Giancarlo La Camera
Associate Professor
Director, Center for Neural Circuit Dynamics
PhD, University of Bern
Giancarlo.LaCamera@stonybrook.edu
Life Sciences Building
Office: Room 518
Phone: (631) 632-9109 - office
Fax: (631) 632-6661
Training
Giancarlo La Camera studied Theoretical Physics at the University of Rome "La Sapienza" and received a Laurea (M. Sci.) in 1999. He went on to obtain a PhD in Neurobiology from the University of Bern in 2003. Between 2004 and 2008 he was a visiting fellow at the National Institute of Mental Health, where he performed research on the neural basis of cognitive functions. He then returned to the University of Bern where he focused on the topic of reinforcement learning in populations of spiking neurons. In early 2011 he joined the faculty of Stony Brook University as an Assistant Professor of Neurobiology & Behavior and was promoted to the rank of Associate Professor with tenure in 2017.
Research
theoretical neuroscience / sensory and cognitive processes / learning and behavior
My laboratory pursues theoretical and computational research on the neural basis of sensory and cognitive processes. These include memory, decision making, and more recently the chemosensory processes involving taste perception. Priority is given to developing biologically plausible models, often in terms of populations of spiking neurons, using techniques borrowed from physics, machine learning, and data science. With this approach we have characterized the metastable nature of ongoing and evoked activity in several cortical areas (most notably the primary gustatory cortex), and worked out some of its consequences for neural coding. We hope that uncovering the common basis of ongoing and evoked activity can tell us much about how cortical networks are organized and function. The investigation of what type of sensory and cognitive functions can be subserved by metastable (rather than stable) neural activity is also a central effort of the lab.
We are also interested in the theory of learning and modeling synaptic plasticity in cortical circuits. On this topic we have made progress on two main fronts: 1) We have proposed a model of how to segment a sensory stream to extract the patterns of neural activity of unknown nature and timing that are relevant for making decisions. This was achieved by reinforcement learning in a network of spiking neurons, and is a first step towards understanding how an agent can learn to identify the 'features' of its environment that are relevant for decision making. 2) We have proposed a biologically plausible plasticity rule that tunes a spiking network into the metastable dynamical regime we have observed and analyzed in depth in cortical data. The plasticity co-exists with ongoing metastable dynamics (the bulk of the synaptic weights are stable against ongoing neural activity) and can learn new representations by training with new stimuli (the weights remain plastic), offering a potential solution to the long-standing stability-plasticity dilemma.
While the main focus of our lab is the mammalian brain (especially the neocortex), our interests remain eclectic and we are always willing to take on intriguing challenges. For example, we have recently proposed the first mathematical model of decentralized vision in the sea urchin, an invertebrate that orients to visual stimuli despite lacking eyes.
In addition to building mathematical models of neural circuits and their function, we team up with other research groups in the Department of Neurobiology and elsewhere to test our models against empirical data.
Teaching
Undergraduate (U) and graduate (G) courses which I direct or co-direct:
- AMS/BIO 332 (U) – Computational Modeling of Physiological Systems
- NEU 536 (G) – Introduction to Computational Neuroscience
- BNB 567 (G) – Statistics and Data Analysis for Neuroscience I: Foundations
- BNB 568 (G) – Statistics and Data Analysis for Neuroscience II: Applications
Undergraduate (U) and graduate (G) courses to which I contribute:
- BIO 335 (U) – Neurobiology Laboratory (2012-2016)
- BIO 338 (U) – From Synapse to Circuit: Self-organization of the Brain
- GRD 500 (G) – Integrity in Science (aka “Responsible Conduct of Research”)
- GRD 600 (G) – Rigor and Reproducibility in Research
- NEU 501 (G) – Introduction to Neuroscience Research
- BNB 562 (G) – Introduction to Neuroscience II: Systems Neuroscience
- BNB 597 (G) – Seminar Themes: Research Topics in Neuroscience
- PHY 687 (G) – Topics in Biological Physics: Introduction to Computational Neuroscience
(2011)