Abstract
A key problem in systems neuroscience is to characterize how populations of neurons encode information in their patterns of activity. An understanding of the encoding process is essential both for gaining insight into the origins of perception and for the development of brain-computer interfaces. However, this characterization is complicated by the highly variable nature of neural responses, and thus usually requires probabilistic methods for analysis. Drawing on techniques from statistical modeling and machine learning, we review recent methods for extracting important variables that quantitatively describe how sensory information is encoded in neural activity. In particular, we discuss methods for estimating receptive fields, modeling neural population dynamics, and inferring low dimensional latent structure from a population of neurons, in the context of both electrophysiology and calcium imaging data.
Highlights
An animal’s perceptual capabilities critically depend on the ability of its brain to form appropriate representations of sensory stimuli
Many of the statistical models discussed in this review are abstract mathematical descriptions of how stimuli are related to patterns of neural activity
There are cases where the goal is to infer biophysical variables, as in e.g., models for calcium imaging data or for the anatomical architecture of a neural circuit, and greater care must be taken to constrain the model by relevant physiological data (Paninski et al, 2007; Real et al, 2017; Latimer et al, 2018)
Summary
An animal’s perceptual capabilities critically depend on the ability of its brain to form appropriate representations of sensory stimuli. By fitting the model one can extract important variables that quantitatively describe the encoding procedure taking place Such models enable the estimation of receptive fields and/or interneuronal coupling strengths. Linear and generalized linear models are among the most straightforward classes of statistical models for spike trains and assume that a neuron’s activity is a noisy linear combination of the stimulus features These models are highly effective at explaining the structure of sensory receptive fields and are computationally tractable, but do not explicitly model the temporal structure of the recorded signal and have difficulty accounting for correlations between neurons in short time windows. A further challenge is presented by calcium imaging, which provides only indirect access to neural activity through recorded fluorescence levels that reflect the concentration of calcium within a neuron. Our goal is to provide sufficient mathematical detail to appreciate the respective strengths and weaknesses of each model, while leaving formal treatment of their associated fitting algorithms to their original sources
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