information that minimizes the loss. The optimal connection profile has a center-surround structure with a spatial extent closely matching the neurons' tuning curves. In addition, we show how the optimal connectivity depends on the correlation structure of the trial-to-trial variability in the neuronal responses. Our results explain how optimal communication of population codes requires the center-surround architectures found in the nervous system and provide explicit predictions on the connectivity parameters.

journal_name

Neural Comput

journal_title

Neural computation

authors

Renart A,van Rossum MC

doi

10.1162/NECO_a_00227

subject

Has Abstract

pub_date

2012-02-01 00:00:00

pages

391-407

issue

2

eissn

0899-7667

issn

1530-888X

journal_volume

24

pub_type

杂志文章
  • McCulloch-Pitts Brains and Pseudorandom Functions.

    abstract::In a pioneering classic, Warren McCulloch and Walter Pitts proposed a model of the central nervous system. Motivated by EEG recordings of normal brain activity, Chvátal and Goldsmith asked whether these dynamical systems can be engineered to produce trajectories that are irregular, disorderly, and apparently unpredict...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/NECO_a_00841

    authors: Chvátal V,Goldsmith M,Yang N

    更新日期:2016-06-01 00:00:00

  • Learning Hough transform: a neural network model.

    abstract::A single-layered Hough transform network is proposed that accepts image coordinates of each object pixel as input and produces a set of outputs that indicate the belongingness of the pixel to a particular structure (e.g., a straight line). The network is able to learn adaptively the parametric forms of the linear segm...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/089976601300014501

    authors: Basak J

    更新日期:2001-03-01 00:00:00

  • Optimal approximation of signal priors.

    abstract::In signal restoration by Bayesian inference, one typically uses a parametric model of the prior distribution of the signal. Here, we consider how the parameters of a prior model should be estimated from observations of uncorrupted signals. A lot of recent work has implicitly assumed that maximum likelihood estimation ...

    journal_title:Neural computation

    pub_type: 信件

    doi:10.1162/neco.2008.10-06-384

    authors: Hyvärinen A

    更新日期:2008-12-01 00:00:00

  • Variations on the Theme of Synaptic Filtering: A Comparison of Integrate-and-Express Models of Synaptic Plasticity for Memory Lifetimes.

    abstract::Integrate-and-express models of synaptic plasticity propose that synapses integrate plasticity induction signals before expressing synaptic plasticity. By discerning trends in their induction signals, synapses can control destabilizing fluctuations in synaptic strength. In a feedforward perceptron framework with binar...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/NECO_a_00889

    authors: Elliott T

    更新日期:2016-11-01 00:00:00

  • Populations of tightly coupled neurons: the RGC/LGN system.

    abstract::A mathematical model, of general character for the dynamic description of coupled neural oscillators is presented. The population approach that is employed applies equally to coupled cells as to populations of such coupled cells. The formulation includes stochasticity and preserves details of precisely firing neurons....

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/neco.2007.03-07-482

    authors: Sirovich L

    更新日期:2008-05-01 00:00:00

  • A bio-inspired, computational model suggests velocity gradients of optic flow locally encode ordinal depth at surface borders and globally they encode self-motion.

    abstract::Visual navigation requires the estimation of self-motion as well as the segmentation of objects from the background. We suggest a definition of local velocity gradients to compute types of self-motion, segment objects, and compute local properties of optical flow fields, such as divergence, curl, and shear. Such veloc...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/NECO_a_00479

    authors: Raudies F,Ringbauer S,Neumann H

    更新日期:2013-09-01 00:00:00

  • Determining Burst Firing Time Distributions from Multiple Spike Trains.

    abstract::Recent experimental findings have shown the presence of robust and cell-type-specific intraburst firing patterns in bursting neurons. We address the problem of characterizing these patterns under the assumption that the bursts exhibit well-defined firing time distributions. We propose a method for estimating these dis...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/neco.2008.07-07-571

    authors: Lago-Fernández LF,Szücs A,Varona P

    更新日期:2009-04-01 00:00:00

  • Invariant global motion recognition in the dorsal visual system: a unifying theory.

    abstract::The motion of an object (such as a wheel rotating) is seen as consistent independent of its position and size on the retina. Neurons in higher cortical visual areas respond to these global motion stimuli invariantly, but neurons in early cortical areas with small receptive fields cannot represent this motion, not only...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/neco.2007.19.1.139

    authors: Rolls ET,Stringer SM

    更新日期:2007-01-01 00:00:00

  • Analytical integrate-and-fire neuron models with conductance-based dynamics for event-driven simulation strategies.

    abstract::Event-driven simulation strategies were proposed recently to simulate integrate-and-fire (IF) type neuronal models. These strategies can lead to computationally efficient algorithms for simulating large-scale networks of neurons; most important, such approaches are more precise than traditional clock-driven numerical ...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/neco.2006.18.9.2146

    authors: Rudolph M,Destexhe A

    更新日期:2006-09-01 00:00:00

  • Neuronal assembly dynamics in supervised and unsupervised learning scenarios.

    abstract::The dynamic formation of groups of neurons--neuronal assemblies--is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis suggests that synchronized oscillations underpin their formation and functioning, with a focus...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/NECO_a_00502

    authors: Moioli RC,Husbands P

    更新日期:2013-11-01 00:00:00

  • Learning Precise Spike Train-to-Spike Train Transformations in Multilayer Feedforward Neuronal Networks.

    abstract::We derive a synaptic weight update rule for learning temporally precise spike train-to-spike train transformations in multilayer feedforward networks of spiking neurons. The framework, aimed at seamlessly generalizing error backpropagation to the deterministic spiking neuron setting, is based strictly on spike timing ...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/NECO_a_00829

    authors: Banerjee A

    更新日期:2016-05-01 00:00:00

  • Why Does Large Batch Training Result in Poor Generalization? A Comprehensive Explanation and a Better Strategy from the Viewpoint of Stochastic Optimization.

    abstract::We present a comprehensive framework of search methods, such as simulated annealing and batch training, for solving nonconvex optimization problems. These methods search a wider range by gradually decreasing the randomness added to the standard gradient descent method. The formulation that we define on the basis of th...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/neco_a_01089

    authors: Takase T,Oyama S,Kurihara M

    更新日期:2018-07-01 00:00:00

  • A theory of slow feature analysis for transformation-based input signals with an application to complex cells.

    abstract::We develop a group-theoretical analysis of slow feature analysis for the case where the input data are generated by applying a set of continuous transformations to static templates. As an application of the theory, we analytically derive nonlinear visual receptive fields and show that their optimal stimuli, as well as...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/NECO_a_00072

    authors: Sprekeler H,Wiskott L

    更新日期:2011-02-01 00:00:00

  • Toward a biophysically plausible bidirectional Hebbian rule.

    abstract::Although the commonly used quadratic Hebbian-anti-Hebbian rules lead to successful models of plasticity and learning, they are inconsistent with neurophysiology. Other rules, more physiologically plausible, fail to specify the biological mechanism of bidirectionality and the biological mechanism that prevents synapses...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/089976698300017629

    authors: Grzywacz NM,Burgi PY

    更新日期:1998-04-01 00:00:00

  • Modeling short-term synaptic depression in silicon.

    abstract::We describe a model of short-term synaptic depression that is derived from a circuit implementation. The dynamics of this circuit model is similar to the dynamics of some theoretical models of short-term depression except that the recovery dynamics of the variable describing the depression is nonlinear and it also dep...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/089976603762552942

    authors: Boegerhausen M,Suter P,Liu SC

    更新日期:2003-02-01 00:00:00

  • On the emergence of rules in neural networks.

    abstract::A simple associationist neural network learns to factor abstract rules (i.e., grammars) from sequences of arbitrary input symbols by inventing abstract representations that accommodate unseen symbol sets as well as unseen but similar grammars. The neural network is shown to have the ability to transfer grammatical kno...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/089976602320264079

    authors: Hanson SJ,Negishi M

    更新日期:2002-09-01 00:00:00

  • A finite-sample, distribution-free, probabilistic lower bound on mutual information.

    abstract::For any memoryless communication channel with a binary-valued input and a one-dimensional real-valued output, we introduce a probabilistic lower bound on the mutual information given empirical observations on the channel. The bound is built on the Dvoretzky-Kiefer-Wolfowitz inequality and is distribution free. A quadr...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/NECO_a_00144

    authors: VanderKraats ND,Banerjee A

    更新日期:2011-07-01 00:00:00

  • A neurocomputational approach to prepositional phrase attachment ambiguity resolution.

    abstract::A neurocomputational model based on emergent massively overlapping neural cell assemblies (CAs) for resolving prepositional phrase (PP) attachment ambiguity is described. PP attachment ambiguity is a well-studied task in natural language processing and is a case where semantics is used to determine the syntactic struc...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/NECO_a_00290

    authors: Nadh K,Huyck C

    更新日期:2012-07-01 00:00:00

  • Linking Neuromodulated Spike-Timing Dependent Plasticity with the Free-Energy Principle.

    abstract::The free-energy principle is a candidate unified theory for learning and memory in the brain that predicts that neurons, synapses, and neuromodulators work in a manner that minimizes free energy. However, electrophysiological data elucidating the neural and synaptic bases for this theory are lacking. Here, we propose ...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/NECO_a_00862

    authors: Isomura T,Sakai K,Kotani K,Jimbo Y

    更新日期:2016-09-01 00:00:00

  • Downstream Effect of Ramping Neuronal Activity through Synapses with Short-Term Plasticity.

    abstract::Ramping neuronal activity refers to spiking activity with a rate that increases quasi-linearly over time. It has been observed in multiple cortical areas and is correlated with evidence accumulation processes or timing. In this work, we investigated the downstream effect of ramping neuronal activity through synapses t...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/NECO_a_00818

    authors: Wei W,Wang XJ

    更新日期:2016-04-01 00:00:00

  • Estimation and marginalization using the Kikuchi approximation methods.

    abstract::In this letter, we examine a general method of approximation, known as the Kikuchi approximation method, for finding the marginals of a product distribution, as well as the corresponding partition function. The Kikuchi approximation method defines a certain constrained optimization problem, called the Kikuchi problem,...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/0899766054026693

    authors: Pakzad P,Anantharam V

    更新日期:2005-08-01 00:00:00

  • Some sampling properties of common phase estimators.

    abstract::The instantaneous phase of neural rhythms is important to many neuroscience-related studies. In this letter, we show that the statistical sampling properties of three instantaneous phase estimators commonly employed to analyze neuroscience data share common features, allowing an analytical investigation into their beh...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/NECO_a_00422

    authors: Lepage KQ,Kramer MA,Eden UT

    更新日期:2013-04-01 00:00:00

  • Scalable Semisupervised Functional Neurocartography Reveals Canonical Neurons in Behavioral Networks.

    abstract::Large-scale data collection efforts to map the brain are underway at multiple spatial and temporal scales, but all face fundamental problems posed by high-dimensional data and intersubject variability. Even seemingly simple problems, such as identifying a neuron/brain region across animals/subjects, become exponential...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/NECO_a_00852

    authors: Frady EP,Kapoor A,Horvitz E,Kristan WB Jr

    更新日期:2016-08-01 00:00:00

  • A semiparametric Bayesian model for detecting synchrony among multiple neurons.

    abstract::We propose a scalable semiparametric Bayesian model to capture dependencies among multiple neurons by detecting their cofiring (possibly with some lag time) patterns over time. After discretizing time so there is at most one spike at each interval, the resulting sequence of 1s (spike) and 0s (silence) for each neuron ...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/NECO_a_00631

    authors: Shahbaba B,Zhou B,Lan S,Ombao H,Moorman D,Behseta S

    更新日期:2014-09-01 00:00:00

  • A Novel Reconstruction Framework for Time-Encoded Signals with Integrate-and-Fire Neurons.

    abstract::Integrate-and-fire neurons are time encoding machines that convert the amplitude of an analog signal into a nonuniform, strictly increasing sequence of spike times. Under certain conditions, the encoded signals can be reconstructed from the nonuniform spike time sequences using a time decoding machine. Time encoding a...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/NECO_a_00764

    authors: Florescu D,Coca D

    更新日期:2015-09-01 00:00:00

  • Dissociable forms of repetition priming: a computational model.

    abstract::Nondeclarative memory and novelty processing in the brain is an actively studied field of neuroscience, and reducing neural activity with repetition of a stimulus (repetition suppression) is a commonly observed phenomenon. Recent findings of an opposite trend-specifically, rising activity for unfamiliar stimuli-questi...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/NECO_a_00569

    authors: Makukhin K,Bolland S

    更新日期:2014-04-01 00:00:00

  • Enhanced stimulus encoding capabilities with spectral selectivity in inhibitory circuits by STDP.

    abstract::The ability to encode and transmit a signal is an essential property that must demonstrate many neuronal circuits in sensory areas in addition to any processing they may provide. It is known that an appropriate level of lateral inhibition, as observed in these areas, can significantly improve the encoding ability of a...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/NECO_a_00100

    authors: Coulon A,Beslon G,Soula HA

    更新日期:2011-04-01 00:00:00

  • Correlational Neural Networks.

    abstract::Common representation learning (CRL), wherein different descriptions (or views) of the data are embedded in a common subspace, has been receiving a lot of attention recently. Two popular paradigms here are canonical correlation analysis (CCA)-based approaches and autoencoder (AE)-based approaches. CCA-based approaches...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/NECO_a_00801

    authors: Chandar S,Khapra MM,Larochelle H,Ravindran B

    更新日期:2016-02-01 00:00:00

  • On the performance of voltage stepping for the simulation of adaptive, nonlinear integrate-and-fire neuronal networks.

    abstract::In traditional event-driven strategies, spike timings are analytically given or calculated with arbitrary precision (up to machine precision). Exact computation is possible only for simplified neuron models, mainly the leaky integrate-and-fire model. In a recent paper, Zheng, Tonnelier, and Martinez (2009) introduced ...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/NECO_a_00112

    authors: Kaabi MG,Tonnelier A,Martinez D

    更新日期:2011-05-01 00:00:00

  • An amplitude equation approach to contextual effects in visual cortex.

    abstract::A mathematical theory of interacting hypercolumns in primary visual cortex (V1) is presented that incorporates details concerning the anisotropic nature of long-range lateral connections. Each hypercolumn is modeled as a ring of interacting excitatory and inhibitory neural populations with orientation preferences over...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/089976602317250870

    authors: Bressloff PC,Cowan JD

    更新日期:2002-03-01 00:00:00