Abstract
Recent studies have applied dimensionality reduction methods to understand how the multi-dimensional structure of neural population activity gives rise to brain function. It is unclear, however, how the results obtained from dimensionality reduction generalize to recordings with larger numbers of neurons and trials or how these results relate to the underlying network structure. We address these questions by applying factor analysis to recordings in the visual cortex of non-human primates and to spiking network models that self-generate irregular activity through a balance of excitation and inhibition. We compared the scaling trends of two key outputs of dimensionality reduction—shared dimensionality and percent shared variance—with neuron and trial count. We found that the scaling properties of networks with non-clustered and clustered connectivity differed, and that the in vivo recordings were more consistent with the clustered network. Furthermore, recordings from tens of neurons were sufficient to identify the dominant modes of shared variability that generalize to larger portions of the network. These findings can help guide the interpretation of dimensionality reduction outputs in regimes of limited neuron and trial sampling and help relate these outputs to the underlying network structure.
Highlights
Dimensionality reduction methods have revealed compelling descriptions of neural mechanisms underlying decision-making [2, 3], motor control [4, 5], olfaction [6], working memory [7], visual attention [8], audition [9], rule learning [10], and speech [11]
We address these questions by applying factor analysis to recordings in the visual cortex of non-human primates and to spiking network models that self-generate irregular activity through a balance of excitation and inhibition
Varying neuron and trial count for network models within the experimental regime In the previous sections, we identified trends in dshared and percent shared variance using in vivo recordings
Summary
Dimensionality reduction methods (for review, see [1]) have revealed compelling descriptions of neural mechanisms underlying decision-making [2, 3], motor control [4, 5], olfaction [6], working memory [7], visual attention [8], audition [9], rule learning [10], and speech [11] These methods characterize the multi-dimensional structure of neural population activity based on how the activity of different neurons co-varies. Because we can sample as many neurons and trials as desired from a spiking network model, we can measure how the outputs of dimensionality reduction vary over a wide range of neuron and trial counts This allows us to assess whether the results obtained using a limited number of neurons and trials are representative of the larger network. This paper utilizes these three benefits of spiking network models to develop a deeper intuition for the relationship between the outputs of dimensionality reduction and the underlying neural circuit
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