The progress in experimental neuroscience over the past two decades allows scientists to record brain activity with unprecedented spatial and temporal resolution. In many cases, the limiting factor is the ability to analyze and interpret the experimental data. One of the most successful theoretical approaches in this context is the mathematical, or computer, modeling of neurons: using biophysical properties of the neuronal membrane one can represent a neuron as an equivalent electric circuit. Such an appr oach allows to study properties of neuronal networks in a setup where all the quantities are controlled or monitored by the researcher.
The goal of this project is to mathematically model alectric potentials which can be measured in brain. Specifically, we plan to simulate populations of cortical neurons and predict LFP (local field potential) and VSD (voltage-sensitive dye) signals which would be measured in the modeled system. This approach is called ?forward modeling?. The project will provide the li nk between the brain activity at the population level and the quantities which are directly measured in experiments. The overall goal of this would be to improve the data analysis techniques through testing in the controlled, model situation.
The main p art of the project will be computer simulations of compartmental models of neurons. We will use the NEURON simulator together with morphologically detailed neuron models available through public databases. The results of the simulation (LFP and VSD calcul ated from activity of a population of neurons) will be then compared to LFP and VSD signals co-registered in the rat barrel cortex (data provided by a collaborating experimental neuroscience group).
The final result of the project will be a set of metho ds and an efficient software tool for joint modeling of LFP and VSD data generated by neuronal populations.