@inproceedings{7d3de7d5325c47be97b4eac5321a1c48,
title = "Competing inhibition-stabilized networks in sensory and memory processing",
abstract = "In simplified models of neocortical circuits, inhibition is either modeled in a feedforward manner or through mutual inhibitory interactions that provide for competition between neuronal populations. By contrast, recent work has suggested a critical role for recurrent inhibition as a negative feedback element that stabilizes otherwise unstable recurrent excitation. Here, we show how models based upon a motif of recurrently connected 'E-I' pairs of excitatory and inhibitory units can be used to describe experimental observations in sensory and memory networks. In a sensory network model of binocular rivalry, a model based on competing E-I motifs captures psychophysical observations about how incongruous images presented to the two eyes compete. In a model of cortical working memory, an architecturally similar model with modified synaptic time constants can mathematically accumulate signals into a working memory buffer in a manner that is robust to the abrupt removal of cells. These results suggest the inhibition-stabilized EI motif as a fundamental building block for models of a wide array of neocortical dynamics.",
keywords = "E-I motif, integrator, memory, negative feedback, neural network, rivalry",
author = "Lankow, {Benjamin S.} and Goldman, {Mark S}",
year = "2019",
month = feb,
day = "19",
doi = "10.1109/ACSSC.2018.8645209",
language = "English (US)",
series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",
publisher = "IEEE Computer Society",
pages = "97--103",
editor = "Matthews, {Michael B.}",
booktitle = "Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018",
note = "52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 ; Conference date: 28-10-2018 Through 31-10-2018",
}