Abstract
This study examines the relationship between population coding and spatial connection statistics in networks of noisy neurons. Encoding of sensory information in the neocortex is thought to require coordinated neural populations, because individual cortical neurons respond to a wide range of stimuli, and exhibit highly variable spiking in response to repeated stimuli. Population coding is rooted in network structure, because cortical neurons receive information only from other neurons, and because the information they encode must be decoded by other neurons, if it is to affect behavior. However, population coding theory has often ignored network structure, or assumed discrete, fully connected populations (in contrast with the sparsely connected, continuous sheet of the cortex). In this study, we modeled a sheet of cortical neurons with sparse, primarily local connections, and found that a network with this structure could encode multiple internal state variables with high signal-to-noise ratio. However, we were unable to create high-fidelity networks by instantiating connections at random according to spatial connection probabilities. In our models, high-fidelity networks required additional structure, with higher cluster factors and correlations between the inputs to nearby neurons.
Highlights
In order to understand cortical function, it is important to know how populations of neurons work together to encode and process information
Receptive fields are much larger in middle temporal area (MT) than in V1 (Gattass and Gross, 1981)
We begin with an example of 2-D dynamics in a densely connected network
Summary
In order to understand cortical function, it is important to know how populations of neurons work together to encode and process information. Many neurons’ spike rates vary with a given external variable, such as reach direction (Schwartz et al, 1988), suggesting that there may be far fewer dimensions to cortical activity than there are neurons. There are millions of neurons in the primate middle temporal area (MT), but they may encode only a few thousand variables. The activity of MT neurons varies markedly with fields of motion direction and speed (Maunsell and van Essen, 1983), binocular disparity (Zeki, 1974; DeAngelis and Uka, 2003), and to some extent orientation and spatial frequency (Maunsell and Newsome, 1987). For the sake of argument, that each receptive field encodes 10 motion-related variables, and if there are about 1000 distinct receptive fields in MT, perhaps only about 10,000 distinct variables are encoded by the activity of millions of neurons
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