Computational principles of neural circuits

One of the challenges for understanding the neural code is the issue of dimensionality. If we image each neuron is like a logic gate that can either be on or off, then the number of possible patterns of activity a group of neurons can generate scales ~2^Neurons.  means For even a population of a few hundred neurons (~150-300, which we regularly record from in the lab), the possible patterns is ~1090, log orders more than the number of atoms estimated in the observable universe.  The lab has developed a number of computational/theoretical methods to overcome this challenge of dimensionality to analyze the activity of large populations of neurons.  Using advances from the fields of statistical mechanics and information theory the lab aims to identify how patterns of neural activity are used to represent features of the sensory world and how these patterns generate motor behaviors.   Additionally, the lab is focused on using these methods to understand how changes in neural activity due to diseases such as Schizophrenia and Alzheimer’s can lead to alterations in cognitive function.  

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