Postdocs : Available: 1 fully-funded postdoctoral position to collaborate with me and the broader systems neuroscience community at Caltech. The start date is flexible, but with a preference for beginning in early Fall 2007. The initial appointment will be for 1 year with a possibility of an extension. Strong quantitative training in Physics, Mathematics, Engineering, or Computer Science is required. Ability to program in Matlab or C and knowledge of neuroscience a plus. If interested, please contact me with a copy of your cv or resume, a statement of research interests, and 3 letters of recommendation. I will review applications after receiving all the requested information.

Students: Research projects with funding are available for graduate and undergraduate Caltech students interested in computational and theoretical neuroscience. These projects can be short enough for rotations or long enough to turn into Masters or Ph.D. theses. Broadly speaking, projects will involve modeling the dynamics of activity and learning in networks that underlie complex behaviors such as singing in songbirds and navigation in rodents and ants. Projects could involve working closely with data in collaboration with systems neuroscience labs at Caltech, depending on student interest. Students will be mentored jointly by Christof Koch and me. Some quantitative training in Physics, Mathematics, Engineering, or Computer Science is strongly desired. Ability to program in C and Matlab, and knowledge of neuroscience a plus. If interested, please contact me with a copy of your cv or resume.

I work on the theory and modeling of neural systems where the collective behavior is rich, but where underlying features such as single-neuron properties and local connectivity are constrained by experiment. My aims are to help (1) Elucidate the essential dynamical principles underlying emergent motor and sensorimotor function, (2) Understand principles of the encoding and decoding of neural information based on system function, and (3) Drive fruitful interactions between theory and experiment by generating non-trivial predictions for neural organization, activity, and synaptic plasticity. Ongoing and recent projects investigate:

• Navigation and idiothetic path integration in rats: dynamical modeling, precision and robustness
• Analysis of position codes in rat entorhinal cortex: properties and readouts
• Emergence of sequential neural activity (synaptic chains) in neural networks
• General rules for goal-directed (gradient) learning in recurrent networks
• Sensorimotor song learning in songbirds
• Sparse codes and learning in feedforward neural networks

 

• I. R. Fiete, Y. Burak and T. Brookings. What grid cells encode about rat position. (pdf) J. Neuroscience ( In Press 2008).
  
        Related: T. Brookings, Y. Burak and I. R. Fiete. Triangular lattice neurons (grid cells) may encode rat position
        using an advanced numeral system. (pdf) Preprint at: arxiv.org, q-bio.NC/0606005 (2006).
   
• M. Murthy, I. R. Fiete, and G. Laurent. Non-Stereotyped Odor Responses in Drosophila Mushroom Body Kenyon Cells. Neuron (Accepted 2008).
   
• I. R. Fiete and H. S. Seung. Neural network models of birdsong production, learning, and coding. (pdf) In New Encyclopedia of Neuroscience. Eds L. Squire, T.Albright, F. Bloom, F.Gage, and N. Spitzer. Elsevier ( In Press, To appear Sept 2008).
   
• I. R. Fiete, M.S. Fee and H. S. Seung. Model of birdsong learning based on gradient estimation by dynamic perturbation of neural conductances. (pdf) J. Neurophysiology 98, 2038-2057 (2007).
   
• Y. Burak and I. R. Fiete. Do we understand the emergent dynamics of grid cell activity? (pdf) J. Neuroscience 26, 9352-9354 (2006).
   
• I. R. Fiete and H. S. Seung. Gradient learning in spiking neural networks by dynamic perturbation of conductances. (pdf) Physical Review Letters 97, 048104 (2006).
   
• I. R. Fiete, R.H.R Hahnloser, M.S. Fee and H. S. Seung. Temporal sparseness of the premotor drive is important for rapid learning in a neural network model of birdsong. (pdf) J.Neurophysiology 92, 2274 (2004).
   
• I. R. Fiete. Learning and coding in biological neural networks. Ph.D. Thesis, Harvard University (2004).
   
• S. Sullow, I.R. Prasad, M.C. Aronson et al. Metallization and magnetic order in EuB_6. (pdf) Physical Review B 62, 11626 (2000).
   
• S. Sullow, I.R. Prasad, S. Bogdanovich et al. Magnetotransport in the low carrier density ferromagnet EuB_6. (pdf) J. Applied Physics 87, 5591 (2000).
   
• S. Sullow, I.R. Prasad, M.C. Aronson et al. Magnetic order of EuB_6. (pdf) Physical Review B 57, 5860 (1998).
   

Broad Fellow:   California Institute of Technology (2006-)
Postdoc:            Kavli Institute for Theoretical Physics, UCSB (2004-2006)
Ph.D.                 Physics, Harvard University (Dec 2003)
                          Advisor: H. Sebastian Seung
                          Field: computational and theoretical neuroscience
M.A.                  Physics, Harvard University (2000)
B.S.                  Physics and Mathematics, University of Michigan (1997)

Ila Varma: Junior in Mathematics and Computer Science, Caltech.
Peter Welinder: Graduate student in Center for Neural Systems, Caltech.
Ni Ji: Graduate student in Brain and Cognitive Sciences, MIT.
Ted Brookings: Postdoctoral fellow at Volen Center, Brandeis.
Prashant Joshi: Postdoctoral fellow at FIAS, Frankfurt.