Frequency-separated principal component analysis of cortical population activity

Abstract

A hallmark of neocortical activity is the presence of low-dimensional fluctuations in firing rate that are coordinated across neurons. However, the impact of these fluctuations on sensory processing remains unclear. Here, we examined fluctuations in populations of orientation-selective neurons from anesthetized macaque primary visual cortex (V1) during stimulus viewing as well as spontaneous activity. We introduce a novel approach termed frequency-separated principal components analysis (FS-PCA) to characterize these fluctuations. This method unveiled a distribution of components with a broad range of frequencies whose eigenvalues and variance followed an approximate power-law. During stimulus viewing, subpopulations of V1 neurons correlated either positively or negatively with low-dimensional fluctuations. These two subpopulations displayed distinct activation properties and noise correlations in response to sensory input. Together, results suggest that slow, low-dimensional fluctuations in V1 population activity shape the response of individual neurons to oriented stimuli and may impact the transmission of sensory information to downstream regions of the primary visual system.

Estimating null and potent modes of feedforward communication in a computational model of cortical activity

“…These fluctuations may contribute to the scope of null space interactions across visual areas. However, a limitation of this explanation is that anesthesia-induced fluctuations are typically one-dimensional 15 . By comparison, our results suggest the presence of several modes in the null space of V1-V2 communication (Fig.…”

“…These analyses suggest that reliable patterns of covariation amongst cells, termed "neural modes", emerge from the collective behavior of large neuronal groups. These neural modes are reported in early visual cortex (V1), where the activity of large groups of cells is captured by linear models with only a few dimensions 15 , 16 .…”

“…Several approaches have been proposed to reduce the sheer volume of neural data and summarize neuronal population activity using a handful of dimensions that reflect broad interactions within local circuits 10 – 15 . These analyses suggest that reliable patterns of covariation amongst cells, termed "neural modes", emerge from the collective behavior of large neuronal groups.…”

Estimating null and potent modes of feedforward communication in a computational model of cortical activity

“…Synaptic weights 𝐖 between the two areas were adjusted such that a specific set of neural modes were set to null. This was achieved by solving a linear equation that projects the activity from the sender network to the receiver network (see Materials and Methods, equations (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)). Unless otherwise stated, 10 seconds of simulated activity was generated for each run of the model.…”

“…Several approaches have been proposed to reduce the sheer volume of neural data and summarize neuronal population activity using a handful of dimensions that reflect broad interactions within local circuits [10][11][12][13][14][15]. These analyses suggest that reliable patterns of covariation amongst cells, termed "neural modes", emerge from the collective behavior of large neuronal groups.…”