scholarly journals Rise-to-threshold and dynamical systems views of proactive inhibition

2021 ◽  
Author(s):  
Vishal Rawji ◽  
Sachin Modi ◽  
Lorenzo Rocchi ◽  
Marjan Jahanshahi ◽  
John C. Rothwell

AbstractSuccessful models of movement should encompass the flexibility of the human motor system to execute movements under different contexts. One such context-dependent modulation is proactive inhibition, a type of behavioural inhibition concerned with responding with restraint. Whilst movement has classically been modelled as a rise-to-threshold process, there exists a lack of empirical evidence for this in limb movements. Alternatively, the dynamical systems view conceptualises activity during motor preparation as setting the initial state of a dynamical system, that evolves into the movement upon receipt of a trigger. We tested these models by measuring how proactive inhibition influenced movement preparation and execution in humans. We changed the orientation (PA: postero-anterior and AP: antero-posterior flowing currents) and pulse width (120 μs and 30 μs) of motor cortex transcranial magnetic stimulation to probe different corticospinal interneuron circuits. PA and AP interneuron circuits represent the dimensions of a state space upon which motor cortex activity unfolds during motor preparation and execution. AP30 inputs were inhibited at the go cue, regardless of proactive inhibition, whereas PA120 inputs scaled inversely with the probability of successful inhibition. When viewed through a rise-to-threshold model, proactive inhibition was implemented by delaying the trigger to move, suggesting that motor preparation and execution are independent. A dynamical systems perspective showed that proactive inhibition was marked by a shift in the distribution of interneuron networks (trajectories) during movement execution, despite normalisation for reaction time. Viewing data through the rise-to-threshold and dynamical systems models reveal complimentary mechanisms by which proactive inhibition is implemented.Key pointsWe view proactive inhibition through the rise-to-threshold and dynamical systems models.We change the orientation (PA: postero-anterior and AP: antero-posterior flowing currents) and pulse width (120 μs and 30 μs) of transcranial magnetic stimulation to probe interneuron networks in motor cortex during behavioural tasks employing proactive inhibition.When viewed through a rise-to-threshold model, proactive inhibition was implemented by delaying the trigger to move, suggesting that motor preparation and execution are independent.A dynamical systems perspective showed that despite normalisation for reaction time, the trajectory/balance between PA120 and AP30 interneuron inputs during movement execution depended on proactive inhibition.Viewing data through the rise-to-threshold and dynamical systems models reveal complimentary mechanisms by which proactive inhibition is implemented.

NeuroImage ◽  
2011 ◽  
Vol 54 (2) ◽  
pp. 807-823 ◽  
Author(s):  
Srikanth Ryali ◽  
Kaustubh Supekar ◽  
Tianwen Chen ◽  
Vinod Menon

2017 ◽  
Author(s):  
Wayne M. Getz ◽  
Richard Salter ◽  
Oliver Muellerklein ◽  
Hyun S. Yoon ◽  
Krti Tallam

AbstractEpidemiological models are dominated by SEIR (Susceptible, Exposed, Infected and Removed) dynamical systems formulations and their elaborations. These formulations can be continuous or discrete, deterministic or stochastic, or spatially homogeneous or heterogeneous, the latter often embracing a network formulation. Here we review the continuous and discrete deterministic and discrete stochastic formulations of the SEIR dynamical systems models, and we outline how they can be easily and rapidly constructed using the Numerus Model Builder, a graphically-driven coding platform. We also demonstrate how to extend these models to a metapopulation setting using both the Numerus Model Builder network and geographical mapping tools.


2001 ◽  
Vol 24 (1) ◽  
pp. 50-51 ◽  
Author(s):  
Arthur B. Markman

The proposed model is put forward as a template for the dynamical systems approach to embodied cognition. In order to extend this view to cognitive processing in general, however, two limitations must be overcome. First, it must be demonstrated that sensorimotor coordination of the type evident in the A-not-B error is typical of other aspects of cognition. Second, the explanatory utility of dynamical systems models must be clarified.


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