Method for estimating output neuron spikes based on input-output spike data

A method for estimating output neuron spiking activity based on input-output spike data that includes input spike data and corresponding output spike data, comprising:

• formulating a computational framework for estimating functional spike-timing-dependent plasticity (STDP) parameters, comprising:
• formulating a flexible spiking neuron model structure with STDP including stationary functions that characterize stationary properties of a neuron and plastic functions that characterize plastic properties of the neuron, the plastic properties including at least one neural functional connectivity strength;
• observing input-output spike data that includes input spike data and corresponding output spike data;
• estimating the functional STDP parameters, comprising amplitude functions and STDP induction functions, based on the input-output spike data, wherein the input-output spike data comprises spike timing, wherein the amplitude function is based on the interval between a presynaptic spike and a post synaptic spike, wherein the induction functions describe an induction time course of plasticity and are respectively scaled by the amplitude functions;
• determining at least one weight value based on the STDP parameters;
• formulating an estimation technique and applying the estimation technique using the input-output spike data to identify parameters that increase a likelihood of observing output spiking activity;
• providing at least one output spike train using the identified parameters from the estimation technique that involves use of a feedforward kernel.

A system for estimating output neuron spikes, comprising:

• an input device or an input port configured to receive input-output spike data comprising spike timing that includes input spike data and corresponding output spike data that is used for creating a spiking neuron model structure with adjustable functional spike-timing-dependent plasticity (STDP) parameters, comprising amplitude functions and STDP induction functions, wherein the amplitude function is based on the interval between a presynaptic spike and a post synaptic spike, wherein the induction functions describe an induction time course of plasticity and are respectively scaled by the amplitude functions;
• and
• a controller configured to create the spiking neuron model structure by:
• formulating a weight value that characterizes plastic properties including at least one neural functional connectivity strength of the neuron based on functional STDP parameters which are estimated based on the input-output spike data,
• receiving an input spike train, and
• estimating an output spike train that corresponds to the input spike train by applying the weight value to a feedforward kernel that estimates the output spike train based on the input spike train and the weight value.