Speaker
Salva Dura
Date
Location
University of Houston
Abstract
Understanding the brain's neural code would allow us to develop treatments for brain
disorders affecting 1 out of 2 people, develop technology to read and write information from
the brain, and advance artificial intelligence. Despite the large amount of experimental data,
understanding the brain is challenging due to its complex interactions across scales: from
molecules to cells to circuits to behavior. Large-scale biophysically-detailed brain simulations
provide an unrivaled method to integrate these data and bridge the scales. We have
developed a model of mouse primary motor cortex (M1) with over 10,000 realistic neurons
and 30 million synapses, that can reproduce cell-type and layer-specific in vivo activity
associated with behavior. An earlier version of the model was trained to control a virtual and
robotic arm using reinforcement learning. We have also developed a software tool
(www.netpyne.org) that makes multiscale modeling of brain circuits accessible to the wider
community.
disorders affecting 1 out of 2 people, develop technology to read and write information from
the brain, and advance artificial intelligence. Despite the large amount of experimental data,
understanding the brain is challenging due to its complex interactions across scales: from
molecules to cells to circuits to behavior. Large-scale biophysically-detailed brain simulations
provide an unrivaled method to integrate these data and bridge the scales. We have
developed a model of mouse primary motor cortex (M1) with over 10,000 realistic neurons
and 30 million synapses, that can reproduce cell-type and layer-specific in vivo activity
associated with behavior. An earlier version of the model was trained to control a virtual and
robotic arm using reinforcement learning. We have also developed a software tool
(www.netpyne.org) that makes multiscale modeling of brain circuits accessible to the wider
community.