Inhibitory control of correlated intrinsic variability in cortical networks

Abstract

Cortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to multi-neuron recordings from different rodent species, sensory modalities, and behavioral states. The model generated correlated variability without external noise and accurately reproduced the diverse activity patterns in our recordings. Analysis of the model parameters suggested that differences in noise correlations across recordings were due primarily to differences in the strength of feedback inhibition. Further analysis of our recordings confirmed that putative inhibitory neurons were indeed more active during desynchronized cortical states with weak noise correlations. Our results demonstrate that network models with intrinsically-generated variability can accurately reproduce the activity patterns observed in multi-neuron recordings and suggest that inhibition modulates the interactions between intrinsic dynamics and sensory inputs to control the strength of noise correlations.DOI: http://dx.doi.org/10.7554/eLife.19695.001

Spontaneous behaviors drive multidimensional, brain-wide activity

“…Our data are consistent with previous reports describing low-dimensional correlates of locomotion and arousal in visual cortex [5,[7][8][9][10][11][12][13][14], but suggest these results were glimpses of a much larger set of behavioral and cognitive variables encoded by ongoing activity patterns. We found that 16 dimensions of facial motor activity can predict 31% of the reliable spontaneous variance.…”

“…Correlation with the first PC was positive or negative in approximately similar numbers of neurons (57% ± 6.7% SE positive), indicating that two large sub-populations of neurons alternate their activity ( Figure 1F). The slowness of these fluctuations implies a different underlying phenomenon to previously-studied "up and down phases" [3,12,[26][27][28], which alternate at a much faster timescale (100-300 ms instead of 10-20 s) and correlate with most neurons positively. Indeed, up/down phases could not even have been detected in our recordings, which scanned the cortex every 400 ms.…”

“…The unreliable component is sometimes referred to as "Poisson noise", as it would occur in a model where neurons fired a number of spikes drawn from a Poisson distribution whose rate deterministically encoded the state variable. Such behavior can also occur in networks where strong and balanced excitation and inhibition lead to chaotic variations in spike counts even in deterministic simulations [12,21,57].…”

“…An alternative possibility is that ongoing activity could be related to behavioral and cognitive states. The firing of sensory cortical and sensory thalamic neurons correlates with behavioral variables such as locomotion, pupil diameter, and whisking [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Continued encoding of nonvisual variables when visual stimuli are present could at least in part explain the trial-to-trial variability in responses to repeated presentation of identical sensory stimuli [20].…”

Tuning network dynamics from criticality to the chaotic balanced state

“…experimental measurements have revealed relatively asynchronous firing, particularly in 21 vigilant and active behavioral conditions [16][17][18][19][20]. One of the most prominent theoretical 22 explanations of the asynchronous state, the so-called 'chaotic balanced state' hypothesis, 23 is based on balanced E and I [21][22][23][24].…”