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Untangling emergent cortical dynamics: neurons from networks, noise from chaos

Max Christian Nolte

The way in which cortical microcircuit components -- most importantly neurons -- and their connectivity -- the network -- shape and constrain emergent dynamics is a long-standing question in neuroscience. Experimentally observed dynamical properties can often be explained by circuit models with different simplifying assumptions for the underlying neuron models and their network structure, such as deterministic synapse models describing stochastic synapses, or a uniform network structure describing heterogeneous synaptic connectivity. Intrinsic neural variability, for example, can emerge from both stochastic synaptic properties (noise) and deterministic network dynamics (chaos). It is therefore often not clear if models with ad hoc simplifying assumptions for various biological details provide correct explanations for the emergence of cortical dynamics. In this thesis, we set out to advance our understanding of how detailed biological properties of cortical neurons and their network structure shape emergent dynamics by studying a model of a prototypical neocortical microcircuit that was reconstructed using all relevant available biological data. To make our predictions as biologically accurate as possible, we used a "zero tweak" strategy wherein parameters in the model were not adjusted to replicate specific experimentally observed dynamical properties, but instead were constrained by biological data. This allowed us to characterize the effect of two biological properties that are often abstracted away in ad hoc simplifications: stochastic synaptic transmission and a heterogeneous network structure with complex higher-order connectivity. Studying the model, we made several important predictions: (1) Stochastic synaptic transmission, in an interplay with recurrent network dynamics, causes rapid chaotic divergence of spontaneous activity. (2) Synaptic noise overshadows other local cellular noise sources. (3) Amid the noise and chaos, neurons can reliably respond to external inputs with millisecond spike-time precision. (4) This reliable response goes beyond mere feedforward suppression of recurrent dynamics and is driven by the circuit at a near-critical excitation-inhibition balance. (5) An abundance of high-dimensional cliques of all-to-all connected neurons which shape correlations between neurons in a hierarchical manner. (6) This effect is strongly reduced when synaptic connectivity is replaced by a rejected null model with reduced higher-order network structure. We conclude that a detailed representation of cellular noise sources and high-dimensional network structure is imperative to accurately model emergent cortical network dynamics. Models that make ad hoc simplifying assumptions need to carefully justify the exclusion of such details.

EPFL2019

Cortical reliability amid noise and chaos

Henry Markram, Eilif Benjamin Muller, Michael Reimann, James Gonzalo King, Max Christian Nolte

Typical responses of cortical neurons to identical sensory stimuli appear highly variable. It has thus been proposed that the cortex primarily uses a rate code. However, other studies have argued for spike-time coding under certain conditions. The potential role of spike-time coding is directly limited by the internally generated variability of cortical circuits, which remains largely unexplored. Here, we quantify this internally generated variability using a biophysical model of rat neocortical microcircuitry with biologically realistic noise sources. We find that stochastic neurotransmitter release is a critical component of internally generated variability, causing rapidly diverging, chaotic recurrent network dynamics. Surprisingly, the same non-linear recurrent network dynamics can transiently overcome the chaos in response to weak feed-forward thalamocortical inputs, and support reliable spike times with millisecond precision. Our model shows that the noisy and chaotic network dynamics of recurrent cortical microcircuitry are compatible with stimulus-evoked, millisecond spike-time reliability, resolving a long-standing debate.

2019

Generating and identifying functional subnetworks within structural networks

Henry Markram, Kathryn Hess Bellwald, Michael Reimann, Ran Levi, Max Christian Nolte

In one aspect, a method includes generating a functional subgraph of a network from a structural graph of the network. The structural graph comprises a set of vertices and structural connections between the vertices. Generating the functional subgraph includes identifying a directed functional edge of the functional subgraph based on presence of structural connection and directional communication of information across the same structural connection.

2018