Efficient, simulation-free estimators of firing rates with Markovian surrogates
Spiking neural networks (SNNs) are powerful mathematical models that integrate the biological details of neural systems, but their complexity often makes them computationally expensive and analytically untractable. The firing rate of an SNN is a crucial first-order statistic to characterize network activity. However, estimating firing rates analytically from even simplified SNN models is challenging due to 1) the intricate dependence between the nonlinear network dynamics and parameters, and 2) the singularity and irreversibility of spikes. In this Letter, we propose a class of computationally efficient, simulation-free estimators of firing rates. This is based on a hierarchy of Markovian approximations that reduces the complexity of SNN dynamics. We show that while considering firing rates alone is insufficient for accurate estimations of themselves, the information of spiking synchrony dramatically improves the estimator's accuracy. This approach provides a practical tool for brain modelers, directly mapping biological parameters to firing rate.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Stochastic Wilson-Cowan models of neuronal network dynamics with memory and delay
We consider a simple Markovian class of the stochastic Wilson-Cowan type models of neuronal network dynamics, which incorporates stochastic delay caused by the existence of a refractory period of neurons. From the point …
FEAT: Free energy Estimators with Adaptive Transport
We present Free energy Estimators with Adaptive Transport (FEAT), a novel framework for free energy estimation -- a critical challenge across scientific domains. FEAT leverages learned transports implemented via stochast…
An uncertainty principle for neural coding: Conjugate representations of position and velocity are mapped onto firing rates and co-firing rates of neural spike trains
The hippocampal system contains neural populations that encode an animal's position and velocity as it navigates through space. Here, we show that such populations can embed two codes within their spike trains: a firing …
PositionApproximate Bayesian computation for Markovian binary trees in phylogenetics
Phylogenetic trees describe the relationships between species in the evolutionary process, and provide information about the rates of diversification. To understand the mechanisms behind macroevolution, we consider a cla…
Model-free optimal controller for discrete-time Markovian jump linear systems: A Q-learning approach
This research paper introduces a model-free optimal controller for discrete-time Markovian jump linear systems (MJLSs), employing principles from the methodology of reinforcement learning (RL). While Q-learning methods h…
Q-LearningReinforcement Learning (RL)