A game-theoretic description for a canonical, convergent neural circuit with general STDP and postsynaptic-potential (PSP) kernels that implements contrastive PCA and flow selection.
Abstract
A general framework for inferring the computational role of spike-timing-dependent plasticity (STDP) does not currently exist. Here, we develop a game-theoretic description for a canonical, convergent neural circuit with general STDP and postsynaptic-potential (PSP) kernels. Parity-matched STDP-PSP interactions induce a potential game among presynaptic neurons, whereas parity-mismatched interactions induce a zero-sum game. These components, respectively, implement contrastive PCA and flow selection; their weighted interplay determines learning dynamics and computation for arbitrary STDP rules.
A finite stochastic spiking-neuron network with Elephant-type memory is introduced, in which past firing activity modifies future excitability through a reinforcement-dependent threshold, and non-explosion of the nonlinear process is proved.
F. Najman, I. Papageorgiou, Sabricia K. Cauanny A. da Silveira· 1 citation
This work reformulates temporal credit assignment as a state separation problem: extracting task-required components induced by historical perturbations directly from the current neural state, which enables an online feedback learning framework for NMCs through a gradient tunneling algorithm and the lead-lag expansion...
Xiang-Nan Zhang, Jing-Xin Liu, Ran-Qi Lu et al.· 0 citations
This work presents a Hebbian local learning rule that models synaptic modification as a function of calcium traces tracking neuronal activity and demonstrates how spike timing and rate can be complementary in their role of shaping the connectivity of spiking neural networks.
Willian Soares Girāo, Nicoletta Risi, Caroline Geisler et al.· Neuromorphic Computing and E...· 0 citations
This work formalizes causal regions as polyhedral regions with fixed causal sets and derive upper and lower bounds on the maximal number of causal regions in both shallow and multilayer feedforward spiking networks.
It is found that STDP and desynchronization form a regulatory loop, in which STDP regulates the level of synchrony, and synchrony regulates subsequent learning strength, and STDP is capable of switching neurons from synchronization to desynchronization.
Yun-Qi Huang, M. Lankarany, G. D'Eleuterio· bioRxiv· 0 citations
Spiking reservoir computing, and reservoir computing more generally, is a powerful and efficient framework for neuromorphic and biological applications, in which a fixed random reservoir drives a trained readout. Its performance depends critically on the reservoir initialization, so that enriching the reservoir with ad...
Maciej Kania, Basile Confavreux, T. Vogels· bioRxiv· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
