Predicting spike times from subthreshold dynamics of a neuron
نویسندگان
چکیده
It has been established that a neuron reproduces highly precise spike response to identical fluctuating input currents. We wish to accurately predict the firing times of a given neuron for any input current. For this purpose we adopt a model that mimics the dynamics of the membrane potential, and then take a cue from its dynamics for predicting the spike occurrence for a novel input current. It is found that the prediction is significantly improved by observing the state space of the membrane potential and its time derivative(s) in advance of a possible spike, in comparison to simply thresholding an instantaneous value of the estimated potential.
منابع مشابه
Exact Subthreshold Integration with Continuous Spike Times in Discrete-Time Neural Network Simulations
Very large networks of spiking neurons can be simulated efficiently in parallel under the constraint that spike times are bound to an equidistant time grid. Within this scheme, the subthreshold dynamics of a wide class of integrate-and-fire-type neuron models can be integrated exactly from one grid point to the next. However, the loss in accuracy caused by restricting spike times to the grid ca...
متن کاملSensitivity to the cutoff value in the quadratic adaptive integrate-and-fire model
The quadratic adaptive integrate-and-fire model (Izhikevich, 2003; Izhikevich, 2007) is recognized as very interesting for its computational efficiency and its ability to reproduce many behaviors observed in cortical neurons. For this reason it is currently widely used, in particular for large scale simulations of neural networks. This model emulates the dynamics of the membrane potential of a ...
متن کاملNoise-enhanced coding in phasic neuron spike trains
The stochastic nature of neuronal response has lead to conjectures about the impact of input fluctuations on the neural coding. For the most part, low pass membrane integration and spike threshold dynamics have been the primary features assumed in the transfer from synaptic input to output spiking. Phasic neurons are a common, but understudied, neuron class that are characterized by a subthresh...
متن کاملM-type potassium conductance controls the emergence of neural phase codes: a combined experimental and neuron modelling study.
Rate and phase codes are believed to be important in neural information processing. Hippocampal place cells provide a good example where both coding schemes coexist during spatial information processing. Spike rate increases in the place field, whereas spike phase precesses relative to the ongoing theta oscillation. However, what intrinsic mechanism allows for a single neuron to generate spike ...
متن کاملParameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms.
Cortical information processing originates from the exchange of action potentials between many cell types. To capture the essence of these interactions, it is of critical importance to build mathematical models that reflect the characteristic features of spike generation in individual neurons. We propose a framework to automatically extract such features from current-clamp experiments, in parti...
متن کامل