Ila Fiete

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Ila Fiete is an American physicist and computational neuroscientist as well as an Associate Professor in the Department of Brain and Cognitive Sciences within the McGovern Institute for Brain Research at the Massachusetts Institute of Technology. Fiete analyses neural data and builds theoretical models to uncover how neural circuits wire to perform computations and how the brain represents and manipulates information involved in memory and reasoning.

Fiete pursued her undergraduate studies at the University of Michigan, majoring in mathematics and physics.^[1] Fiete then moved to Boston to pursue her masters and graduate studies at Harvard University in the Department of Physics.^[1]  Fiete was mentored in computational neuroscience by Sebastian Seung at MIT^[2] and in physics by Daniel Fisher at Harvard.^[3] In her graduate degree, Fiete explored the principles of learning and coding in biological neural networks.^[4]

Fiete completed her graduate studies in 2003, and moved across the country to hold an appointment as a postdoctoral fellow at the Kavli Institute for Theoretical Physics at the University of California, Santa Barbara from 2004 until 2006.^[1] During this time, Fiete was also a visiting member of the Center for Theoretical Biophysics at the University of California, San Diego.^[1] From 2006 to 2008, Fiete served as a Broad Fellow in Brain Circuitry at Caltech under the mentorship of Christof Koch.^[5]

During her graduate studies, Fiete and her colleagues used linear networks of learning to show that sparse temporal neural codes minimize synaptic interference and facilitate learning in songbirds.^[6] Fiete then began to explore how the computational principles underlying synaptic plasticity.^[4] She proposed a rule that was able to perform goal-directed learning in recurrent neural networks where the rule performs stochastic gradient ascent on the reward.^[4] Specifically, if the reward signal quantifies network performance then the plasticity rule is able to drive goal-directed learning in the network.^[4] Fiete then tested her model on neurophysiological data of songbirds and they found that their model was fast enough to explain learning in songbirds.^[7]

When Fiete started her postdoctoral research, she began to study the coding principles of cells in the brain that encode location.  In 2006, Fiete and her colleagues described a framework to describe the computations of triangular lattice neurons in the entorhinal cortex that encode the positions of rats.^[8] They found that a computations based on a residue number system allow a small number of cells to represent and update a rats position.^[8] This hypothesis is very different from others of how coding is performed in the brain, and this “arithmetic-friendly” numeral system highlights the ingenuity of neural codes.^[8]

In 2008, Fiete joined the faculty at the University of Texas at Austin.^[1] While at UT Austin, Fiete made a significant impact on the community as both a researcher and an educator, receiving the 2013 CNS Excellence Award for Teaching from the College of Natural Sciences University of Texas, Austin.^[9] After serving on the faculty for 10 years, in 2018, Fiete accepted an offer from the Massachusetts Institute of Technology and became an assistant professor with tenure within the Department of Brain and Cognitive Sciences.^[1] In early 2019, Fiete joined the McGovern Institute at MIT as an Associate Investigator.^[8] Fiete's research program is centered around understanding why the brain contains particular coding properties and how the connectivity and dynamics of neural circuits and synaptic plasticity underlie such coding principals.^[10] Her lab uses numerical and theoretical modelling as well as raw neural data with which to test their models of brain computations.^[10]

Fiete and her colleagues at UT Austin were interested in exploring the neural computations underlying grid cells in the entorhinal cortex.^[11] Grid cells are known to encode the spatial location of an individual animal, and Fiete and her colleagues found that their computations can be modelled through continuous attractor networks.^[12] With only inputs regarding velocity and heading direction, continuous attractor models can generate triangular grid responses as are known to encode position in grid cells.^[11] They further show, with a proof of concept, that continuous attractor dynamics underlie the integration of velocity in grid cells.^[11] In 2013, Fiete and her colleagues used in vivo neural recordings as the basis for their computational investigation of the mechanisms underlying grid cell activity.^[13] Their model, relying on low-dimensional continuous attractor dynamics, reliably characterized grid cell responses in short duration, familiar enclosures.^[13] Over time and in changing conditions, individual grid cell responses change however, the grid parameter ratios and relative phases between simultaneously recorded cells stays essentially constant showing that population level responses are almost invariant.^[13] Their findings argue against the cell-environment hypothesis as they find that the stability of cell-cell responses is but more robust that cell-environment responses.^[13]

The following year, Fiete described a model to explain grid cell development, the moment of eye opening to fully developed grid cell computations.^[14] In the beginning, their model depicts initially unstructured networks of neurons spiking to velocity and location inputs.^[14] They propose, through computational modelling, that grid neurons develop an organized recurrent architecture based on the similarity of their inputs, acting through inhibitory neurons, and this lays the foundation for a mature grid cell network that can compute velocity and location in a coordinated and integrated fashion.^[14]

Fiete was then interested in developing a robust system with which to determine neural circuit mechanisms underlying brain function that do not merely rely on observing neural activity.^[15] Using the grid cell system, which Fiete had extensively probed and serves as a good system for testing computational models, Fiete showed that the "distribution of relative phase shifts" model has the potential to reveal highly detailed cortical circuit mechanisms from sparse neural recordings.^[15] Through the use of perturbative experiments, they find that their method is able to discriminate between feedforward and recurrent neural networks to uncover which model most accurately described neural computations.^[15]

In 2019, once Fiete had arrived at MIT, she published a paper using topological modelling to transform the neural activity of large populations of neurons into a data cloud representing the shape of a ring.^[16] This ring-like spatial representation of neural activity has been shown in flies to underlie head direction, and now, by Fiete, has been shown to represent head direction in mice - somewhat like an internal compass.^[16] The ring like shape that the neural activity creates is known as a manifold in computational analyses, a shape represented in multiple dimensions to depict multidimensional data.^[17] Its shape and dimensionality represent the data in a more interpretable way.^[17] The approach the Fiete describes, using a manifold to depict neural activity, enables blind discovery and decoding of specific variables using only neural activity as an input.^[17]

• 2018 CIFAR Senior Fellow^[10]
• 2016 Howard Hughes Faculty Scholar^[18]
• 2013 CNS Excellence Award for Teaching - College of Natural Sciences University of Texas at Austin^[9]
• 2011-2013 McKnight Scholar^[19]
• 2013 Office of Naval Research Young Investigator^[20]
• 2010 Searle Scholar^[21]
• 2009 Alfred P. Sloan Foundation Fellow in Neuroscience^[22]

• Trettel SG, Trimper JB, Hwaun E, Fiete IR, Colgin LL. Grid cell co-activity patterns during sleep reflect spatial overlap of grid fields during active behaviors. Nature Neuroscience. 22: 609–617. PMID 30911183 DOI: 10.1038/s41593-019-0359-6^[23]
• Widloski J, Fiete IR. Inferring circuit mechanisms from sparse neural recording and global perturbation in grid cells. Elife. 7. PMID 29985132 DOI: 10.7554/eLife.33503^[23]
• Computational principles of memory. Chaudhuri, R., Fiete, I. (2016). Nature Neuroscience 19, 394–403.^[24]
• Widloski J, Fiete IR. A model of grid cell development through spatial exploration and spike time-dependent plasticity. Neuron. 83: 481–95. PMID 25033187 DOI: 10.1016/j.neuron.2014.06.018^[23]
• Widloski J, Fiete I. How does the brain solve the computational problems of spatial navigation? Space, Time and Memory in the Hippocampal Formation. 373–407. DOI: 10.1007/978-3-7091-1292-2_14^[23]
• Specific evidence of low-dimensional continuous attractor dynamics in grid cells. Yoon, K., Buice, M.A., Barry, C., Hayman, R., Burgess, N., Fiete, I.R. (2013). Nature Neuroscience 16, 1077–1084.^[24]
• Accurate path integration in continuous attractor network models of grid cells. Burak, Y., Fiete, I.R. (2009). PLoS Computational Biology 5, e1000291.^[24]
• Fiete IR, Fee MS, Seung HS. Model of birdsong learning based on gradient estimation by dynamic perturbation of neural conductances. Journal of Neurophysiology. 98: 2038–57. PMID 17652414 DOI: 10.1152/jn.01311.2006^[23]
• Fiete IR, Seung HS. Gradient learning in spiking neural networks by dynamic perturbation of conductances. Physical Review Letters. 97: 048104. PMID 16907616 DOI: 10.1103/PhysRevLett.97.048104^[23]