Paradigmatic Working Memory (Attractor) Cell in IT Cortex

We discuss paradigmatic properties of the activity of single cells comprising an attractora developed stable delay activity distribution. To demonstrate these properties and a methodology for measuring their values, we present a detailed account of the spike activity recorded from a single cell in the inferotemporal cortex of a monkey performing a delayed match-to-sample (DMS) task of visual images. In particular, we discuss and exemplify (1) the relation between spontaneous activity and activity immediately preceding the first stimulus in each trial during a series of DMS trials, (2) the effect on the visual response (i.e., activity during stimulation) of stimulus degradation (moving in the space of IT afferents), (3) the behavior of the delay activity (i.e., activity following visual stimulation) under stimulus degradation (attractor dynamics and the basin of attraction), and (4) the propagation of information between trialsthe vehicle for the formation of (contextual) correlations by learning a fixed stimulus sequence (Miyashita, 1988). In the process of the discussion and demonstration, we expose effective tools for the identification and characterization of attractor dynamics.1 A color version of this article is found on the Web at: http://www.fiz.huji.ac.il/staff/acc/faculty/damita

[1]  G. Orban,et al.  Selectivity of macaque inferior temporal neurons for partially occluded shapes , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[2]  Nicolas Brunel,et al.  Learning internal representations in an attractor neural network with analogue neurons , 1995 .

[3]  N Brunel,et al.  Correlations of cortical Hebbian reverberations: theory versus experiment , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[5]  Y. Miyashita,et al.  Neural organization for the long-term memory of paired associates , 1991, Nature.

[6]  J. Fuster.  Memory in the cerebral cortex , 1994 .

[7]  D. Amit,et al.  Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex. , 1997, Cerebral cortex.

[8]  Y. Miyashita,et al.  Neuronal correlate of pictorial short-term memory in the primate temporal cortexYasushi Miyashita , 1988, Nature.

[9]  D. Amit.  The Hebbian paradigm reintegrated: Local reverberations as internal representations , 1995, Behavioral and Brain Sciences.

[10]  Nicolas Brunel,et al.  Global Spontaneous Activity and Local Structured (learned) Delay Activity in Cortex , 1995 .

[11]  P. Goldman-Rakic,et al.  Dissociation of object and spatial processing domains in primate prefrontal cortex. , 1993, Science.

[12]  Y. Miyashita.  Neuronal correlate of visual associative long-term memory in the primate temporal cortex , 1988, Nature.

[13]  Yasushi Miyashita,et al.  Generation of fractal patterns for probing the visual memory , 1991, Neuroscience Research.

[14]  P. Goldman-Rakic,et al.  Modulation of memory fields by dopamine Dl receptors in prefrontal cortex , 1995, Nature.

[15]  K. Nakamura,et al.  Mnemonic firing of neurons in the monkey temporal pole during a visual recognition memory task. , 1995, Journal of neurophysiology.

[16]  N Brunel,et al.  Modeling memory: what do we learn from attractor neural networks? , 1998, Comptes rendus de l'Academie des sciences. Serie III, Sciences de la vie.

[17]  R. Desimone,et al.  Activity of neurons in anterior inferior temporal cortex during a short- term memory task , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[18]  Nicolas Brunel,et al.  Dynamics of an attractor neural network converting temporal into spatial correlations Network: Compu , 1994 .

[19]  D. Amit.  Persistent delay activity in cortex: a Galilean phase in neurophysiology? , 1994 .

[20]  Daniel J. Amit,et al.  Conversion of Temporal Correlations Between Stimuli to Spatial Correlations Between Attractors , 1999, Neural Computation.

[21]  Nicolas Brunel,et al.  Hebbian Learning of Context in Recurrent Neural Networks , 1996, Neural Computation.