BEAR LAB
Howard Hughes Medical Institute :: MIT :: The Picower Institute for Learning and Memory		  

Jeff Gavornik, Ph.D.

Post-Doctoral Fellow

e-mail

617-324-7008

Research Interests

All cognitive functions somehow emerge from the ongoing communication between neurons in the brain. In a very real sense, each of us is entirely defined by our unique neural anatomy. We know that the connections between neurons are continuously modified by experience over a lifetime and that neural structure determines how information is represented and processed within the brain. Except in very limited ways (often involving highly contrived laboratory conditions) we don't know how brain structure actually relates to function or how experience driven synaptic plasticity actually modifies the connections between neurons. In other words, we don't know how the brain works. I'm trying to figure it out.

I use a combination of computational and experimental techniques to study how visual experience modifies visual processing and perception. My goal is to understand how the cortex learns and represents sequential relationships (for example, "B" follows "A"), processes time and makes predictions. It may seem that these things are unrelated. What, after all, does vision have to do with temporal processing or sequences? The answer is quite a bit. When you look at a baseball, you recognize the object because your brain knows that the image projected on your retinas represents a baseball, and you can can predict a ball's future location in space using motion cues gleaned from the temporal sequence of retinal activation patterns that results as it flies through the air. You weren't born with these abilities, you learned them by looking at the world around you using the same cortical mechanisms that underlie all learning and memory. I believe that insights gained studying the early visual system will inform a general understanding of how the brain works.

I am interested in neuroscience primarily because neuroscience is really interesting. The brain is an incredibly complex, non-linear, non-stationary stochastic, dynamic system. Through unsupervised experience it can learn to recognize, predict and generate patterns while maintaining robust I/O control of the body's sensory and effector systems. I want to understand the principles by which biological neural networks organize and processes information. Disease states that disrupt neural function can be devastating; understanding how molecular machinery creates and maintains brain function is absolutely necessary to design effective medical interventions targeting all manner of mental disorders and neurological dysfunctions. An algorithmic understanding of the brain will also allow us to design neuromimetic perception and control systems that actually work.


Publications

Shouval HZ, Agarwal A, Gavornik JP. (2013) Scaling of perceptual errors can predict the shape of neural tuning curves. Phys. Rev. Letters (in press).

Gavornik JP, Shouval HZ. (2010) A network of spiking neurons that can represent interval timing: mean field analysis. J. Comput. Neurosci. 30(2) 501-13.

Shouval HZ, Gavornik JP (2010) A single spiking neuron that can represent interval timing: analysis, plasticity and multi-stability. J. Comput. Neurosci. 30(2) 488-99.

Coleman J, Nahmani M, Gavornik JP, Haslinger R, Heynen A, Bear MF and Erisir A. (2010) Rapid structural remodeling of thalamocortical synapses parallels experience-dependent functional plasticity in mouse primary visual cortex. J. Neurosci. 30(29) 9670-82.

Gavornik JP, Shuler MGH, Loewenstein Y, Bear MF and Shouval, HZ (2009) Learning reward timing in cortex through reward dependent expression of synaptic plasticity. Proc. Natl. Acad. Sci. U S A 106(16):6826-31.

Cai Y, Gavornik JP, Cooper LN, Yeung LC, and Shouval HZ (2007) Effect of stochastic synaptic and dendritic dynamics on synaptic plasticity in visual cortex and hippocampus. J. Neurophysiol. 97:375-386.


Funding

K99/R00 Cortical mechanisms of learned spatial-temporal sequence coding (NIMH:1K99MH099654-01)


Education, Work, and Research Experience

HHMI Postdoctoral Associate in the Mark F. Bear Lab. The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA. (2009 – present)

Doctor of Philosophy in Electrical Engineering. The University of Texas at Austin, Austin, TX. (May 2009)

Researcher, Department of Neurobiology and Anatomy. The University of Texas Health Science Center. Houston, TX. (2006-2009)

Summer Program in Experimental & Computational Neurodynamics, Center for Theoretical Biological Physics, The University of California San Diego, La Jolla, CA. (August 2006)

Master of Electrical Engineering. Rice University. Houston, TX (May 2003)

Engineer, Integrated Defense Systems, International Space Station Hardware/Software Integration. The Boeing Company. Houston, TX. (1999-2006)

Bachelor of Science, Electrical and Computer Engineering & History. Rice University. Houston, TX. (May 1999)

Internship, Satellite Communications Division. The MITRE Corp. Bedford, MA. (1998-1999)

Intern and Carl B. & Florence E. King Foundation Summer Program Participant and Internship, Diagnostics Physics Department, MD Anderson Cancer Center. Houston, TX. (1995~1998)

Internship, Anderson Lab. Air Force Office of Scientific Research. San Antonio TX. (1993-1995)


Chili, Done Right

Austin Chili