Program and Abstract / OCNC2016

Lecturer

We are acquiring visual information at the eye, auditory information in the ear, olfactory information at the nose etc., which is conveyed to the brain and processed and transformed to make us to recognize as a sense. The brain also works for generating and controlling a complicated behavior, and are responsible to define aspects of behavior as feelings and abstract of thought.  

Neurons are the smallest component of the brain and are the key players for signal processing for making these difficult tasks with wiring each other.     

 In this lecture we will learn basic physiological character and mechanism of neurons to see how those complicated tasks can be performed. We will also try to get an idea how neurons can compute signals by wisely connecting each other.

Title 2

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One hypothesis of the neuronal code is that relevant information about a stimulus (e.g. the orientation of a bar) is represented by the activity of a set of relatively homogenous populations of neurons (e.g. neurons of different orientation tuning). Even though such neurons might respond differently under different stimuli (e.g. they might have different color preferences), for description of orientation tuning we can bundle them together. 

Is it possible to simulate the response statistics of populations of neurons directly without simulating individual neurons? Yes, for fairly simple neuronal and synaptic models. To this end we developed DiPDE, a simulation platform for numerically solving the time evolution of coupled networks of neuronal populations. Instead of solving the sub-threshold dynamics of individual model leaky-integrate-and-fire (LIF) neurons, DiPDE models the voltage distribution of a population of neurons with a single population density equation. In this way, DiPDE can facilitate the fast exploration of mesoscale (population-level) network topologies, where large populations of neurons are treated as homogeneous with random fine-scale connectivity.

I will provide a series of examples on how this method can be used to study the activity in a small number of coupled populations and how it can be used to explore the computational properties of a cortical column model. In the end I will discuss the possibilities of using these tool in combination with measured mesocopic connectivity data.

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