Theory of Neuronal Perturbome: Linking Connectivity to Coding via Perturbations

Abstract

To unravel the functional properties of the brain, we need to untangle how neurons interact with each other and coordinate in large-scale recurrent networks. One way to address this question is to measure the functional influence of individual neurons on each other by perturbing them in vivo. Application of such single-neuron perturbations in mouse visual cortex has recently revealed feature-specific suppression between excitatory neurons, despite the presence of highly specific excitatory connectivity, which was deemed to underlie feature-specific amplification. Here, we studied which connectivity profiles are consistent with these seemingly contradictory observations, by modelling the effect of single-neuron perturbations in large-scale neuronal networks. Our numerical simulations and mathematical analysis revealed that, contrary to the prima facie assumption, neither inhibition-dominance nor broad inhibition alone were sufficient to explain the experimental findings; instead, strong and functionally specific excitatory-inhibitory connectivity was necessary, consistent with recent findings in the primary visual cortex of rodents. Such networks had a higher capacity to encode and decode natural images in turn, which was accompanied by the emergence of response gain nonlinearities at the population level. Our study provides a general computational framework to investigate how single-neuron perturbations are linked to cortical connectivity and sensory coding, and paves the road to map the perturbome of neuronal networks in future studies. amplification and suppression). We found that to obtain feature-specific suppression, strong and functionally-specific subnetworks of E and I were necessary. That is, both E and I neurons with similar receptive fields (RFs) should be connected together more strongly than their non-similar counterparts, which was consistent with recent results in visual cortex (Znamenskiy et al., 2018).Our modelling results shed light on the above mentioned controversy by showing that featurespecific amplification and suppression could both exist in the cortex, depending on the regime of functional similarity between the influencers and the influencees. Our model suggests specific predictions on how to observe this in the cortex. Computational modelling also helped us to formulate further predictions that experiments could not directly assess, for instance regarding the temporal evolution of functional influence. We linked the result of single-neuron perturbations to sensory processing, by studying how our model networks in different regimes encode and decode natural images. More generally, we show that our theory can be extended to study multiple-cell perturbations to map the perturbome of neuronal networks in future. Results Single-Neuron Perturbations in Large-scale Networks of Visual CortexWe studied the effect of single-neuron perturbations on functional properties of neurons in largescale network models of visual cortex (Figure 1A). Individual excitatory and inhibitory neurons were modelled ...

Learning excitatory-inhibitory neuronal assemblies in recurrent networks

“…To test whether we observe the same feature-specific suppression, we compute the influence of perturbing a Pyr on the rest of the network as a function of the receptive field correlation of the perturbed cell and each measured cell. In line with recent perturbation studies [Chettih and Harvey, 2019, Sadeh and Clopath, 2020], we observe that—on average—neurons are more strongly inhibited if they have a similar tuning to the perturbed neuron (Fig. 4d).…”

“…S6b). This confirms that a “blanket of inhibition” does not account for feature-specific suppression between excitatory neurons [Sadeh and Clopath, 2020].…”

“…This notion is questioned by a recent perturbation experiment demonstrating feature-specific suppression between pyramidal cells with similar tuning [Chettih and Harvey, 2019]. It has been suggested that this apparently paradoxical effect requires strong and tuned connections between excitatory and inhibitory neurons [Sadeh and Clopath, 2020]. The E/I assemblies that develop in our model provide sufficiently strong and specific inhibitory feedback to cause a suppression between similarly tuned Pyr neurons in response to perturbations.…”

“…While on average the network exhibits feature competition, the influence of individual Pyr neurons on the rest of the network is highly variable. According to recent modeling work [Sadeh and Clopath, 2020], the strength of Pyr → PV synapses strongly influences whether a network will exhibit feature competition. In our network, the total out-going weight of a Pyr cell onto the PV neurons indeed predicts the average influence that neuron will have on the rest of the network when perturbed ( Fig.…”

“…For each stimulus, we compute the steady state firing rates r j of all excitatory cells both with and without the perturbation. The influence of the perturbation of neuron i on neuron j is defined as the difference between these two firing rates, normalized by the pertubation magnitude [Sadeh and Clopath, 2020]. This stimulation protocol is repeated for 90 randomly selected excitatory neurons.…”