The premises of a transitive inference task modulate the synchrony of local field potentials in macaque dorsal premoto rcortex

Transitive reasoning is a high-level cognitive ability allowing the prediction of novel knowledge by linking previously acquiredinformation. To study this ability in experimental settings participants are required first to acquire a set of premises for an inferential deduction as A>B, B>C, C>D, D>E, or E>F (Learning phase), and then to identify the ordinal relationship between two items notembedded in the premises as infer that B>E (Test phase). Several brain imaging studies in humans highlighted which cortical and subcortical areas are recruited during the execution of the different steps of this Transitive Inference (TI) task (Xiaoyinget al., 2022). Recently monkey neurophysiology studies approached the investigation of the neuronal computations subtending this ability and observed that the activity of premotor cortex is modulated by the difficulty in comparing items during the test phase. If and how the activity of this area is modulated by the acquisition of the premises of this task is an open question. Here we investigated if this acquisition is reflected in the modulation of the local field potential (LFP)

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. Our methods are tailored to interpret data from chronic neural interfaces, especially spike rasters from measurements of single neuron activity, and generalize the maximum entropy model for neural networks so that the time evolution of the system is also taken into account. This is obtained by bridging particle physics and neuroscience, paving the way for particle physics-inspired models of the neocortex

Neuronal population dynamics and network organizations reflect motivational context in monkey premotor cortex during movement execution and inhibition

Actions require constant updating of response preparation, which may involve suppressing or executing the action depending on context. Many studies have shown that the dorsal premotor cortex (PMd) is a key area for controlling the level of movement preparation. However, it is still unclear how contextual information is integrated to regulate movement preparation. To address this issue, we investigated the neuronal population dynamics and network organization in different motivational contexts. We recorded neuronal activity from PMd of two monkeys (Macaca mulatta), performing a a stop-signal reaching task. The task required to respond to a Go signal as fast as possible (Go trials), and to inhibit the response if an unexpected Stop signal (Stop trial) was presented. Before each trial, a Cue signal indicated in which motivational context (Go+: higher reward for correct Go than Stop trials; Stop+: higher reward for Stop than Go trials; Neutral: same amount for both correct trials) the current trial would run. We used spike density function (SDF) to perform neuronal analysis on well-isolated single unit activity. We extracted the neuronal dynamic by mapping the neuronal population activity in a Low-Dimensional State Space using a Principal Component Analysis (PCA). We also investigated the multiscale network topology by using the node-based multifractal analysis framework (NMFA) and minimal spanning tree analysis (MST). Behavioral results show that in both animals (M1 sessions=2; M2 sessions=5) the motivational context affected motor preparation by lengthening response times (RT) and increasing the ability to inhibit in the Stop+ condition compared to the Go+ condition. Analysis of the neuronal state space showed that in Go trials of the Stop+ and Neutral conditions, the neural trajectories from the Go signal to motion generation evolved similarly, while in the Go+ condition, the trajectories followed a different evolution. The functional network analysis revealed that PMd has a more complex organization when deciding to stop than when deciding to move in the Go+ condition. However, this difference in complexity wasn’t present in the Stop+ and Neutral conditions. The MST identified the topological backbone of the network revealing a network endowed have with hubs present the Go+ condition in both trial types, absent in the other conditions with a more dispersed functional communication between neurons. These results indicate that the motivational context influences the movement preparation in PMd. At neuronal level, these influences can be detected as changes in the neuronal dynamics, as well as in the complexity and topological organization of the network

Functional network complexity reflects diverse reward contexts in macaque premotor cortex

The behavior in the stop signal task-probing inhibitory control-is strongly modulated by the reward context, and this modulation is reflected in neural activity recorded from single sites of the dorsal premotor cortex (PMd) of macaque monkeys [1]. However, how the neural dynamics of motor inhibition is modulated at the network level in different motivational contexts is still unexplored. To tackle the issue, we characterized the synchronization patterns between spiking activities (SUA) recorded using a multielectrode (Utah array, 96 electrodes) and applied graph theory methods to describe and quantify the complexity and heterogeneity of the network structure at different scales when movement is planned or successfully canceled after the presentation of a Stop signal