Ph.D. University of Bern
Fax: (631) 632-6661
Life Sciences Building
Office: Room 513
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.
Theoretical neuroscience / theory of neural representations / learning and behavior
My long-term interest is in the neural basis of cognitive function. Currently, I pursue three main lines of investigation: i) the characterization of cortical activity, ii) the theory of spike-based learning, and iii) the neural basis of decision-making. In all those threads, my efforts revolve around the central question of how to build powerful representations of external stimuli and events that are relevant to behavior.
i) Neural activity in cortex is highly variable and richly structured, both in the presence ('evoked') and in the absence ('ongoing') of overt sensory stimulation. I'm interested in the origin and nature of neural variability and in the precise relationship between ongoing and evoked activity. I investigate these questions by analyzing experimental data and by modeling the observed phenomenona with networks of spiking neurons.
ii) Learning is accomplished by experience-dependent modifications of the synaptic connections among neurons. These modifications depend on the 'local' spiking activity of the neurons and on the 'global' action of neurotransmitters, which are broadcast throughout wide brain regions in the presence of relevant events (such as rewards, punishments, fearful events, and so on). I'm interested in biologically plausible learning rules for large populations of spiking neurons that can handle difficult tasks, such as learning to identify relevant stimuli from a continuous sensory stream, without prior information on their relevance and timing.
iii) I have a long-lasting interest in the neural basis of decision-making and its motivational and hormonal basis. In particular, I'm interested in how these processes depend on contextual factors, and how they shape our processing of relevant stimuli (i.e., how we ‘see’ and interpret the world). Context is a powerful modulator of the way we make decisions. We are very susceptible to factors such as the way in which an option is framed or our emotional state when exposed to a choice between two equivalent options. I'm particularly interested in the neural substrate of decisions between options that are equivalent in terms of their 'economic' value.
In addition to seeking a theoretical understanding of these phenomena, I team up with other research groups in the Department of Neurobiology and elsewhere to test our model predictions against empirical data.
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
Undergraduate (U) and graduate (G) courses to which I contribute:- BIO 335 (U) – Neurobiology Laboratory
- BIO 338 (U) – From Synapse to Circuit: Self-organization of the Brain
- GRD 500 (G) – Integrity in Science (aka “Responsible Conduct of 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)
- Representative Publications
- Laboratory Personnel
- L. Mazzucato, G. La Camera* and A. Fontanini*, Expectation-induced modulation of metastable activity underlies faster coding of sensory stimuli, bioRxiv 199380; doi: https://doi.org/10.1101/199380, 2017
- G. La Camera, S. Bouret and B.J. Richmond, Contributions of different prefrontal cortical regions to abstract rule acquisition and reversal in monkeys, bioRxiv 180893; doi: https://doi.org/10.1101/180893, 2017
- L. Le Donne, L. Mazzucato, R. Urbanczik, W. Senn* and G. La Camera*, Spike-based reinforcement learning for temporal stimulus segmentation and decision making, Computing with Spikes Workshop, NIPS 2016, Barcelona, Spain, 2016
- L. Mazzucato, A. Fontanini and G. La Camera, Stimuli reduce the dimensionality of cortical activity, Front Sys Neurosci 10:11, 2016. Pubmed entry
- L. Mazzucato, A. Fontanini* and G. La Camera*, Dynamics of multi-stable states during ongoing and evoked cortical activity, J Neurosci 35(21): 8214-8231, 2015. Pubmed entry
- G. La Camera, R. Urbanczik and W. Senn, Stimulus detection and decision making via spike-based reinforcement learning, Proceedings of the 1st Multidisciplinary Conference on Reinforcement Learning and Decision Making, pp. 183-187, Princeton NJ, 2013
- A. Jezzini*, L. Mazzucato*, G. La Camera and A. Fontanini, Processing of hedonic and chemosensory features of taste in medial prefrontal and insular networks, J Neurosci 33(48): 18966-18978, 2013
- T. Minamimoto, G. La Camera, and B.J. Richmond, Measuring and Modeling the Interaction Among Reward Size, Delay to Reward, and Satiation Level on Motivation in Monkeys, J Neurophysiol 101:437-447, 2009
- M. Giugliano, G. La Camera, S. Fusi and W. Senn, The response of cortical neurons to in vivo-like input current: theory and experiment II. Time-varying and spatially distributed inputs, Biol Cybern 99(4-5):303-18, 2008
- G. La Camera, M. Giugliano, W. Senn and S. Fusi, The response of cortical neurons to in vivo-like input current: theory and experiment I. Noisy inputs with stationary statistics, Biol Cybern 99(4-5):279-301, 2008
- G. La Camera and B.J. Richmond, Modeling the violation of reward maximization and invariance in reinforcement schedules, PLoS Comput Biol 4(8): e1000131, 2008
- G. La Camera*, A. Rauch*, D. Thurbon, H-R Lüscher, W. Senn and S. Fusi, Multiple time scales of temporal response in pyramidal and fast spiking cortical neurons, J Neurophysiol 96(6): 3448-3464, 2006
- E. Curti, G. Mongillo, G. La Camera and D.J. Amit, Mean-Field and capacity in realistic networks of spiking neurons storing sparsely coded random memories, Neural Comput 16(12): 2597-2637, 2004
- G. La Camera, A. Rauch, H-R Lüscher, W. Senn and S. Fusi, Minimal models of adapted neuronal response to in vivo-like input currents, Neural Comput 16(10): 2101-2124, 2004
- A. Rauch*, G. La Camera*, H-R Lüscher, W. Senn and S. Fusi, Neocortical pyramidal cells respond as integrate-and-fire neurons to in vivo-like input currents, J Neurophysiol 90(3): 1598-1612, 2003
- Luca Mazzucato, Ph.D. Physics, SISSA/ISAS Trieste: Sr. Postdoc (2012-13); Research Assistant Professor (2013-present) (in collaboration with Dr. A. Fontanini).
- Luisa Le Donne, M.Sc. Physics, Sapienza University of Rome: Graduate Student (2011-2017)
- Lucinda A. Davies, Ph.D. Biomedical Sciences, University of Leeds: Sr. Postdoc (2011-2015) -- now at ICON Clinical Research.