Title: Balanced Excitatory and Inhibitory Synaptic Currents Promote Efficient Coding and Metabolic Efficiency

Abstract

A balance between excitatory and inhibitory synaptic currents is thought to be important for several aspects of information processing in cortical neurons in vivo, including gain control, bandwidth and receptive field structure. These factors will affect the firing rate of cortical neurons and their reliability, with consequences for their information coding and energy consumption. Yet how balanced synaptic currents contribute to the coding efficiency and energy efficiency of cortical neurons remains unclear. We used single compartment computational models with stochastic voltage-gated ion channels to determine whether synaptic regimes that produce balanced excitatory and inhibitory currents have specific advantages over other input regimes. Specifically, we compared models with only excitatory synaptic inputs to those with equal excitatory and inhibitory conductances, and stronger inhibitory than excitatory conductances (i.e. approximately balanced synaptic currents). Using these models, we show that balanced synaptic currents evoke fewer spikes per second than excitatory inputs alone or equal excitatory and inhibitory conductances. However, spikes evoked by balanced synaptic inputs are more informative (bits/spike), so that spike trains evoked by all three regimes have similar information rates (bits/s). Consequently, because spikes dominate the energy consumption of our computational models, approximately balanced synaptic currents are also more energy efficient thanmore » other synaptic regimes. Thus, by producing fewer, more informative spikes approximately balanced synaptic currents in cortical neurons can promote both coding efficiency and energy efficiency.« less

Authors:

Sengupta, Biswa ^[1]; Laughlin, Simon B. ^[2]; Niven, Jeremy E. ^[3]

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+ Show Author Affiliations

1. Univ. College London, London (United Kingdom). Wellcome Trust Centre for Neuroimaging; Indian Inst. of Science, Bangalore (India). Centre for Neuroscience
2. Univ. of Cambridge (United Kingdom). Dept. of Zoology
3. Univ. of Sussex, Falmer (United Kingdom). School of Life Sciences and Centre for Computational Neuroscience and Robotics

Publication Date:

2013-10-03

Research Org.:

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); UT-Battelle LLC/ORNL, Oak Ridge, TN (United States)

Sponsoring Org.:

USDOE Office of Science (SC)

OSTI Identifier:

1565245

Grant/Contract Number:  

AC05-00OR22725

Resource Type:

Journal Article: Accepted Manuscript

Journal Name:

PLoS Computational Biology (Online)

Additional Journal Information:

Journal Volume: 9; Journal Issue: 10; Journal ID: ISSN 1553-7358

Publisher:

Public Library of Science

Country of Publication:

United States

Language:

English

Subject:

59 BASIC BIOLOGICAL SCIENCES; Biochemistry & Molecular Biology; Mathematical & Computational Biology