Lattice physics approaches for neural networks.

Modern neuroscience has evolved into a frontier field that draws on numerous disciplines, resulting in the flourishing of novel conceptual frames primarily inspired by physics and complex systems science. Contributing in this direction, we recently introduced a mathematical framework to describe the spatiotemporal interactions of systems of neurons using lattice field theory, the reference paradigm for theoretical particle physics. In this note, we provide a concise summary of the basics of the theory, aiming to be intuitive to the interdisciplinary neuroscience community.

Force monitoring reveals single trial dynamics of motor control in a stop signal task.

The Stop Signal Task (SST) has been the benchmark for studying the behavioral and physiological basis of movement generation and inhibition. In our study, we extended the scope beyond physiological findings related to muscle activity, focusing our analysis on the initial biomechanical state of the effector. By incorporating a force sensitive resistor (FSR), we continuously monitored the force applied by the effector (here, the index finger) during a button release version of the SST.

Response inhibition in premotor cortex corresponds to a complex reshuffle of the mesoscopic information network.

Recent studies have explored functional and effective neural networks in animal models; however, the dynamics of information propagation among functional modules under cognitive control remain largely unknown. Here, we addressed the issue using transfer entropy and graph theory methods on mesoscopic neural activities recorded in the dorsal premotor cortex of rhesus monkeys. We focused our study on the decision time of a Stop-signal task, looking for patterns in the network configuration that could influence motor plan maturation when the Stop signal is provided.

Neural Activity in Quarks Language: Lattice Field Theory for a Network of Real Neurons.

Brain-computer interfaces have seen extraordinary surges in developments in recent years, and a significant discrepancy now exists between the abundance of available data and the limited headway made in achieving a unified theoretical framework. This discrepancy becomes particularly pronounced when examining the collective neural activity at the micro and meso scale, where a coherent formalization that adequately describes neural interactions is still lacking. Here, we introduce a mathematical framework to analyze systems of natural neurons and interpret the related empirical observations in terms of lattice field theory, an established paradigm from theoretical particle physics and statistical mechanics.

The transitive inference task to study the neuronal correlates of memory-driven decision making: A monkey neurophysiology perspective.

A vast amount of literature agrees that rank-ordered information as A>B>C>D>E>F is mentally represented in spatially organized schemas after learning. This organization significantly influences the process of decision-making, using the acquired premises, i.e.

Reward prospect affects strategic adjustments in stop signal task.

Interaction with the environment requires us to predict the potential reward that will follow our choices. Rewards could change depending on the context and our behavior adapts accordingly. Previous studies have shown that, depending on reward regimes, actions can be facilitated (i.

Restart errors reaction time of a two-step inhibition process account for the violation of the race model's independence in multi-effector selective stop signal task.

Goal-oriented actions often require the coordinated movement of two or more effectors. Sometimes multi-effector movements need to be adjusted according to a continuously changing environment, requiring stopping an effector without interrupting the movement of the others. This form of control has been investigated by the selective Stop Signal Task (SST), requiring the inhibition of an effector of a multicomponent action.

Neuronal dynamics of signal selective motor plan cancellation in the macaque dorsal premotor cortex.

Primates adopt various strategies to interact with the environment. Yet, no study has examined the effects of behavioural strategies with regard to how movement inhibition is implemented at the neuronal level. We used a modified version of the stop-task by adding an extra signal - termed the Ignore signal - capable of influencing the inhibition of movements only within a specific strategy.

The small scale functional topology of movement control: Hierarchical organization of local activity anticipates movement generation in the premotor cortex of primates.

How neurons coordinate their collective activity for behavioural control is an open question in neuroscience. Several studies have progressively proven, on various scales, that the patterns of neural synchronization change accordingly with behavioural events. However, the topological features of the neural dynamics that underlie task-based cognitive decisions on the small scale level are not understood.

Hierarchical organization of functional connectivity in the mouse brain: a complex network approach.

This paper represents a contribution to the study of the brain functional connectivity from the perspective of complex networks theory. More specifically, we apply graph theoretical analyses to provide evidence of the modular structure of the mouse brain and to shed light on its hierarchical organization. We propose a novel percolation analysis and we apply our approach to the analysis of a resting-state functional MRI data set from 41 mice.