Volume 9 Supplement 1

## Seventeenth Annual Computational Neuroscience Meeting: CNS*2008

- Poster presentation
- Open Access

# Investigating the effect of Cortical Discharge Variability on the accuracy of population decoders

- Mehdi Aghagolzadeh
^{1}Email author and - Karim Oweiss
^{1, 2}

**9(Suppl 1)**:P2

https://doi.org/10.1186/1471-2202-9-S1-P2

© Aghagolzadeh and Oweiss; licensee BioMed Central Ltd. 2008

**Published: **11 July 2008

## Keywords

- Gaussian Model
- Response Property
- Firing History
- Tuning Width
- Firing Probability

Estimation of the response properties of cortical neurons from within a recorded population is an essential component in a cortically-controlled brain machine interface application. The response properties of interest typically include precise spike timing, mean firing rate and any inherent correlation in the activity of the recorded ensemble. These response properties are essential for the operation of a neural decoder that translates the observed cortical activity to command signals for controlling robotic arms. In this work we investigate the effect of cortical discharge variability on the decoding performance. Specifically cortical discharge variability was induced in two types of cortical network models. The first one is a probabilistic model in which the activity was modeled as an inhomogeneous Poisson process with firing probability that depends on the neuron's own firing history and those of other neurons connected to it through time-varying synaptic couplings. The second is a biophysical leaky integrate and fire model. Neurons in both models were cosine-tuned to movement direction with random tuning widths.

_{i}is the number of spikes for the i

^{th}neuron over a fixed bin width. The mean and variance of each Gaussian model is defined through the training process. The other two decoders are the Wiener and the Kalman filters. The entire population was 45 neurons and we used a random subset of these for decoding. The maximum likelihood decoder demonstrates a superior performance compared to the Weiner and Kalman based decoders (Fig. 1).

## Authors’ Affiliations

## Copyright

This article is published under license to BioMed Central Ltd.