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lmazzuca - at - uoregon - dot - edu
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University of Oregon, Eugene

Welcome to the Mazzucato Lab
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.
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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.​

Lab News
09 10 2019
New tutorial on spiking network simulations and Hidden Markov Model analyses
for the Summer School on Computational Neuroscience in Venice. Including customizable demo scripts for E-I clustered networks and a data analysis suite with simple examples.
06 19 2019
New review published on Current Opinion in Neurobiology:
"Cortical computations via metastable activity" in collaboration with the La Camera and Fontanini labs.
04 28 2019
Proposal accepted for a Bernstein Workshop on "Cortical computations via metastable activity" 
​September 17-18, 2019, in Berlin
04 18 2019
New biorxiv paper out
"Hippocampal network reorganization underlies the formation of a temporal association memory," in collaboration with the Fusi and Losonczy labs.
04 01 2019
New paper accepted in Nature Neuroscience
Sensory stimuli are recognized faster when they are expected. Comparing a spiking network model to cortical recordings from behaving animals, We showed that expectation accelerates sensory processing by modulating the intrinsically generated activity preceding stimulation.
04 01 2019
New "Artificial Intelligence and the Brain" Program at UO
Visitor program at the Institute of Neuroscience kicks off in April with Stefano Recanatesi (UW) and Gabe Ocker (Allen)
03 04 2019
Talk at the Cosyne Workshops "Why spikes"
03 04 2019
Proposal accepted for a Simons Workshop on the "Physics of neural circuits and network dynamics"  
January 27-31 2020 at the Simons Center for Geometry and Physics, Stony Brook.
Proposal accepted for a Simons Center Program on the "Neural networks and the Data Science Revolution: from theoretical physics to neuroscience, and back"
 
January 2020 at the Simons Center for Geometry and Physics, Stony Brook.