Open Source
Neuromodulation Through Intrinsic Noise
arXiv eess.SY0 views
arXiv:2611.13780v1 Announce Type: cross Abstract: Neuromodulation, the modulation of excitability through the regulation of neuronal parameters, is a key tool in neuromorphic control design. This paper investigates how spiking excitability can be modulated by intrinsic noise. Focusing on a simplified memristive neuron model, we show that intrinsic noise plays distinct functional roles depending on the feedback pathways: Intrinsic noise in the fast excitatory feedback shapes the nature of excitability and modulates the sensitivity of the neuron to external/internal stimuli, whereas that in the slow inhibitory feedback provides internal stimuli that can trigger stochastic spiking. We expect these findings to guide the design of neuromorphic systems that harness intrinsic noise to exploit beneficial stochastic phenomena, such as stochastic resonance.
Source:https://arxiv.org/abs/2611.13780PreviousFAME-QP: A GPU-Accelerated Quadratic-Programming Solver for Real-Time Model Predictive ControlNextOptimal Last-Iterate Bounds for Nonexpansive Stochastic Approximation
Related posts

Reverse Engineering Tools: A Free Starter Kit for Your First Binary
0dev.to
Software Refactoring Research with Large Language Models: A Systematic Literature Review
0arXiv cs.SE
Antalia-2 Mini: A 7.6-million-parameter Turkish text-to-speech model trained entirely on synthetic speech
0arXiv cs.SD
Emilia2: A Large-Scale Dataset for Controllable Long-Form Speech Generation
0arXiv cs.SD
Asymptotically good codes from Siegel modular varieties
0arXiv math.IT
On the Concavity of R\'enyi Entropy and R\'enyi Entropy Power along Heat Flows on Manifolds
0arXiv math.IT