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Gain control network conditions in early sensory coding

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posted on 2023-06-08, 16:48 authored by Eduardo Serrano, Thomas NowotnyThomas Nowotny, Rafael Levi, Brian H Smith, Ramón Huerta
Gain control is essential for the proper function of any sensory system. However, the precise mechanisms for achieving effective gain control in the brain are unknown. Based on our understanding of the existence and strength of connections in the insect olfactory system, we analyze the conditions that lead to controlled gain in a randomly connected network of excitatory and inhibitory neurons. We consider two scenarios for the variation of input into the system. In the first case, the intensity of the sensory input controls the input currents to a fixed proportion of neurons of the excitatory and inhibitory populations. In the second case, increasing intensity of the sensory stimulus will both, recruit an increasing number of neurons that receive input and change the input current that they receive. Using a mean field approximation for the network activity we derive relationships between the parameters of the network that ensure that the overall level of activity of the excitatory population remains unchanged for increasing intensity of the external stimulation. We find that, first, the main parameters that regulate network gain are the probabilities of connections from the inhibitory population to the excitatory population and of the connections within the inhibitory population. Second, we show that strict gain control is not achievable in a random network in the second case, when the input recruits an increasing number of neurons. Finally, we confirm that the gain control conditions derived from the mean field approximation are valid in simulations of firing rate models and Hodgkin-Huxley conductance based models.

Funding

Green Brain; EPSRC; EP/J019690/1

History

Publication status

  • Published

File Version

  • Published version

Journal

PLoS Computational Biology

ISSN

1553-7358

Publisher

Public Library of Science

Issue

7

Volume

9

Article number

e1003133

Department affiliated with

  • Informatics Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2014-03-03

First Open Access (FOA) Date

2014-03-03

First Compliant Deposit (FCD) Date

2014-03-03

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