Network dynamics underlying OFF responses in the auditory cortex

Abstract

Across sensory systems, complex spatio-temporal patterns of neural activity arise following the onset (ON) and offset (OFF) of simple stimuli. While ON responses have been widely studied, the mechanisms generating OFF responses in cortical areas have so far not been fully elucidated. Recent studies have argued that OFF responses reflect strongly transient sensory coding at the population level, suggesting they may be generated by a collective, network mechanism. We examine here the hypothesis that OFF responses are single-cell signatures of network dynamics and propose a simple model that generates transient OFF responses through recurrent interactions. To test this hypothesis, we analyse the responses of large populations of neurons in auditory cortex recorded using two-photon calcium imaging in awake mice passively listening to auditory stimuli. Adopting a population approach, we find that the OFF responses evoked by individual stimuli define trajectories of activity that evolve in low-dimensional neural subspaces. A geometric analysis of the population OFF responses reveals that, across different stimuli, these neural subspaces lie on largely orthogonal dimensions that define low-dimensional transient coding channels. We show that a linear network model with recurrent interactions provides a simple mechanism for the generation of the strong OFF responses observed in auditory cortex, and accounts for the low-dimensionality of the transient channels and their global structure across stimuli. We finally compare the network model with a single-cell mechanism and show that the single-cell model, while explaining some prominent features of the data, does not account for the structure across stimuli captured by the network model.

Geometry of inter-areal interactions in mouse visual cortex

“…Directly computing the subspace angles between did not affect our conclusions (Supplement: Computation of subspace angles). Subspace angles have been used in [41] to show that OFF responses in auditory cortex to different stimuli lie mostly in orthogonal subspaces.…”

An evolving perspective on the dynamic brain: Notes from the Brain Conference on Dynamics of the brain: Temporal aspects of computation

“…In a typical linear dynamical system, responses will decay away monotonically in the absence of a stimulus, making them poor candidates for coding with transients. Building on ideas from a class of linear systems called “non‐normal,” Bondanelli presented a framework for encoding multiple stimuli in strongly amplified transient trajectories by choosing the connectivity matrix to be the sum of appropriate low‐rank pieces (Bondanelli & Ostojic, 2020), and showed that it could explain various observed features of auditory cortical neural data, such as non‐monotonic transient activity at stimulus offset and better discriminability during the offset transient phase (Bondanelli, Deneux, Bathellier, & Ostojic, 2019).…”

Interrogating theoretical models of neural computation with emergent property inference

“…To understand how EPI scales in comparison to existing techniques, we consider recurrent neural networks (RNNs). Transient amplification is a hallmark of neural activity throughout cortex, and is often thought to be intrinsically generated by recurrent connectivity in the responding cortical area [43][44][45]. It has been shown that to generate such amplified, yet stabilized responses, the connectivity of RNNs must be non-normal [43,50], and satisfy additional constraints [51].…”

“…First, we show EPI's ability to handle biologically realistic circuit models using a five-neuron model of the stomatogastric ganglion [41]: a neural circuit whose parametric degeneracy is closely studied [42]. Then, we show EPI's scalability to high dimensional parameter distributions by inferring connectivities of recurrent neural networks that exhibit stable, yet amplified responses -a hallmark of neural responses throughout the brain [43][44][45]. In a model of primary visual cortex [46,47], EPI reveals how the recurrent processing across different neuron-type populations shapes excitatory variability: a finding that we show is analytically intractable.…”