Modelling brain dynamics by Boolean networks

Abstract

Understanding the relationship between brain architecture and brain function is a central issue in neuroscience. We modeled realistic spatio-temporal patterns of brain activity on a human connectome with a Boolean networks model with the aim of computationally replicating certain cognitive functions as they emerge from the standardization of many fMRI studies, identified as patterns of human brain activity. Results from the analysis of simulation data, carried out for different parameters and initial conditions identified many possible paths in the space of parameters of these network models, with normal (ordered asymptotically constant patterns), chaotic (oscillating or disordered) but also highly organized configurations, with countless spatial–temporal patterns. We interpreted these results as routes to chaos, permanence of the systems in regimes of complexity, and ordered stationary behavior, associating these dynamics to cognitive processes. The most important result of this work is the study of emergent neural circuits, i.e., configurations of areas that synchronize over time, both locally and globally, determining the emergence of computational analogues of cognitive processes, which may or may not be similar to the functioning of biological brain. Furthermore, results put in evidence the creation of how the brain creates structures of remote communication. These structures have hierarchical organization, where each level allows for the emergence of brain organizations which behave at the next superior level. Taken together these results allow the interplay of dynamical and topological roots of the multifaceted brain dynamics to be understood.

Review of the Brain’s Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM)

“…In the past, the brain’s adaptive and beneficial variability in neuronal morphology [ 412 , 413 , 414 , 415 ] has perhaps been addressed with limited statistical tools and frequently from a static perspective [ 416 ]. Stochastic/heuristic approaches are available for this purpose, such as Boolean networks [ 417 ]—wherein Boolean logic junctions act as stochastic surrogates for brain activation dynamics—and, most commonly, agent-based models [ 418 , 419 ]. The latter provide a more flexible framework, where an agent (e.g., a migrating neuron) interacts with its environment, reaching equilibrium points (homeostasis) within complex dynamic settings.…”

A social robot connected with chatGPT to improve cognitive functioning in ASD subjects

“…Neurodevelopmental disorders (NDD) are characterized by deficits in development that impair personal, social, academic, or work functioning. These conditions often have their origins in childhood and persist into adulthood, representing a significant healthcare and cost burden for families and society (American Psychiatric Association, 2013;Buescher et al, 2014;Bertacchini et al, 2021). The most common NDDs include ADHD, ASD, and specific learning disorders (Simmons et al, 2009).…”

“…However, further research is required to determine the effectiveness, feasibility, and acceptability of these technologies within the context of NDDs, and to overcome the associated implementation challenges. In this paper, we present an innovative application of social robotics, utilizing a Pepper robot connected to the OpenAI system (Chat-GPT), to facilitate real-time dialogue and image generation as opposed to the traditional use of dynamic generative systems (Bilotta et al, 2009 , 2010 , 2021 ; Adamo et al, 2010 ; Bertacchini et al, 2010 , 2012 , 2013 , 2022a , b , c , 2023 ; Gabriele et al, 2017 ). By combining these technologies, we aim to enhance the interaction experience and promote communication in individuals with Autism Spectrum Disorder (ASD), particularly those with low-medium functioning capabilities.…”