- Poster presentation
- Open Access
Designing spiking neural models of neurophysiological recordings using gene expression programming
© Espinosa-Ramos et al; licensee BioMed Central Ltd. 2013
- Published: 8 July 2013
- Pyramidal Neuron
- Reference Signal
- Spike Train
- Gene Expression Programming
- Neural Model
Spiking Neural Models (SNMs) can accurately predict the spike trains produced by cortical neurons in response to somatically injected currents. Since the specific characteristics of the model depend on the neuron, a computational method is required to fit models to electrophysiological (EP) recording. However, important drawbacks of these models are that they only work within the defined limits to fit the EP recordings presented. These limitations suggest that the ideal would not be to fit existing models, but to construct a model for each kind of neurons. Recently, several labs around the world have approached the question about what constitutes a good neuron model by assessing it quality regarding to spike timing prediction or features of the voltage trace.
This work describes a first effort to design a methodology that creates automatically SNMs using an Evolutionary Computation Strategy (ECS). This methodology generates a mathematical equation that reproduces the behavior of biological neurons. Creating a SNM to reproduce EP data is performed by maximizing a fitness function which measures the adequacy of the model to the data. This task is done by using Gene Expression Programming (GEP), an ECS that automatically creates computer programs such as conventional mathematical models, sophisticated nonlinear models, and so on. In this research, we applied the gamma factor as a fitness function , which is based on the number of coincidences between the model spikes and the spikes experimentally recorded.
Experimental results suggest that the proposed methodology can be applied to generate SNM from EP recordings with high accuracy. Although the signal shape is not the same compared with the reference signal, spike timing matched the reference signal with great accuracy.
The authors thank Universidad La Salle for the economic support under grant I-061/12 and CONACyT through the project 132073.
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