Abstract
How interactions between neurons relate to tuned neural responses is a longstanding question in systems neuroscience. Here we use statistical modeling and simultaneous multi-electrode recordings to explore the relationship between these interactions and tuning curves in six different brain areas. We find that, in most cases, functional interactions between neurons provide an explanation of spiking that complements and, in some cases, surpasses the influence of canonical tuning curves. Modeling functional interactions improves both encoding and decoding accuracy by accounting for noise correlations and features of the external world that tuning curves fail to capture. In cortex, modeling coupling alone allows spikes to be predicted more accurately than tuning curve models based on external variables. These results suggest that statistical models of functional interactions between even relatively small numbers of neurons may provide a useful framework for examining neural coding.
Highlights
One of the central tenets of systems neuroscience is that the functional properties of neurons, such as receptive fields and tuning curves, arise from the inputs that each neuron receives from pre-synaptic neurons
Due to the fact that neurons are not independent, these models can improve both encoding accuracy as well as decoding accuracy
Time-instantaneous, generalized linear models (GLMs) to recorded spike trains from 10 different datasets and attempt to predict spiking given: 1) external variables, 2) the activity of other observed neurons, or 3) both external variables and the activity of other observed neurons. After fitting these models to spike data the estimated parameters correspond to a typical tuning curve model, a phenomenological model of interactions between neurons, and a full model that allows functional interactions between neurons to provide an alternative explanation for the spiking that is traditionally attributed to tuning to external variables
Summary
One of the central tenets of systems neuroscience is that the functional properties of neurons, such as receptive fields and tuning curves, arise from the inputs that each neuron receives from pre-synaptic neurons. To understand how interactions between neurons drive neural activity, recent model-based statistical methods attempt to predict the activity of each neuron based on the activity of other simultaneously observed neurons in addition to any external variables, such as the orientation of a visual stimulus or the direction of hand movement [15,16,17,18,19]. This type of inferential approach provides estimates of potential interactions between neurons and allows us to assess how much external variables or interactions between neurons may have contributed to the observed spiking. Due to the fact that neurons are not independent, these models can improve both encoding accuracy (how well neural responses can be predicted) as well as decoding accuracy (how well external variables can be predicted from neural responses)
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