Abstract
Cortical networks that have been found to operate close to a critical point exhibit joint activations of large numbers of neurons. However, in motor cortex of the awake macaque monkey, we observe very different dynamics: massively parallel recordings of 155 single-neuron spiking activities show weak fluctuations on the population level. This a priori suggests that motor cortex operates in a noncritical regime, which in models, has been found to be suboptimal for computational performance. However, here, we show the opposite: The large dispersion of correlations across neurons is the signature of a second critical regime. This regime exhibits a rich dynamical repertoire hidden from macroscopic brain signals but essential for high performance in such concepts as reservoir computing. An analytical link between the eigenvalue spectrum of the dynamics, the heterogeneity of connectivity, and the dispersion of correlations allows us to assess the closeness to the critical point.
Highlights
Cortical networks that have been found to operate close to a critical point exhibit joint activations of large numbers of neurons
For the covariance c that is integrated over the time lag, the relation c(W ) is independent of the neuron model; it holds for networks of spiking model neurons as well as for binary model neurons and even for continuous rate dynamics [18]
We show that a linear model explains the on average weak pairwise correlations and the wide dispersion across cells that we observe in massively parallel spike recordings of macaque motor cortex
Summary
Cortical networks that have been found to operate close to a critical point exhibit joint activations of large numbers of neurons. In motor cortex of the awake macaque monkey, we observe very different dynamics: massively parallel recordings of 155 single-neuron spiking activities show weak fluctuations on the population level. This a priori suggests that motor cortex operates in a noncritical regime, which in models, has been found to be suboptimal for computational performance. The data rather reveal weak and fast fluctuations of the population activity This observation is in line with the network operating in the so-called balanced state [7]: in this model architecture, the connectivity is endowed with an excess of inhibitory feedback so that a low level of activity arises that is dynamically stabilized. Identifying such directions in the data is obviously a daunting task, and we are in need of indirect indicators of such a state
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