Dynamic representations in networked neural systems
A group of neurons can generate patterns of activity that represent information about stimuli;
subsequently, the group can transform and transmit activity patterns across synapses to …
subsequently, the group can transform and transmit activity patterns across synapses to …
Brain modeling for control: A review
Neurostimulation technologies have seen a recent surge in interest from the neuroscience
and controls communities alike due to their proven potential to treat conditions such as …
and controls communities alike due to their proven potential to treat conditions such as …
Models of communication and control for brain networks: distinctions, convergence, and future outlook
Recent advances in computational models of signal propagation and routing in the human
brain have underscored the critical role of white-matter structure. A complementary …
brain have underscored the critical role of white-matter structure. A complementary …
Adaptive internal models in neuroscience
ME Broucke - Foundations and Trends® in Systems and …, 2022 - nowpublishers.com
This monograph examines in mathematical terms an open question in neuroscience on the
function of the cerebellum, a major brain region involved in regulation of the motor systems …
function of the cerebellum, a major brain region involved in regulation of the motor systems …
Non-Euclidean contractivity of recurrent neural networks
Critical questions in dynamical neuroscience and machine learning are related to the study
of recurrent neural networks and their stability, robustness, and computational efficiency …
of recurrent neural networks and their stability, robustness, and computational efficiency …
Hierarchical selective recruitment in linear-threshold brain networks—Part I: Single-layer dynamics and selective inhibition
Goal-driven selective attention (GDSA) refers to the brain's function of prioritizing the activity
of a task-relevant subset of its overall network to efficiently process relevant information …
of a task-relevant subset of its overall network to efficiently process relevant information …
Optimal network interventions to control the spreading of oscillations
Oscillations are a prominent feature of neuronal activity and are associated with a variety of
phenomena in brain tissue, both healthy and unhealthy. Characterizing how oscillations …
phenomena in brain tissue, both healthy and unhealthy. Characterizing how oscillations …
Contraction analysis of hopfield neural networks with hebbian learning
Motivated by advances in neuroscience and machine learning, this paper is concerned with
the modeling and analysis of Hopfield neural networks with dynamic recurrent connections …
the modeling and analysis of Hopfield neural networks with dynamic recurrent connections …
Non-Euclidean contraction analysis of continuous-time neural networks
Critical questions in dynamical neuroscience and machine learning are related to the study
of continuous-time neural networks and their stability, robustness, and computational …
of continuous-time neural networks and their stability, robustness, and computational …
Diversity of emergent dynamics in competitive threshold-linear networks
Threshold-linear networks consist of simple units interacting in the presence of a threshold
nonlinearity. Competitive threshold-linear networks have long been known to exhibit …
nonlinearity. Competitive threshold-linear networks have long been known to exhibit …