Adaptation to sensory input tunes visual cortex to criticality

A long-standing hypothesis at the interface of physics and neuroscience is that neural networks self-organize to the critical point of a phase transition, thereby optimizing aspects of sensory information processing1–3. This idea is partially supported by strong evidence for critical dynamics observed in the cerebral cortex4–10, but the impact of sensory input on these dynamics is largely unknown. Thus, the foundations of this hypothesis—the self-organization process and how it manifests during strong sensory input—remain unstudied experimentally. Here we show in visual cortex and in a computational model that strong sensory input initially elicits cortical network dynamics that are not critical, but adaptive changes in the network rapidly tune the system to criticality. This conclusion is based on observations of multifaceted scaling laws predicted to occur at criticality4,11. Our findings establish sensory adaptation as a self-organizing mechanism that maintains criticality in visual cortex during sensory information processing. Sensory nervous systems adapt, dynamically tuning interactions among large networks of neurons, to cope with a changing environment12,13. The principles governing such adaptation at the macroscopic level of neuronal network dynamics are not well understood. Computational models and theory suggest that such adaptation can maintain critical network dynamics14–16, but these previous studies did not consider the strongly driven regime that is expected during intense sensory input. Indeed, sufficiently strong input may increase the overall excitability of a network by bringing neurons closer to their firing thresholds and potentially tipping the network into a high firing rate regime that is inconsistent with critical dynamics (Supplementary Information 1). Thus, the question remains: does strong sensory input drive cortical network dynamics away from criticality or can adaptation counteract this tendency and maintain the critical regime? Here we addressed this question in turtle visual cortex and in a companion computational model. In our experiments, we obtained long-duration recordings of population neural activity (local field potential, LFP) using a microelectrode array inserted into the geniculo-recipient dorsal cortex (visual cortex) of the turtle eyeattached whole-brain ex vivo preparation17 (Fig. 1a and Supplementary Information 2). We measured multi-scale spatiotemporal patterns of neural activity while visually stimulating the retina. Similarly, in our model we studied changes in neural network activity in response to changes in external input. Experimentally, and in the model, we assessedwhether themeasured dynamics were near or far from criticality. For this, we examined statistics and spatiotemporal scaling laws of ‘neuronal avalanches’, which are bouts of elevated population activity with correlations in space and time5 (Fig. 1b). In brief, a neuronal avalanche is defined as a group of LFP peaks, occurring on any electrode, irrespective of location, and separated by inter-peak intervals less than a specified time (Methods). For experiments in which spikes (that is, multiunit activity) were also measurable, we confirmed that the rate of LFP peaks increases with the rate of spikes (Supplementary Information 3). Thus, a period of time with many LFP peaks—for example, a neuronal avalanche— reflects an increase in population spike rate in the cortex. At the onset of stimulation, we observed that LFP amplitude, LFP peak rate, and avalanches were typically large scale—not consistent with critical dynamics—during a transient period (Fig. 1c–e and Supplementary Information 4). More specifically, avalanche sizes S and durations D were often bimodally distributed during the transient (Fig. 1f,g and Supplementary Information 5). Following this large-scale transient response, LFP amplitude decreased and avalanches becamemore diverse in spatiotemporal scale (Fig. 1e), resulting in power-law distributions, P(S)∼ S (Fig. 1f) and P(D)∼D (Fig. 1g) over a wide range of sizes and durations. This fact is supported by rigorousmaximum likelihood fittingmethods10,18 and strict statistical criteria for fit quality (q>0.1, Methods). These conclusions held for nine turtles and four types of visual stimuli (n=13 data sets; complex movies, static grey screen, diffuse flashes, moving dots) with power-law quality values q=0.31±0.13 (mean ± s.d.). Importantly, the different visual stimuli had very different spatiotemporal structure, yet all resulted in power-law avalanche distributions. This indicates that the power laws were due to inherent neuronal network dynamics rather than externally imposed statistics of the stimulus. Notably, randomizing the recorded LFP peak times abolished the power-law distributions of avalanche size and duration, thus demonstrating the importance of correlations, (Fig. 1f,g). Moreover, activity recorded outside visual cortex was not power-law distributed (Supplementary Information 6). What biophysical mechanisms could mediate self-organization towards scale-free population activity during visual processing? To address this question, we investigated a parsimonious model network of probabilistic integrate-and-fire neurons with all-to-all connectivity6,19,20 (Fig. 2a). A subset of neurons (20%) was inhibitory. Motivated by previous experiments21 and models14, we modelled adaptation as short-term synaptic depression with recovery (Methods). However, our model differed from previously studied models, as detailed in Supplementary Information 7. We studied how the model dynamics and avalanche statistics change as a result of increasing input rate. During a transient period after increasing the input rate, the population spike rate increased and synapses depressed (Fig. 2b,c). During the transient, avalanches also increased markedly in size and duration (Fig. 2d), qualitatively similar to the experimental observations (Fig. 1e).