How do cognition and behavior emerge from the collective activity of large networks of neurons in our brain? Can we simulate these neural circuits in silico to build artificial neural networks that share our complex cognitive abilities? Leveraging a vast network of collaborations with experimental colleagues, our lab aims to explain the neural underpinnings of natural and artificial intelligence, with the belief that we can only understand cognitive function once we can build it from the bottom up in biologically plausible neural network models. We focus in particular on how our cognitive abilities change depending on context, our mental state, and varying levels of neuromodulators (such as serotonin). This contextual modulation occurs for example when we make more mistakes while distracted, or perform really well while attentive. We design brain-machine interfaces to read and write neural activity in real time with the long-term goal of rescuing cognitive deficits for therapeutic interventions in the human brain.
Our lab is focused on elucidating the neural basis of sensory perception, decision-making, motor generation, and associative learning. Some of the questions we focus on are:
- What neural mechanisms underlie the temporal variability observed in naturalistic animal behavior?
- How does behavior emerge from the complex temporal interactions of cortical and subcortical networks?
- How do different physiological states, such as arousal, task engagement, expectation, affect sensory processing and modulate an animal's behavior?
- What drives associative learning at the level of synaptic plasticity in cortical and subcortical circuits?
To study these and other questions, we blend methods from statistical physics, information theory, machine learning, and dynamical systems. We combine analysis of neurophysiological data from large populations of neurons in behaving animals with theoretical models based on neural networks.