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Found 8 papers, 0 papers with code
Signal Coding and Reconstruction using Spike Trains
In many animal sensory pathways, the transformation from external stimuli to spike trains is essentially deterministic.
Spike-Triggered Descent
The characterization of neural responses to sensory stimuli is a central problem in neuroscience.
Signal Coding and Perfect Reconstruction using Spike Trains
The framework considers encoding of a signal through spike trains generated by an ensemble of neurons via a standard convolve-then-threshold mechanism.
Multi-Scale Generalized Plane Match for Optical Flow
In this paper, we propose a novel optical flow estimation framework which can provide accurate dense correspondence and occlusion localization through a multi-scale generalized plane matching approach.
Learning Feedforward and Recurrent Deterministic Spiking Neuron Network Feedback Controllers
We address the problem of learning feedback control where the controller is a network constructed solely of deterministic spiking neurons.
Sensitivity Analysis for additive STDP rule
Spike Timing Dependent Plasticity (STDP) is a Hebbian like synaptic learning rule.
Learning Precise Spike Train to Spike Train Transformations in Multilayer Feedforward Neuronal Networks
First, an error functional is proposed that compares the spike train emitted by the output neuron of the network to the desired spike train by way of their putative impact on a virtual postsynaptic neuron.
A Novel Kernel for Learning a Neuron Model from Spike Train Data
From a functional viewpoint, a spiking neuron is a device that transforms input spike trains on its various synapses into an output spike train on its axon.
