Feature Detection in Visual Cortex during Different Functional States

Cortical activity exhibits distinct characteristics in different functional states. In awake behaving animals it shows less synchrony, while in rest or sleeping state cortical activity is most synchronous. Previous studies showed that switching between functional states can change the efficiency of flowing sensory information. Switching between functional states can be triggered by releasing neuromodulators which affect neurotransmitter release probability and depolarization of cortical neurons. In this work we focus on studying primary visual area V1, by using firing rate ring model with short-term synaptic depression (STD). We show that reconstruction of visual features from V1 activity depends on the functional state, with best precision achieved at the state with intermediate release probability. We suggest that this regime corresponds to the state of maximal visual attention.

[1]  K. Harris,et al.  Cortical state and attention , 2011, Nature Reviews Neuroscience.

[2]  S. Romani,et al.  Short‐term plasticity based network model of place cells dynamics , 2015, Hippocampus.

[3]  H. Markram,et al.  The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Michael Okun,et al.  The Subthreshold Relation between Cortical Local Field Potential and Neuronal Firing Unveiled by Intracellular Recordings in Awake Rats , 2010, The Journal of Neuroscience.

[5]  Almut Schüz.  Neuroanatomy in a computational perspective , 1998 .

[6]  Misha Tsodyks,et al.  Population spikes in cortical networks during different functional states , 2012, Front. Comput. Neurosci..

[7]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[8]  Mark C. W. van Rossum,et al.  Recurrent networks with short term synaptic depression , 2009, Journal of Computational Neuroscience.

[9]  R. Tremblay,et al.  GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits , 2016, Neuron.

[10]  J. Cowan,et al.  Excitatory and inhibitory interactions in localized populations of model neurons. , 1972, Biophysical journal.

[11]  H. Sompolinsky,et al.  13 Modeling Feature Selectivity in Local Cortical Circuits , 2022 .

[12]  Lisa M. Giocomo,et al.  Neuromodulation by Glutamate and Acetylcholine can Change Circuit Dynamics by Regulating the Relative Influence of Afferent Input and Excitatory Feedback , 2007, Molecular Neurobiology.

[13]  D. McCormick,et al.  Neurotransmitter control of neocortical neuronal activity and excitability. , 1993, Cerebral cortex.

[14]  Ole Tange,et al.  GNU Parallel 20150322 ('Hellwig') , 2015 .

[15]  H. Sompolinsky,et al.  Theory of orientation tuning in visual cortex. , 1995, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Henry Markram,et al.  Neural Networks with Dynamic Synapses , 1998, Neural Computation.

[17]  M. Scanziani,et al.  How Inhibition Shapes Cortical Activity , 2011, Neuron.

[18]  Guillaume Lajoie,et al.  Dynamic Signal Tracking in a Simple V1 Spiking Model , 2016, Neural Computation.

[19]  Orientation hypercolumns of the visual cortex: Ring model , 2011, Biofizika.

[20]  J. Poulet,et al.  Internal brain state regulates membrane potential synchrony in barrel cortex of behaving mice , 2008, Nature.

[21]  Karl Deisseroth,et al.  Activation of Specific Interneurons Improves V1 Feature Selectivity and Visual Perception , 2012, Nature.

[22]  Michael J. Goard,et al.  Fast Modulation of Visual Perception by Basal Forebrain Cholinergic Neurons , 2013, Nature Neuroscience.

[23]  Ole Tange,et al.  GNU Parallel: The Command-Line Power Tool , 2011, login Usenix Mag..

[24]  D. Hubel,et al.  Receptive fields of single neurones in the cat's striate cortex , 1959, The Journal of physiology.