Constrained brain volume in an efficient coding model explains the fraction of excitatory and inhibitory neurons in sensory cortices

• Type of Material: Article
• Authors: Arish Alreja, Carnegie Mellon University; Ilya Nemenman, Emory University; Christopher J Rozell, Georgia Institute of Technology, Atlanta
• Language: English
• Date: 2022-01-01
• Publisher: PLOS
• Publication Version: Final Published Version
• Copyright Statement: © 2022 Alreja et al
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.pcbi.1009642
• Title of Journal or Parent Work: PLoS Computational Biology
• Volume: 18
• Issue: 1
• Start Page: e1009642
• End Page: e1009642
• Grant/Funding Information: This work was partially supported by National Institutes of Health (NIH; www.nih.gov) R01EY019965 and National Science Foundation (NSF; www.nsf.gov) CCF-1350954 to C.R and NIH EB022872, NS099375, NS084844 and NSF BCS1822677 to I.N. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
• Keywords: Primates; Action Potentials; Animals; Computational Biology; Models, Neurological; Visual Cortex; Cats; Rats; Organ Size; Neurons
• Research Categories: Computer Science

Abstract

The number of neurons in mammalian cortex varies by multiple orders of magnitude across different species. In contrast, the ratio of excitatory to inhibitory neurons (E:I ratio) varies in a much smaller range, from 3:1 to 9:1 and remains roughly constant for different sensory areas within a species. Despite this structure being important for understanding the function of neural circuits, the reason for this consistency is not yet understood. While recent models of vision based on the efficient coding hypothesis show that increasing the number of both excitatory and inhibitory cells improves stimulus representation, the two cannot increase simultaneously due to constraints on brain volume. In this work, we implement an efficient coding model of vision under a constraint on the volume (using number of neurons as a surrogate) while varying the E:I ratio. We show that the performance of the model is optimal at biologically observed E:I ratios under several metrics. We argue that this happens due to trade-offs between the computational accuracy and the representation capacity for natural stimuli. Further, we make experimentally testable predictions that 1) the optimal E:I ratio should be higher for species with a higher sparsity in the neural activity and 2) the character of inhibitory synaptic distributions and firing rates should change depending on E:I ratio. Our findings, which are supported by our new preliminary analyses of publicly available data, provide the first quantitative and testable hypothesis based on optimal coding models for the distribution of excitatory and inhibitory neural types in the mammalian sensory cortices.