Action-stopping is a canonical executive function thought to involve top-down control over the motor system. Here we aimed to validate this stopping system using high temporal resolution methods in humans. We show that, following the requirement to stop, there was an increase of right frontal beta (~13 to 30 Hz) at ~120 ms, likely a proxy of right inferior frontal gyrus; then, at 140 ms, there was a broad skeletomotor suppression, likely reflecting the impact of the subthalamic nucleus on basal ganglia output; then, at ~160 ms, suppression was detected in the muscle, and, finally, the behavioral time of stopping was ~220 ms. This temporal cascade supports a physiological model of action-stopping, and partitions it into subprocesses that are isolable to different nodes and are more precise than the behavioral latency of stopping. Variation in these subprocesses, including at the single-trial level, could better explain individual differences in impulse control.

15Action-stopping is a canonical executive function thought to involve top-down control 16 over the motor system. Here we aimed to validate this stopping system using high temporal 17 resolution methods in humans. We show that, following the requirement to stop, there was an 18 increase of right frontal beta (~13 to 30 Hz) at ~120 ms, likely a proxy of right inferior frontal 19 gyrus; then, at 140 ms, there was a broad skeletomotor suppression, likely reflecting the impact 20 of the subthalamic nucleus on basal ganglia output; then, at ~160 ms, suppression was detected 21 in the muscle, and, finally, the behavioral time of stopping was ~220 ms. This temporal cascade 22 confirms a detailed model of action-stopping, and partitions it into subprocesses that are isolable 23 to different nodes and are more precise than the behavioral speed of stopping. Variation in these 24 subprocesses, including at the single-trial level, could better explain individual differences in 25 impulse control. 26 27 28The ability to control one's actions and thoughts is important for our daily lives; for 29 example: changing gait when there is an obstacle in the path 1 , resisting the temptation to eat 30 when on a diet 2 , overcoming the tendency to say something hurtful 3 . While many processes 31 contribute to such forms of control, one important process is response inhibition -the prefrontal 32 (top-down) stopping of initiated response tendencies 4 . In the laboratory, response inhibition is 33 often studied with the stop-signal task 5 . On each trial, the participant initiates a motor response, 34 and then, when a subsequent Stop signal occurs, tries to stop. From the behavioral data one can 35 estimate a latent variable; the speed of stopping known as Stop Signal Reaction Time (SSRT), 36 which is typically 200-250 ms in healthy adults 5 . SSRT has been useful in neuropsychiatry 37where it is often longer for patients vs. controls [6][7][8][9][10][11] . The task has also provided a rich test-bed, 38 3 across species, for mapping out a putative neural architecture of prefrontal-basal-ganglia-regions 39 for rapidly suppressing motor output areas 6,12,13 . Given this rich literature, this task is one of the 40 few paradigms included in the longitudinal Adolescent Brain Cognitive Development study 14 of 41 10,000 adolescents over 10 years. 42Against this background, a puzzle is that the relation between SSRT and 'real-world' 43 self-reported impulsivity is often weak 15-20 . One explanation is that SSRT may not accurately 44 index the brain's true stopping speed. Indeed, recent mathematical modelling of behavior during 45 the stop-signal task suggests that standard calculations of SSRT may overestimate the brain's 46 stopping speed by ~100 ms 15 [also see 21 ]. Further, in a recent study 22 , electromyographic (EMG) 47 recordings revealed an initial increase in EMG activity in response to the Go cue, followed by a 48 sudden decline at ~150 ms after the Stop signal. This decline in EMG could be because of the 49 Stop process 'kicking in'...

Human action-stopping is thought to rely on a prefronto-basal ganglia-thalamocortical network, with right inferior frontal cortex (rIFC) posited to play a critical role in the early stage of implementation. Here we sought causal evidence for this idea in experiments involving healthy human participants. We first show that action-stopping is preceded by bursts of electroencephalographic activity in the beta band over prefrontal electrodes, putatively rIFC, and that the timing of these bursts correlates with the latency of stopping at a single-trial level: earlier bursts are associated with faster stopping. From this we reasoned that the integrity of rIFC at the time of beta bursts might be critical to successful stopping. We then used fMRI-guided transcranial magnetic stimulation (TMS) to disrupt rIFC at the approximate time of beta bursting. Stimulation prolonged stopping latencies and, moreover, the prolongation was most pronounced in individuals for whom the pulse appeared closer to the presumed time of beta bursting. These results help validate a model of the neural architecture and temporal dynamics of action-stopping. They also highlight the usefulness of prefrontal beta bursts to index an apparently important sub-process of stopping, the timing of which might help explain within- and between-individual variation in impulse control.

We present a computational model of altered gait velocity patterns in Parkinson's Disease (PD) patients. PD gait is characterized by short shuffling steps, reduced walking speed, increased double support time and sometimes increased cadence. The most debilitating symptom of PD gait is the context dependent cessation in gait known as freezing of gait (FOG). Cowie et al. (2010) and Almeida and Lebold (2010) investigated FOG as the changes in velocity profiles of PD gait, as patients walked through a doorway with variable width. The former reported a sharp dip in velocity, a short distance from the doorway that was greater for narrower doorways. They compared the gait performance in PD freezers at ON and OFF dopaminergic medication. In keeping with this finding, the latter also reported the same for ON medicated PD freezers and non-freezers. In the current study, we sought to simulate these gait changes using a computational model of Basal Ganglia based on Reinforcement Learning, coupled with a spinal rhythm mimicking central pattern generator (CPG) model. In the model, a simulated agent was trained to learn a value profile over a corridor leading to the doorway by repeatedly attempting to pass through the doorway. Temporal difference error in value, associated with dopamine signal, was appropriately constrained in order to reflect the dopamine-deficient conditions of PD. Simulated gait under PD conditions exhibited a sharp dip in velocity close to the doorway, with PD OFF freezers showing the largest decrease in velocity compared to PD ON freezers and controls. PD ON and PD OFF freezers both showed sensitivity to the doorway width, with narrow door producing the least velocity/ stride length. Step length variations were also captured with PD freezers producing smaller steps and larger step-variability than PD non-freezers and controls. In addition this model is the first to explain the non-dopamine dependence for FOG giving rise to several other possibilities for its etiology.

Spatial cells in the hippocampal complex play a pivotal role in the navigation of an animal. Exact neural principles behind these spatial cell responses have not been completely unraveled yet. Here we present two models for spatial cells, namely the Velocity Driven Oscillatory Network (VDON) and Locomotor Driven Oscillatory Network. Both models have basically three stages in common such as direction encoding stage, path integration (PI) stage, and a stage of unsupervised learning of PI values. In the first model, the following three stages are implemented: head direction layer, frequency modulation by a layer of oscillatory neurons, and an unsupervised stage that extracts the principal components from the oscillator outputs. In the second model, a refined version of the first model, the stages are extraction of velocity representation from the locomotor input, frequency modulation by a layer of oscillators, and two cascaded unsupervised stages consisting of the lateral anti-hebbian network. The principal component stage of VDON exhibits grid cell-like spatially periodic responses including hexagonal firing fields. Locomotor Driven Oscillatory Network shows the emergence of spatially periodic grid cells and periodically active border-like cells in its lower layer; place cell responses are found in its higher layer. This model shows the inheritance of phase precession from grid cell to place cell in both one- and two-dimensional spaces. It also shows a novel result on the influence of locomotion rhythms on the grid cell activity. The study thus presents a comprehensive, unifying hierarchical model for hippocampal spatial cells.