Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation

Abstract

It has been hypothesized that the brain optimizes its capacity for computation by self-organizing to a critical point. The dynamical state of criticality is achieved by striking a balance such that activity can effectively spread through the network without overwhelming it and is commonly identified in neuronal networks by observing the behavior of cascades of network activity termed “neuronal avalanches.” The dynamic activity that occurs in neuronal networks is closely intertwined with how the elements of the network are connected and how they influence each other's functional activity. In this review, we highlight how studying criticality with a broad perspective that integrates concepts from physics, experimental and theoretical neuroscience, and computer science can provide a greater understanding of the mechanisms that drive networks to criticality and how their disruption may manifest in different disorders. First, integrating graph theory into experimental studies on criticality, as is becoming more common in theoretical and modeling studies, would provide insight into the kinds of network structures that support criticality in networks of biological neurons. Furthermore, plasticity mechanisms play a crucial role in shaping these neural structures, both in terms of homeostatic maintenance and learning. Both network structures and plasticity have been studied fairly extensively in theoretical models, but much work remains to bridge the gap between theoretical and experimental findings. Finally, information theoretical approaches can tie in more concrete evidence of a network's computational capabilities. Approaching neural dynamics with all these facets in mind has the potential to provide a greater understanding of what goes wrong in neural disorders. Criticality analysis therefore holds potential to identify disruptions to healthy dynamics, granted that robust methods and approaches are considered.

Criticality-Driven Evolution of Adaptable Morphologies of Voxel-Based Soft-Robots

“…The bottom row shows the avalanche size and duration. Image adapted from (Heiney et al, 2021) in the context of biological neural networks on Micro-Electrode Arrays.…”

“…When the avalanche distribution follows a power law, the system is said to be at criticality. A recent review on criticality and connectivity in relation to neural computation is available at (Heiney et al, 2021).…”

Dynamics of Long-Range Temporal Correlations in Broadband EEG During Different Motor Execution and Imagery Tasks

“…Neural activity has been reported to produce long-range interactions leading to power-law scaling, suggesting that these neuronal processes are similar across different scales (Kello et al, 2010;Berthouze and Farmer, 2012;Heiney et al, 2021). The power-law scaling is observed in several cases of neuronal recordings such as neuronal firings (Hu et al, 2013), neuronal avalanches (Benayoun et al, 2010;Palva et al, 2013), intracranial recordings such as local field potentials (Benayoun et al, 2010), and electrocorticography (Chaudhary et al, 2017), and non-invasive scalp recordings of EEG and magnetoencephalography (Nikulin and Brismar, 2005;Benayoun et al, 2010;Kwok et al, 2019;Jannesari et al, 2020) in both oscillatory and non-oscillatory processes.…”

Computational annealing as mechanism in eye movement desensitization and reprocessing (EMDR)

“…By maintaining a balance between stability and instability -order and disorder -nerve networks are thought to achieve a greater flexibility in responding to both inner conditions and environmental demands. This balance has been identified in the brain and termed, criticality (Heiney, et al, 2021;Plenz & Thiagarajan, 2007;Yu et al, 2013). Criticality is thought to be a general operational principle of healthy cortical functioning (Hesse & Gross, 2014).…”