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.
Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing
3 Pith papers cite this work. Polarity classification is still indexing.
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phys-MCP is a substrate-aware orchestration layer that exposes heterogeneous physical neural networks as invocable resources with standardized capability, lifecycle, telemetry, and digital-twin interfaces.
Presents a framework for portable execution of spiking neural networks across von-Neumann HPC and neuromorphic systems via EBRAINS JupyterLab, PyUNICORE, Apptainer containers, and NESTML for model compilation.
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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phys-MCP: A Control Plane for Heterogeneous Physical Neural Networks
phys-MCP is a substrate-aware orchestration layer that exposes heterogeneous physical neural networks as invocable resources with standardized capability, lifecycle, telemetry, and digital-twin interfaces.
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Unifying von-Neumann HPC and Neuromorphic Acceleration via the EBRAINS Research Infrastructure: A Framework for High-Performance Workflows
Presents a framework for portable execution of spiking neural networks across von-Neumann HPC and neuromorphic systems via EBRAINS JupyterLab, PyUNICORE, Apptainer containers, and NESTML for model compilation.