Dr. Rajan and her lab design neural network models based on experimental data, and reverse-engineer them to figure out how brain circuits function in health and disease. They have been developing a powerful new framework for tracing neural paths across multiple brain regions. They call this framework CURrent-Based Decomposition (CURBD). It enables the team to compute excitatory and inhibitory input currents from other neurons that drive a given neuron. This enables the team to learn about the way entire populations of neurons behave across multiple interacting brain regions. They have applied this method to studying behavior--for example in a model called learned helplessness often used to study depression and anxiety in animals. They have thus uncovered some of the underlying biology driving adaptive and maladaptive behaviors. With this powerful framework Dr. Rajan's team probes for mechanisms at work across brain regions that support both healthy and disease states, as well as identify key divergences.
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