TY - JOUR
T1 - Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference
AU - Echeveste, Rodrigo
AU - Aitchison, Laurence
AU - Hennequin, Guillaume
AU - Lengyel, Máté
N1 - Publisher Copyright:
© 2020, The Author(s), under exclusive licence to Springer Nature America, Inc.
PY - 2020/9/1
Y1 - 2020/9/1
N2 - Sensory cortices display a suite of ubiquitous dynamical features, such as ongoing noise variability, transient overshoots and oscillations, that have so far escaped a common, principled theoretical account. We developed a unifying model for these phenomena by training a recurrent excitatory–inhibitory neural circuit model of a visual cortical hypercolumn to perform sampling-based probabilistic inference. The optimized network displayed several key biological properties, including divisive normalization and stimulus-modulated noise variability, inhibition-dominated transients at stimulus onset and strong gamma oscillations. These dynamical features had distinct functional roles in speeding up inferences and made predictions that we confirmed in novel analyses of recordings from awake monkeys. Our results suggest that the basic motifs of cortical dynamics emerge as a consequence of the efficient implementation of the same computational function—fast sampling-based inference—and predict further properties of these motifs that can be tested in future experiments.
AB - Sensory cortices display a suite of ubiquitous dynamical features, such as ongoing noise variability, transient overshoots and oscillations, that have so far escaped a common, principled theoretical account. We developed a unifying model for these phenomena by training a recurrent excitatory–inhibitory neural circuit model of a visual cortical hypercolumn to perform sampling-based probabilistic inference. The optimized network displayed several key biological properties, including divisive normalization and stimulus-modulated noise variability, inhibition-dominated transients at stimulus onset and strong gamma oscillations. These dynamical features had distinct functional roles in speeding up inferences and made predictions that we confirmed in novel analyses of recordings from awake monkeys. Our results suggest that the basic motifs of cortical dynamics emerge as a consequence of the efficient implementation of the same computational function—fast sampling-based inference—and predict further properties of these motifs that can be tested in future experiments.
UR - http://www.scopus.com/inward/record.url?scp=85089255480&partnerID=8YFLogxK
U2 - 10.1038/s41593-020-0671-1
DO - 10.1038/s41593-020-0671-1
M3 - Article
C2 - 32778794
AN - SCOPUS:85089255480
SN - 1097-6256
VL - 23
SP - 1138
EP - 1149
JO - Nature Neuroscience
JF - Nature Neuroscience
IS - 9
ER -