Abstract
Neuroscience models come in a wide range of scales and specificity, from mean-field rate models to large-scale networks of spiking neurons. There are potential trade-offs between simplicity and realism, versatility and computational speed. This paper is about large-scale cortical network models, and the question we address is one of scalability: would scaling down cell density impact a network’s ability to reproduce cortical dynamics and function? We investigated this problem using a previously constructed realistic model of the monkey visual cortex that is true to size. Reducing cell density gradually up to 50-fold, we studied changes in model behavior. Size reduction without parameter adjustment was catastrophic. Surprisingly, relatively minor compensation in synaptic weights guided by a theoretical algorithm restored mean firing rates and basic function such as orientation selectivity to models 10-20 times smaller than the real cortex. Not all was normal in the reduced model cortices: intracellular dynamics acquired a character different from that of real neurons, and while the ability to relay feedforward inputs remained intact, reduced models showed signs of deficiency in functions that required dynamical interaction among cortical neurons. These findings are not confined to models of the visual cortex, and modelers should be aware of potential issues that accompany size reduction. Broader implications of this study include the importance of homeostatic maintenance of firing rates, and the functional consequences of feedforward versus recurrent dynamics, ideas that may shed light on other species and on systems suffering cell loss.
Highlights
One of the greatest challenges of science today is to unlock the mysteries of the brain
Can one scale down a cortical network without loss of realism? Are neuronal network models scalable, that is, are emergent dynamics retained in network models that are orders of magnitude smaller than the real cortex? To what degree can one scale down a cortical network without impacting its performance or compromising its function? Are some ways better than others to do the downsizing, and are there ways to compensate for inevitable losses? These fundamental questions of theoretical neuroscience motivated this investigation
We offer insights gleaned from a case study, namely a model of the monkey primary visual cortex (V1), which is very similar to human visual cortex
Summary
One of the greatest challenges of science today is to unlock the mysteries of the brain. Modeling is the key to understanding cortical functions, and to build a model of cortex, one is confronted with the issue of size: the number of neurons in the human cortex is approximately 20 × 109 [1]. If one could work with a model that consists of only 1% of the neurons per unit area in real cortex, one would be able to simulate, with similar resources, regions of cortex 100 times larger, or do the simulation 100 times faster. Can one scale down a cortical network without loss of realism? Are neuronal network models scalable, that is, are emergent dynamics retained in network models that are orders of magnitude smaller than the real cortex? To what degree can one scale down a cortical network without impacting its performance or compromising its function? Can one scale down a cortical network without loss of realism? Are neuronal network models scalable, that is, are emergent dynamics retained in network models that are orders of magnitude smaller than the real cortex? To what degree can one scale down a cortical network without impacting its performance or compromising its function? Are some ways better than others to do the downsizing, and are there ways to compensate for inevitable losses? These fundamental questions of theoretical neuroscience motivated this investigation
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
