Cortical reliability amid noise and chaos

Abstract

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 nonlinear 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.

Impact of higher-order network structure on emergent cortical activity

“…We have further demonstrated that this leads to changes in the activity patterns of the circuit, specifically a reduced reliability of the population response to thalamic input. It has been shown that reliable responses are linked to increased correlations of synaptic inputs (Nolte et al, 2019; Wang, Spencer, Fellous, & Sejnowski, 2010), and we previously observed that such input correlations can be generated by directed simplex motifs with stronger correlations found in larger simplices (Reimann, Nolte, et al, 2017).…”

“…Spontaneous activity in the NMC-model is variable and chaotic, yet, thalamic stimuli can evoke highly reliable responses (Nolte, Reimann, King, Markram, & Muller, 2019). To study the impact of higher-order structure on this evoked activity, we next stimulated the NMC- and cloud-models with thalamic input (Figure 6A1, see Methods).…”

“…We have previously shown that the in-degree also influences the spike-time reliability ( r spike ) of individual neurons in response to repeated trials of a thalamic stimulus. (Nolte et al, 2019). While there is overall little change in r spike going from NMC- to cloud-model (Supplementary Figure S6ABC), we observed a drop in reliability near the top of layer 5, which also displays a large in-degree reduction in the cloud-model.…”

“…After one second (at t = 0 ms, as we discard the first second) we apply a thalamic stimulus through synapses of 310 VPM fibers that innervate the microcircuit. The stimulus lasts five seconds ( t = 0 to 5 s) and is identical to a previously described stimulus (Nolte et al, 2019; Reimann, Nolte, et al, 2017) based on in vivo thalamic recordings to whisker deflection (Bale, Ince, Santagata, & Petersen, 2015). We simulated 30 trials of the same stimulus in the NMC-model, cloud-model and NMC-model cloud-control at five different [ Ca 2+ ] o concentrations around the in vivo-like state at [ Ca 2+ ] o = 1.25 mM.…”

“…The spike times of each neuron n in each trial k ( K = 30 trials) were convolved with a Gaussian kernel of width σ S = 5 ms to result in filtered signals s ( n, k , ; t ) for each neuron n and each trial k (Δ t S = 0.5 ms ). For each neuron n , the spike-timing reliability is defined as the mean inner product between all pairs of signals divided by their magnitude: Computation of spike-time reliability is identical to a previous study (Nolte et al, 2019).…”