# Building a Dynamical Network Model from Neural Spiking Data: Application of Poisson Likelihood

@article{Doruk2017BuildingAD, title={Building a Dynamical Network Model from Neural Spiking Data: Application of Poisson Likelihood}, author={Ozgur R Doruk and Kechen Zhang}, journal={arXiv: Neurons and Cognition}, year={2017} }

Research showed that, the information transmitted in biological neurons is encoded in the instants of successive action potentials or their firing rate. In addition to that, in-vivo operation of the neuron makes measurement difficult and thus continuous data collection is restricted. Due to those reasons, classical mean square estimation techniques that are frequently used in neural network training is very difficult to apply. In such situations, point processes and related likelihood methods…

## 2 Citations

An attempt to fit all parameters of a dynamical recurrent neural network from sensory neural spiking data

- Computer Science
- 2018

This work will be a continuation of a previous study where the parameters associated with the sigmoidal gain functions are not taken into account, and will construct a similar framework but all parameters related to the model are estimated.

Fitting of dynamic recurrent neural network models to sensory stimulus-response data

- BiologyJournal of biological physics
- 2018

A theoretical study aiming at model fitting for sensory neurons with universal approximation feature of the recurrent dynamical neuron network models, which allows it to describe excitatory-inhibitory characteristics of an actual sensory neural network with any desired number of neurons.

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