Literature DB >> 1517829

Time course of neural responses discriminating different views of the face and head.

M W Oram1, D I Perrett.   

Abstract

1. Measurements of the magnitude and time course of response were made from 44 cells responsive to static head views at different levels of stimulus effectiveness. In this way responses to complex stimulus patterns evoking good, poor, and midrange responses could be compared across the cell population. 2. Cells exhibiting both good and poor initial discrimination between head views were found at short and long latencies; there was no correlation of any of the temporal response parameters measured with cell response latency. 3. The time course of the population response to the most effective stimuli showed a rapid increase to a peak firing rate (onset to peak, rise time, 58 ms) that was on average 115 spikes/s above spontaneous activity (S/A), followed by slower decay (decay time, 93 ms) to a maintained discharge rate (15% of the peak rate above S/A). 4. Discrimination between responses to different head views exhibited by the population showed a sharp rise and reached highly significant levels within 25 ms after the population's response onset. 5. On average, activity in a single neuron (the Average Cell) rises to 44% of its peak response rate within 5 ms of the response onset. 6. The Average Cell also showed exceptionally fast discrimination between views, significant within 5 ms of response onset. 7. It is argued that the fast rise in firing rate, followed by a decay to a lower rate and the very fast emergence of discrimination are features of pattern processing present in real neural systems that are lacking in many processing models based on artificial networks of neuronlike elements, particularly those where discrimination relies on top-down and/or lateral competitive inhibition. 8. It is concluded that the only way to account for the rapid discrimination is to consider a coding system in which the first spike from multiple sources is used to transmit information between stages of processing.

Mesh:

Year:  1992        PMID: 1517829     DOI: 10.1152/jn.1992.68.1.70

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


  44 in total

1.  Correlations and the encoding of information in the nervous system.

Authors:  S Panzeri; S R Schultz; A Treves; E T Rolls
Journal:  Proc Biol Sci       Date:  1999-05-22       Impact factor: 5.349

2.  Correlated firing in macaque visual area MT: time scales and relationship to behavior.

Authors:  W Bair; E Zohary; W T Newsome
Journal:  J Neurosci       Date:  2001-03-01       Impact factor: 6.167

Review 3.  The temporal resolution of neural codes: does response latency have a unique role?

Authors:  M W Oram; D Xiao; B Dritschel; K R Payne
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2002-08-29       Impact factor: 6.237

4.  Correlates of transsaccadic integration in the primary visual cortex of the monkey.

Authors:  Paul S Khayat; Henk Spekreijse; Pieter R Roelfsema
Journal:  Proc Natl Acad Sci U S A       Date:  2004-08-10       Impact factor: 11.205

5.  Bright illusions reduce the eye's pupil.

Authors:  Bruno Laeng; Tor Endestad
Journal:  Proc Natl Acad Sci U S A       Date:  2012-01-23       Impact factor: 11.205

6.  Running as fast as it can: how spiking dynamics form object groupings in the laminar circuits of visual cortex.

Authors:  Jasmin Léveillé; Massimiliano Versace; Stephen Grossberg
Journal:  J Comput Neurosci       Date:  2010-01-29       Impact factor: 1.621

7.  Internal curvature signal and noise in low- and high-level vision.

Authors:  Timothy D Sweeny; Marcia Grabowecky; Yee Joon Kim; Satoru Suzuki
Journal:  J Neurophysiol       Date:  2011-01-05       Impact factor: 2.714

8.  Subset of thin spike cortical neurons preserve the peripheral encoding of stimulus onsets.

Authors:  Frank G Lin; Robert C Liu
Journal:  J Neurophysiol       Date:  2010-10-13       Impact factor: 2.714

9.  A parameterized digital 3D model of the Rhesus macaque face for investigating the visual processing of social cues.

Authors:  Aidan P Murphy; David A Leopold
Journal:  J Neurosci Methods       Date:  2019-06-20       Impact factor: 2.390

10.  Feature extraction from spike trains with Bayesian binning: 'latency is where the signal starts'.

Authors:  Dominik Endres; Mike Oram
Journal:  J Comput Neurosci       Date:  2009-05-16       Impact factor: 1.621

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