Research Interests: Theoretical Neuroscience
Overview: Our lab is focused on elucidating the neural basis of sensory perception, decision-making, and associative learning.
- How does behavior emerge from the complex temporal interactions of cortical and subcortical networks?
- How do different physiological states, such as arousal, disengagement, 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 use 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.
Our lab is passionate about creating an inclusive environment promoting diversity. We embrace a notion of intellectual community enhanced by diversity along a number of dimensions, including race, ethnicity and national origins, gender identity and presentation, disability, class and religion. We aim at fostering greater creativity and encouraging collaborative innovation and interactions.
Neuron. 2021 Oct 25:S0896-6273(21)00779-0. doi: 10.1016/j.neuron.2021.10.011. Online ahead of print.
The timing of self-initiated actions shows large variability even when they are executed in stable, well-learned sequences. Could this mix of reliability and stochasticity arise within the same neural circuit? We trained rats to perform a stereotyped sequence of self-initiated actions and recorded neural ensemble activity in secondary motor cortex (M2), which is known to reflect trial-by-trial action-timing fluctuations. Using hidden Markov models, we established a dictionary between activity patterns and actions. We then showed that metastable attractors, representing activity patterns with a reliable sequential structure and large transition timing variability, could be produced by reciprocally coupling a high-dimensional recurrent network and a low-dimensional feedforward one. Transitions between attractors relied on correlated variability in this mesoscale feedback loop, predicting a specific structure of low-dimensional correlations that were empirically verified in M2 recordings. Our results suggest a novel mesoscale network motif based on correlated variability supporting naturalistic animal behavior.