Representational Accuracy of Stochastic Neural Populations

Wilke, Stefan; Eurich, Christian W.

doi:10.1162/089976602753284482

Cited by 86 publications

(136 citation statements)

References 64 publications

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“…Because the almost identical effects of adaptation on the two terms, the effect of adaptation on the sum of the two terms will be independent on their relative contribution, suggesting that our results will be valid for a wider range of neuron numbers. In heterogeneous populations this saturation of FI 1 dissolves (Wilke and Eurich 2002;Shamir and Sompolisnky 2006), opening the possibility of an interesting interplay between adaptation and heterogeneity, where for instance adaptation yields a heterogeneous response. However, the readout has to be optimized for the heterogeneity (Shamir and Sompolisnky 2006), which is inconsistent with our decoding set-up (below) and we don't examine this possibility here any further.…”

Section: (C) and (D)mentioning

confidence: 99%

The effect of neural adaptation on population coding accuracy

Cortes¹,

Marinazzo²,

Seriès³

et al. 2011

J Comput Neurosci

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Most neurons in the primary visual cortex initially respond vigorously when a preferred stimulus is presented, but adapt as stimulation continues. The functional consequences of adaptation are unclear. Typically a reduction of firing rate would reduce single neuron accuracy as less spikes are available for decoding, but it has been suggested that on the population level, adaptation increases coding accuracy. This question requires careful analysis as adaptation not only changes the firing rates of neurons, but also the neural variability and correlations between neurons, which affect coding accuracy as well. We calculate the coding accuracy using a computational model that implements two forms of adaptation: spike frequency adaptation and synaptic adaptation in the form of short-term synaptic plasticity. We find that the net effect of adaptation is subtle and heterogeneous. Depending on adaptation mechanism and test stimulus, adaptation can either increase or decrease coding accuracy. We discuss the neurophysiological and psychophysical implications of the findings and relate it to published experimental data.

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Section: (C) and (D)mentioning

confidence: 99%

The effect of neural adaptation on population coding accuracy

Cortes¹,

Marinazzo²,

Seriès³

et al. 2011

J Comput Neurosci

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show abstract

“…From the perspective of information theory, whether a broadly tuned neuron is more effective or less effective than a narrowly tuned neuron depends on several factors, e.g., how many dimensions are to be discriminated, whether noise levels remain proportional to the bandwidth of the filters (Eurich and Wilke 2000;Pouget et al 1999;Wilke and Eurich 2002;Zhang and Sejnowski 1999). It is generally agreed that narrow tuning is more effective in coding a single dimension; however, severely narrow tuning that prevents adequate overlap of receptive fields results in degraded coding (Eurich and Wilke 2000).…”

Section: Broad and Narrow Tuning To Binaural Level By Ai Neuronsmentioning

confidence: 99%

“…It is generally agreed that narrow tuning is more effective in coding a single dimension; however, severely narrow tuning that prevents adequate overlap of receptive fields results in degraded coding (Eurich and Wilke 2000). A recent analysis of Fisher information concluded that optimal coding strategies may not be separable on the basis of a simple dichotomy between narrow and broad tuning (Wilke and Eurich 2002). These authors estimated that a population of neurons with variable tuning widths would be superior to a population of neurons with identical tuning curves.…”

Section: Broad and Narrow Tuning To Binaural Level By Ai Neuronsmentioning

confidence: 99%

Modulation of Level Response Areas and Stimulus Selectivity of Neurons in Cat Primary Auditory Cortex

Zhang

Nakamoto²,

Kitzes³

2005

Journal of Neurophysiology

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Zhang, Jiping, Kyle T. Nakamoto, and Leonard M. Kitzes. Modulation of level response areas and stimulus selectivity of neurons in cat primary auditory cortex. J Neurophysiol 94: 2263-2274, 2005. First published May 25, 2005 10.1152/jn.01207.2004. Sounds commonly occur in sequences, such as in speech. It is therefore important to understand how the occurrence of one sound affects the response to a subsequent sound. We approached this question by determining how a conditioning stimulus alters the response areas of single neurons in the primary auditory cortex (AI) of barbiturate-anesthetized cats. The response areas consisted of responses to stimuli that varied in level at the two ears and delivered at the characteristic frequency of each cell. A binaural conditioning stimulus was then presented Ն50 ms before each of the stimuli comprising the level response area. An effective preceding stimulus alters the shape and severely reduces the size and response magnitude of the level response area. This ability of the preceding stimulus depends on its proximity in the level domain to the level response area, not on its absolute level or on the size of the response it evokes. Preceding stimuli evoke a nonlinear inhibition across the level response area that results in an increased selectivity of a cortical neuron for its preferred binaural stimuli. The selectivity of AI neurons during the processing of a stream of acoustic stimuli is likely to be restricted to a portion of their level response areas apparent in the tone-alone condition. Thus rather than being static, level response areas are fluid; they can vary greatly in extent, shape and response magnitude. The dynamic modulation of the level response area and level selectivity of AI neurons might be related to several tasks confronting the central auditory system. I N T R O D U C T I O NAs acoustic stimuli commonly occur in a temporal relation to other acoustic stimuli, it is important to understand how responses of cortical neurons vary during the processing of a stream of acoustic signals. Throughout the auditory neuroaxis, discharge rate as a function of monaural stimulus level (so called rate-level functions) have been shown to be displaced to higher stimulus levels when stimuli are presented either during or after a conditioning stimulus (Boettcher et

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“…This is a problem because neurons in vivo are correlated (Zohary, Shadlen, & Newsome, 1994), and correlations can have a significant impact on Fisher information (Abbott & Dayan, 1999;Yoon & Sompolinsky, 1998;Sompolinsky, Yoon, Kang, & Shamir, 2001;Wilke & Eurich, 2002;Wu, Nakahara, & Amari, 2001). These researchers investigated the effects of correlations by considering a variety of physiologically inspired parameterizations of covariance matrices, but they did not consider how a network of spiking neurons might generate these covariance structures.…”

Section: Introductionmentioning

confidence: 99%

Insights from a Simple Expression for Linear Fisher Information in a Recurrently Connected Population of Spiking Neurons

2011

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A simple expression for a lower bound of Fisher information is derived for a network of recurrently connected spiking neurons that have been driven to a noise-perturbed steady state. We call this lower bound linear Fisher information, as it corresponds to the Fisher information that can be recovered by a locally optimal linear estimator. Unlike recent similar calculations, the approach used here includes the effects of nonlinear gain functions and correlated input noise and yields a surprisingly simple and intuitive expression that offers substantial insight into the sources of information degradation across successive layers of a neural network. Here, this expression is used to (1) compute the optimal (i.e., informationmaximizing) firing rate of a neuron, (2) demonstrate why sharpening tuning curves by either thresholding or the action of recurrent connectivity is generally a bad idea, (3) show how a single cortical expansion is sufficient to instantiate a redundant population code that can propagate across multiple cortical layers with minimal information loss, and (4) show that optimal recurrent connectivity strongly depends on the covariance structure of the inputs to the network.

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Representational Accuracy of Stochastic Neural Populations

Cited by 86 publications

References 64 publications

The effect of neural adaptation on population coding accuracy

The effect of neural adaptation on population coding accuracy

Modulation of Level Response Areas and Stimulus Selectivity of Neurons in Cat Primary Auditory Cortex

Insights from a Simple Expression for Linear Fisher Information in a Recurrently Connected Population of Spiking Neurons

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