Papers

Preprints

R. Schaeffer, M. Khona, L. Meshulam, The International Brain Laboratory, and I.R. Fiete. Reverse-engineering Recurrent Neural Network solutions to a hierarchical inference task for mice. bioRxiv (2020). link

M. Khona and I.R. Fiete. Memories from patterns: Attractor and integrator networks in the brain. Submitted, Nature Reviews Neuroscience (2020).
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M. Yim, L. Sadun, I.R. Fiete, T. Taillefumier. Where can a place cell put its fields? Let us count the ways. In review (2020); bioRxiv 10.1101/2019.12.19.881458 (2019). link

The International Brain Laboratory, V. Aguillon-Rodriguez, D.E. Angelaki,  H.M. Bayer, N. Bonacchi, M. Carandini, F. Cazettes, G.A. Chapuis,  A.K. Churchland, Y.Dan, E.E. Dewitt, M. Faulkner, H. Forrest, L.M. Haetzel, M. Hausser, S.B. Hofer, F. Hu, A. Khanal, C.S. Krasniak, I. Laranjeira, Z.F. Mainen, G.T. Meijer, N.J. Miska, T.D. Mrsic-Flogel, M. Murakami, J.-P. Noel, A. Pan-Vazquez, J.I. Sanders, K.Z. Socha, R. Terry, A.E. Urai, H.M. Vergara, M.J. Wells, C.J. Wilson, I.B. Witten, L.E. Wool, A. Zador. A standardized and reproducible method to measure decision-making in mice. bioRxiv (2020).
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B. Kriener*, R. Chaudhuri*, I.R. Fiete. Robust parallel decision-making in neural circuits with nonlinear inhibition. In review (2020);  bioRxiv 10.1101/231753 (2019).[*co-first author]
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R. Chaudhuri and I.R. Fiete. Associative content-addressable networks with exponentially many robust stable states. arXiv:1704.02019 [q-bio.NC] (2017).
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Published

A. Das and I.R. Fiete. Systematic errors in connectivity inferred from activity in strongly coupled recurrent circuits. Accepted, Nature Neuroscience (2020); bioRxiv 10.1101/512053 (2019). 
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M. Stangl*, I. Kanitscheider*, M. Riemer, I.R. Fiete+, and T. Wolbers+. Sources of path integration error in young and aging humans. Nature Communications (2020). [*co-first author; +co-senior author]
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M. Klukas, M. Lewis, I.R. Fiete. Flexible representation and memory of higher-dimensional cognitive variables with grid cells. PLoS Comp. Biol. (2020).
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R. Chaudhuri, I.R. Fiete. Bipartite expander Hopfield networks as self-decoding high-capacity error correcting codes. Proc. NeurIPS (2019). link

R. Chaudhuri, B. Gercek*, B. Pandey*, A. Peyrache, I.R. Fiete. The intrinsic population dynamics of a canonical cognitive circuit. Nature Neurosci. (2019). [*co-second author]
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S.G. Trettel*, J.B. Trimper*, E. Hwaun, I.R. Fiete, L.L. Colgin. Grid cell co-activity patterns during sleep reflect spatial overlap of grid fields during active behaviors. Nature Neurosci. 22, 609–617 (2019). [*co-first author]
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C. Roth, I. Kanitscheider, I.R. Fiete. Kernel RNN Learning (KeRNL). Proceedings of ICLR (2019).
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Y. Gu, S. Lewallen, A. Kinkhabwala, C. Domnisoru, K. Yoon, J. Gauthier, I.R. Fiete, and D.W. Tank. A map-like micro-organization of grid cells in the medial entorhinal cortex. Cell 175, 736–750 (2018).
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J. Widloski, M. Marder, and I.R. Fiete. Inferring circuit mechanisms from sparse neural recording and global perturbation in grid cells. eLife 10.7554/eLife.33503.001 (2018).
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I. Kanitscheider and I.R. Fiete. Emergence of dynamically reconfigurable hippocampal responses by learning to perform probabilistic spatial reasoning. bioRxiv 10.1101/231159 (2017).
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The International Brain Laboratory (consortium including the Fiete lab). An International Laboratory for Systems and Computational Neuroscience. Neuron (2017).
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I. Kanitscheider and I.R. Fiete. Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems. In Advances in NIPS (2017).
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O.O. Koyluoglu, Y. Pertzov, S. Manohar, M. Husain, I.R. Fiete. Fundamental bound on the persistence and capacity of short-term memory stored as graded persistent activity. eLife 2017;6:e22225 (2017).
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I. Kanitscheider and I.R. Fiete. Toward a comprehensive functional understanding of the brain’s spatial navigation system. Curr. Opinion in Systems Biol. 3 186-194 (2017).
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R. Chaudhuri and I.R. Fiete. Computational principles of memory. Nature Neurosci. 19, 394-403 (2016).
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K Yoon*, S. Lewallen*, A. Kinkhabwalla, D.W. Tank+ and I.R. Fiete+. Grid cell responses in 1D environments assessed as slices through a 2D lattice. Neuron 89(5), 1086-1099 (2016) [*co-first author; +co-senior author]
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Y. Yoo, O.O. Koyluoglu, S. Vishwanath, I. R. Fiete. Multi-periodic neural coding for adaptive information transfer. Theoretical Computer Science 633, 37-53 (2016).
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X. Chen, Q. He, J.W. Kelly, I.R. Fiete and T.P. McNamara. Bias in human path integration is predicted by properties of grid cells. Current Biology 25, 1771–1776 (2015).
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I. Fiete, D. Schwab and N.M. Tran. A binary Hopfield network with 1/log(n) information rate and applications to grid cell decoding. Workshop paper for Biological Distributed Algorithms, Austin TX (2014).
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J. Widloski and I. R. Fiete. A Model of Grid Cell Development through Spatial Exploration and Spike Time-Dependent Plasticity. Neuron 83(2): 481–495 (2014).
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K. Yoon, M. Buice, C. Barry, N. Burgess, and I. R. Fiete. Specific evidence of low dimensional continuous attractor dynamics in grid cells. Nature Neurosci. 16, 1077-1084 doi:10.1038/nn.3450 (2013).
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J. Widloski and I. R. Fiete. How does the brain solve the computational problems of spatial navigation? Bookchapter in Space, Time, and Memory in the Hippocampal Formation. Eds. D. Derdikman and J. Knierim. Springer-Verlag. (2013).
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Y. Burak and I. R. Fiete. Fundamental limits on persistent activity in networks of noisy neurons. PNAS 109 (43): 17645-17650 (2012).
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Y. Yoo, O. O. Koyluoglu, S. Vishwanath and I. R. Fiete. Dynamic shift-map coding with side information at the decoder. 50th Annual Allerton Conference on Communication, Control, and Computing (2012).
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S. Sreenivasan and I. R. Fiete. Grid cells generate an analog error-correcting code for singularly precise neural computation. Nature Neurosci. 14, 1330-1337 doi:10.1038/nn.2901 (2011).
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I. R. Fiete. Losing phase. Neuron 66(3): 331-34 (2010).
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I. R. Fiete, W. Senn, C. Wang, R. H. R. Hahnloser. Spike time-dependent plasticity and heterosynaptic competition organize networks to produce long scale-free sequences of neural activity. Neuron 65(4): 563-576 (2010).
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Y. Burak and I. R. Fiete. Accurate path integration in continuous attractor network models of grid cells. PLoS Comp. Biol. 5(2) (2009).
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I. R. Fiete and H. S. Seung. Birdsong Learning. In Encyclopedia of Neuroscience  (L. Squire, Editor). Amsterdam: Elsevier Academic Press, pp. 227-239 (2009). (Originally available online 2008 on Science Direct.)
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M. Murthy, I. R. Fiete, and G. Laurent. Testing odor response stereotypy in the Drosophila mushroom body. Neuron 59(6):1009-23 (2008).
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  • Related preview: S. Cachero and G. Jefferis. Drosophila olfaction: The end of stereotypy? Neuron 59(6): 843-845 (2008).
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P.E. Welinder, Y. Burak and I. R. Fiete. Grid cells: The position code, neural network models of activity, and the problem of learning. Hippocampus 18(12):1283-300 (2008).
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I. R. Fiete, Y. Burak and T. Brookings. What grid cells encode about rat position. J. Neuroscience 28, 6856-6871 (2008).
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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).
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Y. Burak and I. R. Fiete. Do we understand the emergent dynamics of grid cell activity? J. Neuroscience 26, 9352-9354 (2006).
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I. R. Fiete and H. S. Seung. Gradient learning in spiking neural networks by dynamic perturbation of conductances. Physical Review Letters 97, 048104 (2006).
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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).
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S. Sullow, I.R. Prasad, M.C. Aronson et al. Metallization and magnetic order in EuB_6. Physical Review B 62, 11626 (2000).
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S. Sullow, I.R. Prasad, S. Bogdanovich et al. Magnetotransport in the low carrier density ferromagnet EuB_6. J. Applied Physics 87, 5591 (2000).
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S. Sullow, I.R. Prasad, M.C. Aronson et al. Magnetic order of EuB_6. Physical Review B 57, 5860 (1998).
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