Hongdian Yang

Neuronal avalanches imply maximum dynamic range in cortical networks at criticality.

Spontaneous neuronal activity is a ubiquitous feature of cortex. Its spatiotemporal organization reflects past input and modulates future network output. Here we study whether a particular type of spontaneous activity is generated by a network that is optimized for input processing. Neuronal avalanches are a type of spontaneous activity observed in superficial cortical layers in vitro and in vivo with statistical properties expected from a network operating at “criticality.” Theory predicts that criticality and, therefore, neuronal avalanches are optimal for input processing, but until now, this has not been tested in experiments. Here, we use cortex slice cultures grown on planar microelectrode arrays to demonstrate that cortical networks that generate neuronal avalanches benefit from a maximized dynamic range, i.e., the ability to respond to the greatest range of stimuli. By changing the ratio of excitation and inhibition in the cultures, we derive a network tuning curve for stimulus processing as a function of distance from criticality in agreement with predictions from our simulations. Our findings suggest that in the cortex, (1) balanced excitation and inhibition establishes criticality, which maximizes the range of inputs that can be processed, and (2) spontaneous activity and input processing are unified in the context of critical phenomena.

Information Capacity and Transmission Are Maximized in Balanced Cortical Networks with Neuronal Avalanches

The repertoire of neural activity patterns that a cortical network can produce constrains the ability of the network to transfer and process information. Here, we measured activity patterns obtained from multisite local field potential recordings in cortex cultures, urethane-anesthetized rats, and awake macaque monkeys. First, we quantified the information capacity of the pattern repertoire of ongoing and stimulus-evoked activity using Shannon entropy. Next, we quantified the efficacy of information transmission between stimulus and response using mutual information. By systematically changing the ratio of excitation/inhibition (E/I) in vitro and in a network model, we discovered that both information capacity and information transmission are maximized at a particular intermediate E/I, at which ongoing activity emerges as neuronal avalanches. Next, we used our in vitro and model results to correctly predict in vivo information capacity and interactions between neuronal groups during ongoing activity. Close agreement between our experiments and model suggest that neuronal avalanches and peak information capacity arise because of criticality and are general properties of cortical networks with balanced E/I.

Higher-Order Interactions Characterized in Cortical Activity

In the cortex, the interactions among neurons give rise to transient coherent activity patterns that underlie perception, cognition, and action. Recently, it was actively debated whether the most basic interactions, i.e., the pairwise correlations between neurons or groups of neurons, suffice to explain those observed activity patterns. So far, the evidence reported is controversial. Importantly, the overall organization of neuronal interactions and the mechanisms underlying their generation, especially those of high-order interactions, have remained elusive. Here we show that higher-order interactions are required to properly account for cortical dynamics such as ongoing neuronal avalanches in the alert monkey and evoked visual responses in the anesthetized cat. A Gaussian interaction model that utilizes the observed pairwise correlations and event rates and that applies intrinsic thresholding identifies those higher-order interactions correctly, both in cortical local field potentials and spiking activities. This allows for accurate prediction of large neuronal population activities as required, e.g., in brain–machine interface paradigms. Our results demonstrate that higher-order interactions are inherent properties of cortical dynamics and suggest a simple solution to overcome the apparent formidable complexity previously thought to be intrinsic to those interactions.

Maximal Variability of Phase Synchrony in Cortical Networks with Neuronal Avalanches

Ongoing interactions among cortical neurons often manifest as network-level synchrony. Understanding the spatiotemporal dynamics of such spontaneous synchrony is important because it may (1) influence network response to input, (2) shape activity-dependent microcircuit structure, and (3) reveal fundamental network properties, such as an imbalance of excitation (E) and inhibition (I). Here we delineate the spatiotemporal character of spontaneous synchrony in rat cortex slice cultures and a computational model over a range of different E–I conditions including disfacilitated (antagonized AMPA, NMDA receptors), unperturbed, and disinhibited (antagonized GABAA receptors). Local field potential was recorded with multielectrode arrays during spontaneous burst activity. Synchrony among neuronal groups was quantified based on phase-locking among recording sites. As network excitability was increased from low to high, we discovered three phenomena at an intermediate excitability level: (1) onset of synchrony, (2) maximized variability of synchrony, and (3) neuronal avalanches. Our computational model predicted that these three features occur when the network operates near a unique balanced E–I condition called “criticality.” These results were invariant to changes in the measurement spatial extent, spatial resolution, and frequency bands. Our findings indicate that moderate average synchrony, which is required to avoid pathology, occurs over a limited range of E–I conditions and emerges together with maximally variable synchrony. If variable synchrony is detrimental to cortical function, this is a cost paid for moderate average synchrony. However, if variable synchrony is beneficial, then by operating near criticality the cortex may doubly benefit from moderate mean and maximized variability of synchrony.

Origins of choice-related activity in mouse somatosensory cortex

During perceptual decisions about faint or ambiguous sensory stimuli, even identical stimuli can produce different choices. Spike trains from sensory cortex neurons can predict trial-to-trial variability in choice. Choice-related spiking is widely studied as a way to link cortical activity to perception, but its origins remain unclear. Using imaging and electrophysiology, we found that mouse primary somatosensory cortex neurons showed robust choice-related activity during a tactile detection task. Spike trains from primary mechanoreceptive neurons did not predict choices about identical stimuli. Spike trains from thalamic relay neurons showed highly transient, weak choice-related activity. Intracellular recordings in cortex revealed a prolonged choice-related depolarization in most neurons that was not accounted for by feed-forward thalamic input. Top-down axons projecting from secondary to primary somatosensory cortex signaled choice. An intracellular measure of stimulus sensitivity determined which neurons converted choice-related depolarization into spiking. Our results reveal how choice-related spiking emerges across neural circuits and within single neurons.

Sensory and decision-related activity propagate in a cortical feedback loop during touch perception

The brain transforms physical sensory stimuli into meaningful perceptions. In animals making choices about sensory stimuli, neuronal activity in successive cortical stages reflects a progression from sensation to decision. Feedforward and feedback pathways connecting cortical areas are critical for this transformation. However, the computational functions of these pathways are poorly understood because pathway-specific activity has rarely been monitored during a perceptual task. Using cellular-resolution, pathway-specific imaging, we measured neuronal activity across primary (S1) and secondary (S2) somatosensory cortices of mice performing a tactile detection task. S1 encoded the stimulus better than S2, while S2 activity more strongly reflected perceptual choice. S1 neurons projecting to S2 fed forward activity that predicted choice. Activity encoding touch and choice propagated in an S1–S2 loop along feedforward and feedback axons. Our results suggest that sensory inputs converge into a perceptual outcome as feedforward computations are reinforced in a feedback loop.

Universal organization of resting brain activity at the thermodynamic critical point

Thermodynamic criticality describes emergent phenomena in a wide variety of complex systems. In the mammalian cortex, one type of complex dynamics that spontaneously emerges from neuronal interactions has been characterized as neuronal avalanches. Several aspects of neuronal avalanches such as their size and life time distributions are described by power laws with unique exponents, indicating an underlying critical branching process that governs avalanche formation. Here, we show that neuronal avalanches also reflect an organization of brain dynamics close to a thermodynamic critical point. We recorded spontaneous cortical activity in monkeys and humans at rest using high-density intracranial microelectrode arrays and magnetoencephalography, respectively. By numerically changing a control parameter equivalent to thermodynamic temperature, we observed typical critical behavior in cortical activities near the actual physiological condition, including the phase transition of an order parameter, as well as the divergence of susceptibility and specific heat. Finite-size scaling of these quantities allowed us to derive robust critical exponents highly consistent across monkey and humans that uncover a distinct, yet universal organization of brain dynamics. Our results demonstrate that normal brain dynamics at rest resides near or at criticality, which maximizes several aspects of information processing such as input sensitivity and dynamic range.

Scale-Invariant Neuronal Avalanche Dynamics and the Cut-Off in Size Distributions

Identification of cortical dynamics strongly benefits from the simultaneous recording of as many neurons as possible. Yet current technologies provide only incomplete access to the mammalian cortex from which adequate conclusions about dynamics need to be derived. Here, we identify constraints introduced by sub-sampling with a limited number of electrodes, i.e. spatial ‘windowing’, for well-characterized critical dynamics―neuronal avalanches. The local field potential (LFP) was recorded from premotor and prefrontal cortices in two awake macaque monkeys during rest using chronically implanted 96-microelectrode arrays. Negative deflections in the LFP (nLFP) were identified on the full as well as compact sub-regions of the array quantified by the number of electrodes N (10–95), i.e., the window size. Spatiotemporal nLFP clusters organized as neuronal avalanches, i.e., the probability in cluster size, p(s), invariably followed a power law with exponent −1.5 up to N, beyond which p(s) declined more steeply producing a ‘cut-off’ that varied with N and the LFP filter parameters. Clusters of size s≤N consisted mainly of nLFPs from unique, non-repeated cortical sites, emerged from local propagation between nearby sites, and carried spatial information about cluster organization. In contrast, clusters of size s>N were dominated by repeated site activations and carried little spatial information, reflecting greatly distorted sampling conditions. Our findings were confirmed in a neuron-electrode network model. Thus, avalanche analysis needs to be constrained to the size of the observation window to reveal the underlying scale-invariant organization produced by locally unfolding, predominantly feed-forward neuronal cascades.

Multi-electrode Array Recordings of Neuronal Avalanches in Organotypic Cultures

The cortex is spontaneously active, even in the absence of any particular input or motor output. During development, this activity is important for the migration and differentiation of cortex cell types and the formation of neuronal connections1. In the mature animal, ongoing activity reflects the past and the present state of an animal into which sensory stimuli are seamlessly integrated to compute future actions. Thus, a clear understanding of the organization of ongoing i.e. spontaneous activity is a prerequisite to understand cortex function. Numerous recording techniques revealed that ongoing activity in cortex is comprised of many neurons whose individual activities transiently sum to larger events that can be detected in the local field potential (LFP) with extracellular microelectrodes, or in the electroencephalogram (EEG), the magnetoencephalogram (MEG), and the BOLD signal from functional magnetic resonance imaging (fMRI). The LFP is currently the method of choice when studying neuronal population activity with high temporal and spatial resolution at the mesoscopic scale (several thousands of neurons). At the extracellular microelectrode, locally synchronized activities of spatially neighbored neurons result in rapid deflections in the LFP up to several hundreds of microvolts. When using an array of microelectrodes, the organizations of such deflections can be conveniently monitored in space and time. Neuronal avalanches describe the scale-invariant spatiotemporal organization of ongoing neuronal activity in the brain2,3. They are specific to the superficial layers of cortex as established in vitro4,5, in vivo in the anesthetized rat 6, and in the awake monkey7. Importantly, both theoretical and empirical studies2,8-10 suggest that neuronal avalanches indicate an exquisitely balanced critical state dynamics of cortex that optimizes information transfer and information processing. In order to study the mechanisms of neuronal avalanche development, maintenance, and regulation, in vitro preparations are highly beneficial, as they allow for stable recordings of avalanche activity under precisely controlled conditions. The current protocol describes how to study neuronal avalanches in vitro by taking advantage of superficial layer development in organotypic cortex cultures, i.e. slice cultures, grown on planar, integrated microelectrode arrays (MEA; see also 11-14).

Cortical adaptation and tactile perception

Cortical neurons reduce spiking responses to repetitive sensory stimulation, but the perceptual impact of this adaptation has been difficult to assess. Work now shows that it has profound consequences for tactile perception.