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Found 5 papers, 0 papers with code
Pattern representation and recognition with accelerated analog neuromorphic systems
Despite being originally inspired by the central nervous system, artificial neural networks have diverged from their biological archetypes as they have been remodeled to fit particular tasks.
Stochastic inference with spiking neurons in the high-conductance state
The highly variable dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference but stand in apparent contrast to the deterministic response of neurons measured in vitro.
The high-conductance state enables neural sampling in networks of LIF neurons
The core idea of our approach is to separately consider two different "modes" of spiking dynamics: burst spiking and transient quiescence, in which the neuron does not spike for longer periods.
Stochastic inference with deterministic spiking neurons
The seemingly stochastic transient dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference.
Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints
Recent spiking network models of Bayesian inference and unsupervised learning frequently assume either inputs to arrive in a special format or employ complex computations in neuronal activation functions and synaptic plasticity rules.
