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83
Rate Coding Versus Temporal Order Coding: What the Retinal Ganglion Cells Tell the Visual Cortex
, 2001
"... It is often supposed that messages sent to the visual cortex by the... ..."
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Cited by 97 (15 self)
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It is often supposed that messages sent to the visual cortex by the...
Maximum likelihood estimation of a stochastic integrateandfire neural encoding model
, 2004
"... We examine a cascade encoding model for neural response in which a linear filtering stage is followed by a noisy, leaky, integrateandfire spike generation mechanism. This model provides a biophysically more realistic alternative to models based on Poisson (memoryless) spike generation, and can eff ..."
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Cited by 83 (24 self)
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We examine a cascade encoding model for neural response in which a linear filtering stage is followed by a noisy, leaky, integrateandfire spike generation mechanism. This model provides a biophysically more realistic alternative to models based on Poisson (memoryless) spike generation, and can effectively reproduce a variety of spiking behaviors seen in vivo. We describe the maximum likelihood estimator for the model parameters, given only extracellular spike train responses (not intracellular voltage data). Specifically, we prove that the log likelihood function is concave and thus has an essentially unique global maximum that can be found using gradient ascent techniques. We develop an efficient algorithm for computing the maximum likelihood solution, demonstrate the effectiveness of the resulting estimator with numerical simulations, and discuss a method of testing the model’s validity using timerescaling and density evolution techniques.
Characterization of Neural Responses with Stochastic Stimuli
 TO APPEAR IN: THE NEW COGNITIVE NEUROSCIENCES, 3RD EDITION EDITOR: M. GAZZANIGA
, 2004
"... ose response properties are not at least partially known in advance. This chapter provides an overview of some recently developed characterization methods. In general, the ingredients of the problem are: (a) the selection of a set of experimental stimuli; (b) selection of a model of response; (c) a ..."
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Cited by 72 (27 self)
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ose response properties are not at least partially known in advance. This chapter provides an overview of some recently developed characterization methods. In general, the ingredients of the problem are: (a) the selection of a set of experimental stimuli; (b) selection of a model of response; (c) a procedure for fitting (estimation) of the model. We discuss solutions of this problem that combine stochastic stimuli with models based on an initial linear filtering stage that serves to reduce the dimensionality of the stimulus space. We begin by describing classical reverse correlation in this context, and then discuss several recent generalizations that increase the power and flexibility of this basic method. Thanks to Brian Lau, Dario Ringach, Nicole Rust, and Brian Wandell for helpful comments on the manuscript. This work was funded by the Howard Hughes Medical Institute, and the SloanSwartz Center for Theoretical Visual Neuroscience at New York University. 1 Reverse correlation M
Prediction and Decoding of Retinal Ganglion Cell Responses with a Probabilistic Spiking Model
, 2005
"... ... generation. We show that the stimulus selectivity, reliability, and timing precision of primate retinal ganglion cell (RGC) light responses can be reproduced accurately with a simple model consisting of a leaky integrateandfire spike generator driven by a linearly filtered stimulus, a postspik ..."
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Cited by 66 (21 self)
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... generation. We show that the stimulus selectivity, reliability, and timing precision of primate retinal ganglion cell (RGC) light responses can be reproduced accurately with a simple model consisting of a leaky integrateandfire spike generator driven by a linearly filtered stimulus, a postspike current, and a Gaussian noise current. We fit model parameters for individual RGCs by maximizing the likelihood of observed spike responses to a stochastic visual stimulus. Although compact, the fitted model predicts the detailed time structure of responses to novel stimuli, accurately capturing the interaction between the spiking history and sensory stimulus selectivity. The model also accounts for the variability in responses to repeated stimuli, even when fit to data from a single (nonrepeating) stimulus sequence. Finally, the model can be used to derive an explicit, maximumlikelihood decoding rule for neural spike trains, thus providing a tool for assessing the limitations that spiking variability imposes on sensory performance.
Stochastic nature of precisely timed spike patterns in visual system neuronal responses
 J. NEUROPHYSIOL
, 1999
"... It is not clear how information related to cognitive or psychological processes is carried by or represented in the responses of single neurons. One provocative proposal is that precisely timed spike patterns play a role in carrying such information. This would require that these spike patterns ha ..."
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Cited by 61 (3 self)
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It is not clear how information related to cognitive or psychological processes is carried by or represented in the responses of single neurons. One provocative proposal is that precisely timed spike patterns play a role in carrying such information. This would require that these spike patterns have the potential for carrying information that would not be available from other measures such as spike count or latency. We examined exactly timed (1ms precision) triplets and quadruplets of spikes in the stimuluselicited responses of lateral geniculate nucleus (LGN) and primary visual cortex (V1) neurons of the awake fixating rhesus monkey. Large numbers of these precisely timed spike patterns were found. Information theoretical analysis showed that the precisely timed spike patterns carried only information already available from spike count, suggesting that the number of precisely timed spike
Synergy, Redundancy, and Independence in Population Codes
 The Journal of Neuroscience
, 2003
"... A key issue in understanding the neural code for an ensemble of neurons is the nature and strength of correlations between neurons and how these correlations are related to the stimulus. The issue is complicated by the fact that there is not a single notion of independence or lack of correlation. We ..."
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Cited by 59 (0 self)
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A key issue in understanding the neural code for an ensemble of neurons is the nature and strength of correlations between neurons and how these correlations are related to the stimulus. The issue is complicated by the fact that there is not a single notion of independence or lack of correlation. We distinguish three kinds: (1) activity independence; (2) conditional independence; and (3) information independence. Each notion is related to an information measure: the information between cells, the information between cells given the stimulus, and the synergy of cells about the stimulus, respectively. We show that these measures form an interrelated framework for evaluating contributions of signal and noise correlations to the joint information conveyed about the stimulus and that at least two of the three measures must be calculated to characterize a population code. This framework is compared with others recently proposed in the literature. In addition, we distinguish questions about how information is encoded by a population of neurons from how that information can be decoded. Although information theory is natural and powerful for questions of encoding, it is not sufficient for characterizing the process of decoding. Decoding fundamentally requires an error measure that quantifies the importance of the deviations of estimated stimuli from actual stimuli. Because there is no a priori choice of error measure, questions about decoding cannot be put on the same level of generality as for encoding.
Statistical models for neural encoding, decoding, and optimal stimulus design
 Computational Neuroscience: Progress in Brain Research
, 2006
"... There are two basic problems in the statistical analysis of neural data. The “encoding” problem concerns how information is encoded in neural spike trains: can we predict the spike trains of a neuron (or population of neurons), given an arbitrary stimulus or observed motor response? Conversely, the ..."
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Cited by 53 (17 self)
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There are two basic problems in the statistical analysis of neural data. The “encoding” problem concerns how information is encoded in neural spike trains: can we predict the spike trains of a neuron (or population of neurons), given an arbitrary stimulus or observed motor response? Conversely, the “decoding ” problem concerns how much information is in a spike train: in particular, how well can we estimate the stimulus that gave rise to the spike train? This chapter describes statistical modelbased techniques that in some cases provide a unified solution to these two coding problems. These models can capture stimulus dependencies as well as spike history and interneuronal interaction effects in population spike trains, and are intimately related to biophysicallybased models of integrateandfire type. We describe flexible, powerful likelihoodbased methods for fitting these encoding models and then for using the models to perform optimal decoding. Each of these (apparently quite difficult) tasks turn out to be highly computationally tractable, due to a key concavity property of the model likelihood. Finally, we return to the encoding problem to describe how to use these models to adaptively optimize the stimuli presented to the cell on a trialbytrial basis, in order that we may infer the optimal model parameters as efficiently as possible.
Differential AttentionDependent Response Modulation across Cell Classes in Macaque Visual Area V4
 NEURON
, 2007
"... The cortex contains multiple cell types, but studies of attention have not distinguished between them, limiting understanding of the local circuits that transform attentional feedback into improved visual processing. Parvalbuminexpressing inhibitory interneurons can be distinguished from pyramidal ..."
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Cited by 53 (6 self)
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The cortex contains multiple cell types, but studies of attention have not distinguished between them, limiting understanding of the local circuits that transform attentional feedback into improved visual processing. Parvalbuminexpressing inhibitory interneurons can be distinguished from pyramidal neurons based on their briefer action potential durations. We recorded neurons in area V4 as monkeys performed an attentiondemanding task. We find that the distribution of action potential durations is strongly bimodal. Neurons with narrow action potentials have higher firing rates and larger attentiondependent increases in absolute firing rate than neurons with broad action potentials. The percentage increase in response is similar across the two classes. We also find evidence that attention increases the reliability of the neuronal response. This modulation is more than twofold stronger among putative interneurons. These findings lead to the surprising conclusion that the strongest attentional modulation occurs among local interneurons that do not transmit signals between areas.
Spiketriggered neural characterization
 Journal of Vision
, 2006
"... Response properties of sensory neurons are commonly described using receptive fields. This description may be formalized in a model that operates with a small set of linear filters whose outputs are nonlinearly combined to determine the instantaneous firing rate. Spiketriggered average and covarian ..."
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Cited by 44 (4 self)
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Response properties of sensory neurons are commonly described using receptive fields. This description may be formalized in a model that operates with a small set of linear filters whose outputs are nonlinearly combined to determine the instantaneous firing rate. Spiketriggered average and covariance analyses can be used to estimate the filters and nonlinear combination rule from extracellular experimental data. We describe this methodology, demonstrating it with simulated model neuron examples that emphasize practical issues that arise in experimental situations.
Sequential optimal design of neurophysiology experiments
, 2008
"... Adaptively optimizing experiments has the potential to significantly reduce the number of trials needed to build parametric statistical models of neural systems. However, application of adaptive methods to neurophysiology has been limited by severe computational challenges. Since most neurons are hi ..."
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Cited by 42 (8 self)
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Adaptively optimizing experiments has the potential to significantly reduce the number of trials needed to build parametric statistical models of neural systems. However, application of adaptive methods to neurophysiology has been limited by severe computational challenges. Since most neurons are high dimensional systems, optimizing neurophysiology experiments requires computing highdimensional integrations and optimizations in real time. Here we present a fast algorithm for choosing the most informative stimulus by maximizing the mutual information between the data and the unknown parameters of a generalized linear model (GLM) which we want to fit to the neuron’s activity. We rely on important logconcavity and asymptotic normality properties of the posterior to facilitate the required computations. Our algorithm requires only lowrank matrix manipulations and a 2dimensional search to choose the optimal stimulus. The average running time of these operations scales quadratically with the dimensionality of the GLM, making realtime adaptive experimental design feasible even for highdimensional stimulus and parameter spaces. For example, we