Center for Perceptual Systems

Ila Fiete

Associate ProfessorPh.D., Harvard University

Ila Fiete



I work on the theory and modeling of neural systems where the collective behavior is rich (emergent), 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:

  • Precision and robustness of the dynamics of spatial path integration in the rodent entorhinal cortex
  • Principles and capacity of position encoding in rodent entorhinal cortex
  • Decoding or readout by hippocampus of position-related information from entorhinal cortex
  • Emergence of ultrasparse representations in neural networks, and of ultrasparse sequences in songbird vocal premotor regions
  • General synaptic rules for goal-directed learning in recurrent networks of conductance-based spiking neurons
  • Goal-directed sensorimotor song learning in songbirds
  • The role of sparse codes in learning in feedforward neural networks


Burak, Y. & Fiete, I.R. (2009) Accurate path integration in continuous attractor network models of grid cells. PLoS Comput Biol.5(2):e1000291


Welinder P.E., Burak Y., & Fiete I.R. (2008) Grid cells: The position code, neural network models of activity, and the problem of learning. Hippocampus, 18(12):1283-300. Review.


Murthy M., Fiete I. R., & Laurent G. (2008) Testing odor response stereotypy in the Drosophila mushroom body. Neuron 59(6):1009-23.

Fiete I.R., Burak Y., & Brookings T. (2008) What grid cells convey about rat location. J Neurosci 28: 6858-6871


Fiete I.R., Fee M.S., & Seung HS. (2007) Model of birdsong learning based on gradient estimation by dynamic perturbation of neural conductances. J Neurophysiol 98(4):2038-57.


Burak Y. & Fiete I.R. (2006) Do we understand the emergent dynamics of grid cell activity? J Neurosci 26:9352-9354.


Fiete I. R., & Seung H.S. (2006) Gradient learning in spiking neural networks by dynamic perturbation of conductances. Phys Rev Lett. 97(4):048104.

Fiete I.R., Hahnloser R.H., Fee M.S., & Seung H.S. (2004) Temporal sparseness of the premotor drive is important for rapid learning in a neural network model of birdsong. J Neurophysiol 92(4):2274-82.


Center for Learning and Memory


Semester         Course       Unique No.       Title

2016 Spr       NEU 466M       54555          Quant Mthds in Neuroscience I

2014 Fall       NEU 366M       56890          Quant Mthds in Neuroscience I

2013 Fall        NEU 366M      57865          Quant Mthds in Neurosciece I

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