Estimating fluctuations in neural representations of uncertain environments
Farhoodi, Sahand; Plitt, Mark; Giocomo, Lisa; Eden, Uri
Neural Coding analyses often reflect an assumption that neural populations respond
uniquely and consistently to particular stimuli. For example, analyses of spatial
remapping in hippocampal populations often assume that each environment has
one unique representation and that remapping occurs over long time scales as
an animal traverses between distinct environments. However, as neuroscience
experiments begin to explore more naturalistic tasks and stimuli, and reflect more
ambiguity in neural representations, methods for analyzing population neural codes
must adapt to reflect these features. In this paper, we develop a new state-space
modeling framework to address two important issues related to remapping. First,
neurons may exhibit significant trial-to-trial or moment-to-moment variability in
the firing patterns used to represent a particular environment or stimulus. Second,
in ambiguous environments and tasks that involve cognitive uncertainty, neural
populations may rapidly fluctuate between multiple representations. The statespace
model addresses these two issues by integrating an observation model, which
allows for multiple representations of the same stimulus or environment, with a
state model, which characterizes the moment-by-moment probability of a shift
in the neural representation. These models allow us to compute instantaneous
estimates of the stimulus or environment currently represented by the population.
We demonstrate the application of this approach to the analysis of population
activity in the CA1 region of hippocampus of a mouse moving through ambiguous
virtual environments. Our analyses demonstrate that many hippocampal cells
express significant trial-to-trial variability in their representations and that the
population representation can fluctuate rapidly between environments within a
single trial when spatial cues are most ambiguous.
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