Postdocs : Open postdoctoral positions in computational neuroscience, beginning in Fall 2008. Broadly speaking, projects will involve modeling the dynamics of activity, plasticity, and learning in networks that underlie complex behaviors -- including, but not limited to, neural integrators, song production and learning in songbirds, and navigation in ants and rodents. Collaborations and interactions with the greater neuroscience community are encouraged, and projects will typically involve collaborations with experimentalists. The initial appointment will be for 1 year with a possibility of extension up to 3 years. Quantitative training with a Ph.D. in Physics, Mathematics, Engineering, Computer Science, or Computational Neuroscience is required. Ability to program in Matlab or C and some knowledge of neuroscience is a desirable but not necessary. If interested, please email me with a copy of your CV or resume and a statement of research interests, and arrange to have 3 letters of recommendation sent to me by email or regular mail. I will review applications beginning July 5, and will continue until all positions are filled.

Students: Research projects are available for graduate and undergraduate students interested in computational and theoretical neuroscience. Quantitative training in Physics, Mathematics, Engineering, or Computer Science is required. The ability to program in C or Matlab and knowledge of neuroscience is desirable. 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

 

• M. Murthy, I. R. Fiete, and G. Laurent. Non-Stereotyped Odor Responses in Drosophila Mushroom Body Kenyon Cells. Neuron (In Press 2008).
   
• I. R. Fiete, Y. Burak and T. Brookings. What grid cells encode about rat position. J. Neuroscience 28, 6856-6871 (2008). (pdf)
  
        Related: T. Brookings, Y. Burak and I. R. Fiete. Triangular lattice neurons (grid cells) may encode rat position using an
        advanced numeral system. Preprint at: arxiv.org, q-bio.NC/0606005 (2006). (pdf)
   
• I. R. Fiete and H. S. Seung. Neural network models of birdsong production, learning, and coding. In New Encyclopedia of Neuroscience. Eds L. Squire, T.Albright, F. Bloom, F.Gage, and N. Spitzer. Elsevier ( In Press, To appear Sept 2008). (pdf)
   
• I. R. Fiete, M.S. Fee and H. S. Seung. Model of birdsong learning based on gradient estimation by dynamic perturbation of neural conductances. J. Neurophysiology 98, 2038-2057 (2007). (pdf)
   
• Y. Burak and I. R. Fiete. Do we understand the emergent dynamics of grid cell activity? J. Neuroscience 26, 9352-9354 (2006). (pdf)
   
• I. R. Fiete and H. S. Seung. Gradient learning in spiking neural networks by dynamic perturbation of conductances. Physical Review Letters 97, 048104 (2006). (pdf)
   
• 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. J.Neurophysiology 92, 2274 (2004). (pdf)
   
• 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. Physical Review B 62, 11626 (2000). (pdf)
   
• S. Sullow, I.R. Prasad, S. Bogdanovich et al. Magnetotransport in the low carrier density ferromagnet EuB_6. J. Applied Physics 87, 5591 (2000). (pdf)
   
• S. Sullow, I.R. Prasad, M.C. Aronson et al. Magnetic order of EuB_6. Physical Review B 57, 5860 (1998). (pdf)
   

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.