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Found 7 papers, 1 papers with code
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 Parkinson's Disease, and depression.
Towards a Network Control Theory of Electroconvulsive Therapy Response
We show that whole-brain controllability metrics based on pre-ECT structural connectome data predict ECT response in accordance with our hypotheses.
Structural Characterization of Oscillations in Brain Networks with Rate Dynamics
Exploiting the switched-affine nature of this dynamics, we obtain various necessary and/or sufficient conditions on the network structure and its external input for the existence of oscillations in (i) two-dimensional excitatory-inhibitory networks (E-I pairs), (ii) networks with one inhibitory but arbitrary number of excitatory nodes, (iii) purely inhibitory networks with an arbitrary number of nodes, and (iv) networks of E-I pairs.
Is the brain macroscopically linear? A system identification of resting state dynamics
Contrary to our expectations, linear auto-regressive models achieve the best measures across all three metrics, eliminating the trade-off between accuracy and simplicity.
Teaching Recurrent Neural Networks to Modify Chaotic Memories by Example
Here we demonstrate that a recurrent neural network (RNN) can learn to modify its representation of complex information using only examples, and we explain the associated learning mechanism with new theory.
Hierarchical Selective Recruitment in Linear-Threshold Brain Networks, Part II: Multi-Layer Dynamics and Top-Down Recruitment
Goal-driven selective attention (GDSA) is a remarkable function that allows the complex dynamical networks of the brain to support coherent perception and cognition.
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 while inhibiting the effects of distractions.
