Literature DB >> 11152730

Formal and attribute-specific information in primary visual cortex.

D S Reich1, F Mechler, J D Victor.   

Abstract

We estimate the rates at which neurons in the primary visual cortex (V1) of anesthetized macaque monkeys transmit stimulus-related information in response to three types of visual stimulus. The stimuli-randomly modulated checkerboard patterns, stationary sinusoidal gratings, and drifting sinusoidal gratings-have very different spatiotemporal structures. We obtain the overall rate of information transmission, which we call formal information, by a direct method. We find the highest information rates in the responses of simple cells to drifting gratings (median: 10.3 bits/s, 0.92 bits/spike); responses to randomly modulated stimuli and stationary gratings transmit information at significantly lower rates. In general, simple cells transmit information at higher rates, and over a larger range, than do complex cells. Thus in the responses of V1 neurons, stimuli that are rapidly modulated do not necessarily evoke higher information rates, as might be the case with motion-sensitive neurons in area MT. By an extension of the direct method, we parse the formal information into attribute-specific components, which provide estimates of the information transmitted about contrast and spatiotemporal pattern. We find that contrast-specific information rates vary across neurons-about 0.3 to 2.1 bits/s or 0.05 to 0.22 bits/spike-but depend little on stimulus type. Spatiotemporal pattern-specific information rates, however, depend strongly on the type of stimulus and neuron (simple or complex). The remaining information rate, typically between 10 and 32% of the formal information rate for each neuron, cannot be unambiguously assigned to either contrast or spatiotemporal pattern. This indicates that some information concerning these two stimulus attributes is confounded in the responses of single neurons in V1. A model that considers a simple cell to consist of a linear spatiotemporal filter followed by a static rectifier predicts higher information rates than are found in real neurons and completely fails to replicate the performance of real cells in generating the confounded information.

Mesh:

Year:  2001        PMID: 11152730     DOI: 10.1152/jn.2001.85.1.305

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  13 in total

1.  Natural stimulation of the nonclassical receptive field increases information transmission efficiency in V1.

Authors:  William E Vinje; Jack L Gallant
Journal:  J Neurosci       Date:  2002-04-01       Impact factor: 6.167

2.  Consistency of encoding in monkey visual cortex.

Authors:  M C Wiener; M W Oram; Z Liu; B J Richmond
Journal:  J Neurosci       Date:  2001-10-15       Impact factor: 6.167

3.  Decoding spike trains instant by instant using order statistics and the mixture-of-Poissons model.

Authors:  Matthew C Wiener; Barry J Richmond
Journal:  J Neurosci       Date:  2003-03-15       Impact factor: 6.167

4.  A model-based approach for the analysis of neuronal information transmission in multi-input and -output systems.

Authors:  M Eger; R Eckhorn
Journal:  J Comput Neurosci       Date:  2002 May-Jun       Impact factor: 1.621

5.  Assessing the encoding of stimulus attributes with rapid sequences of stimulus events.

Authors:  M Eger; R Eckhorn
Journal:  J Comput Neurosci       Date:  2002 Nov-Dec       Impact factor: 1.621

6.  Encoding stimulus information by spike numbers and mean response time in primary auditory cortex.

Authors:  Israel Nelken; Gal Chechik; Thomas D Mrsic-Flogel; Andrew J King; Jan W H Schnupp
Journal:  J Comput Neurosci       Date:  2005-10       Impact factor: 1.621

7.  'Simplification' of responses of complex cells in cat striate cortex: suppressive surrounds and 'feedback' inactivation.

Authors:  Cedric Bardy; Jin Yu Huang; Chun Wang; Thomas FitzGibbon; Bogdan Dreher
Journal:  J Physiol       Date:  2006-05-18       Impact factor: 5.182

8.  Spike train analysis toolkit: enabling wider application of information-theoretic techniques to neurophysiology.

Authors:  David H Goldberg; Jonathan D Victor; Esther P Gardner; Daniel Gardner
Journal:  Neuroinformatics       Date:  2009-05-28

9.  Approaches to Information-Theoretic Analysis of Neural Activity.

Authors:  Jonathan D Victor
Journal:  Biol Theory       Date:  2006

10.  A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings.

Authors:  Cesare Magri; Kevin Whittingstall; Vanessa Singh; Nikos K Logothetis; Stefano Panzeri
Journal:  BMC Neurosci       Date:  2009-07-16       Impact factor: 3.288

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