6 research outputs found
Stability of stochastic finite-size spiking-neuron networks: Comparing mean-field, 1-loop correction and quasi-renewal approximations
We examine the stability and qualitative dynamics of stochastic neuronal
networks specified as multivariate nonlinear Hawkes processes and related
point-process generalized linear models that incorporate both auto- and
cross-history effects. In particular, we adapt previous theoretical
approximations based on mean field and mean field plus 1-loop correction to
incorporate absolute refractory periods and other auto-history effects.
Furthermore, we extend previous quasi-renewal approximations to the
multivariate case, i.e. neuronal networks. The best sensitivity and specificity
performance, in terms of predicting stability and divergence to
nonphysiologically high firing rates in the examined simulations, was obtained
by a variant of the quasi-renewal approximation.Comment: 8 pages. This errata note corrects typos and clarifies aspects
related to equations presented in Proc IEEE EMBC (2019), pp. 4380 - 4386, doi
10.1109/EMBC.2019.8857101. A corrected version of the published article is
included, followed by the detailed errata list and correction
The Functional Role of Striatal Cholinergic Interneurons in Reinforcement Learning From Computational Perspective
In this study, we explore the functional role of striatal cholinergic interneurons, hereinafter referred to as tonically active neurons (TANs), via computational modeling; specifically, we investigate the mechanistic relationship between TAN activity and dopamine variations and how changes in this relationship affect reinforcement learning in the striatum. TANs pause their tonic firing activity after excitatory stimuli from thalamic and cortical neurons in response to a sensory event or reward information. During the pause striatal dopamine concentration excursions are observed. However, functional interactions between the TAN pause and striatal dopamine release are poorly understood. Here we propose a TAN activity-dopamine relationship model and demonstrate that the TAN pause is likely a time window to gate phasic dopamine release and dopamine variations reciprocally modulate the TAN pause duration. Furthermore, this model is integrated into our previously published model of reward-based motor adaptation to demonstrate how phasic dopamine release is gated by the TAN pause to deliver reward information for reinforcement learning in a timely manner. We also show how TAN-dopamine interactions are affected by striatal dopamine deficiency to produce poor performance of motor adaptation
26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017
This work was produced as part of the activities of FAPESP Research,\ud
Disseminations and Innovation Center for Neuromathematics (grant\ud
2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud
FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud
supported by a CNPq fellowship (grant 306251/2014-0)
The interplay between cerebellum and basal ganglia in motor adaptation: A modeling study
Motor adaptation to perturbations is provided by learning mechanisms operating in the cerebellum and basal ganglia. The cerebellum normally performs motor adaptation through supervised learning using information about movement error provided by visual feedback. However, if visual feedback is critically distorted, the system may disengage cerebellar error-based learning and switch to reinforcement learning mechanisms mediated by basal ganglia. Yet, the exact conditions and mechanisms of cerebellum and basal ganglia involvement in motor adaptation remain unknown. We use mathematical modeling to simulate control of planar reaching movements that relies on both error-based and non-error-based learning mechanisms. We show that for learning to be efficient only one of these mechanisms should be active at a time. We suggest that switching between the mechanisms is provided by a special circuit that effectively suppresses the learning process in one structure and enables it in the other. To do so, this circuit modulates learning rate in the cerebellum and dopamine release in basal ganglia depending on error-based learning efficiency. We use the model to explain and interpret experimental data on error- and non-error-based motor adaptation under different conditions
The interplay between cerebellum and basal ganglia in motor adaptation: A modeling study.
Motor adaptation to perturbations is provided by learning mechanisms operating in the cerebellum and basal ganglia. The cerebellum normally performs motor adaptation through supervised learning using information about movement error provided by visual feedback. However, if visual feedback is critically distorted, the system may disengage cerebellar error-based learning and switch to reinforcement learning mechanisms mediated by basal ganglia. Yet, the exact conditions and mechanisms of cerebellum and basal ganglia involvement in motor adaptation remain unknown. We use mathematical modeling to simulate control of planar reaching movements that relies on both error-based and non-error-based learning mechanisms. We show that for learning to be efficient only one of these mechanisms should be active at a time. We suggest that switching between the mechanisms is provided by a special circuit that effectively suppresses the learning process in one structure and enables it in the other. To do so, this circuit modulates learning rate in the cerebellum and dopamine release in basal ganglia depending on error-based learning efficiency. We use the model to explain and interpret experimental data on error- and non-error-based motor adaptation under different conditions