Randomly connected networks generate emergent selectivity and predict decoding properties of large populations of neurons

Advances in neural recording methods enable sampling from populations of thousands of neurons during the performance of behavioral tasks, raising the question of how recorded activity relates to the theoretical models of computations underlying performance. In the context of decision making in rodents, patterns of functional connectivity between choice-selective cortical neurons, as well as broadly distributed choice information in both excitatory and inhibitory populations, were recently reported [1]. The straightforward interpretation of these data suggests a mechanism relying on specific patterns of anatomical connectivity to achieve selective pools of inhibitory as well as excitatory neurons. We investigate an alternative mechanism for the emergence of these experimental observations using a computational approach. We find that a randomly connected network of excitatory and inhibitory neurons generates single-cell selectivity, patterns of pairwise correlations, and indistinguishable excitatory and inhibitory readout weight distributions, as observed in recorded neural populations. Further, we make the readily verifiable experimental predictions that, for this type of evidence accumulation task, there are no anatomically defined sub-populations of neurons representing choice, and that choice preference of a particular neuron changes with the details of the task. This work suggests that distributed stimulus selectivity and patterns of functional organization in population codes could be emergent properties of randomly connected networks

Neural criticality from effective latent variables

Observations of power laws in neural activity data have raised the intriguing notion that brains may operate in a critical state. One example of this critical state is "avalanche criticality," which has been observed in a range of systems, including cultured neurons, zebrafish, and human EEG. More recently, power laws have also been observed in neural populations in the mouse under a coarse-graining procedure, and they were explained as a consequence of the neural activity being coupled to multiple latent dynamical variables. An intriguing possibility is that avalanche criticality emerges due to a similar mechanism. Here, we determine the conditions under which dynamical latent variables give rise to avalanche criticality. We find that a single, quasi-static latent variable can generate critical avalanches, but that multiple latent variables lead to critical behavior in a broader parameter range. We identify two regimes of avalanches, both of which are critical, but differ in the amount of information carried about the latent variable. Our results suggest that avalanche criticality arises in neural systems in which there is an emergent dynamical variable or shared inputs creating an effective latent dynamical variable, and when this variable can be inferred from the population activity.Comment: 18 pages, 5 figure

analysis_scripts_and_functions

Processing scripts for state-dependent detection analysis of cortical LFP in awake mouse

csd_allchannels

evoked responses and pre-stimulus activity across all channels (CSD

lfp_dimReduce

evoked responses and pre-stimulus activity across all channels (LFP

State-aware detection of sensory stimuli in the cortex of the awake mouse.

Cortical responses to sensory inputs vary across repeated presentations of identical stimuli, but how this trial-to-trial variability impacts detection of sensory inputs is not fully understood. Using multi-channel local field potential (LFP) recordings in primary somatosensory cortex (S1) of the awake mouse, we optimized a data-driven cortical state classifier to predict single-trial sensory-evoked responses, based on features of the spontaneous, ongoing LFP recorded across cortical layers. Our findings show that, by utilizing an ongoing prediction of the sensory response generated by this state classifier, an ideal observer improves overall detection accuracy and generates robust detection of sensory inputs across various states of ongoing cortical activity in the awake brain, which could have implications for variability in the performance of detection tasks across brain states