Energy-aware RL with a spiking Q-network in a brain circuit model cuts alpha-beta oscillations 45% and stimulation charge 80% vs continuous DBS, then deploys at 0.52 mW on neuromorphic hardware.
Available: https://physoc.onlinelibrary.wiley.com/doi/ abs/10.1113/jphysiol.1952.sp004764
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Information defined as maximum-caliber deviation derives IIT 3.0 cause-effect repertoires from constrained entropy maximization and equates to prediction error under CLT and LDT.
A windowed active set algorithm solves large-scale Lasso for convolutional spike sorting with linear complexity in temporal dimension and parallel efficiency.
A hybrid approach produces a simple circuit implementation of a bursting neuron from phase-locked loop equations.
Further discussion of hyperstatistics foundations with applications to Brownian motion velocity correlations and brain dynamics.
Phenomenological modeling with observables for ion currents, temperature effects, and inductance provides a hybrid framework that brings mathematical descriptions of action potential propagation in axons closer to biological reality.
citing papers explorer
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Neuromorphic Energy-Aware Learning for Adaptive Deep Brain Stimulation
Energy-aware RL with a spiking Q-network in a brain circuit model cuts alpha-beta oscillations 45% and stimulation charge 80% vs continuous DBS, then deploys at 0.52 mW on neuromorphic hardware.
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Information as Maximum-Caliber Deviation: A bridge between Integrated Information Theory and the Free Energy Principle
Information defined as maximum-caliber deviation derives IIT 3.0 cause-effect repertoires from constrained entropy maximization and equates to prediction error under CLT and LDT.
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Large scale Lasso with windowed active set for convolutional spike sorting
A windowed active set algorithm solves large-scale Lasso for convolutional spike sorting with linear complexity in temporal dimension and parallel efficiency.
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Electronic Bursting Neuron: design, equations and hardware implementation
A hybrid approach produces a simple circuit implementation of a bursting neuron from phase-locked loop equations.
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A few remarks on hyperstatistics and some applications
Further discussion of hyperstatistics foundations with applications to Brownian motion velocity correlations and brain dynamics.
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On phenomenology of physical effects in axons
Phenomenological modeling with observables for ion currents, temperature effects, and inductance provides a hybrid framework that brings mathematical descriptions of action potential propagation in axons closer to biological reality.