Papers

Preprints

Akhilan Boopathy & I. R. Fiete. Gradient-trained weights in wide neural networks align layerwise to error-scaled input correlations. arXiv (2021). link

M. Khona and I.R. Fiete. Memories from patterns: Attractor and integrator networks in the brain. Resubmitted, Nature Reviews Neuroscience (2021).
pdf 

J.H.J. Kim, I.R. Fiete, D. Schwab. Superlinear precision and memory in simple population codes. arXiv (2020). link

R. Chaudhuri and I.R. Fiete. Associative content-addressable networks with exponentially many robust stable states. arXiv:1704.02019 [q-bio.NC] (2017).
link

I. Kanitscheider and I.R. Fiete. Emergence of dynamically reconfigurable hippocampal responses by learning to perform probabilistic spatial reasoning. bioRxiv 10.1101/231159 (2017).
link

Published (by year)

R. Schaeffer, B. Bordelon, M. Khona, W. Pan, I.R. Fiete. Efficient online inference for nonparametric mixture models. Conference on Uncertainty in AI, In Proc. Machine Learning Research (2021). pdf

M. Yim, L. Sadun, I.R. Fiete, T. Taillefumier. Place cell capacity and volatility with grid-like inputs. Elife (2021). pdf

The International Brain Laboratory, et al. Standardized and reproducible measurement of decision-making in mice. eLife 10:e63711 (2021). link

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

L.F. Abbott, D.D. Bock, E.M. Callaway, W. Denk, C. Dulac, A.L. Fairhall, I.R. Fiete, K.M. Harris, M. Helmstaedter, V. Jain, N. Kasthuri, Y. LeCun, J.W. Lichtman, P.B. Littlewood, L. Luo, J.H.R. Maunsell, R.C. Reid, B.R. Rosen, G.M. Rubin, T.J. Sejnowski, H.S. Seung, K. Svoboda, D.W. Tank, D. Tsao, D.C. Van Essen. The mind of a mouse. Cell 182(6) (2020). link

B. Kriener*, R. Chaudhuri*, I.R. Fiete. Robust parallel decision-making in neural circuits with nonlinear inhibition. PNAS (2020). [*co-first author]
link

A. Das and I.R. Fiete. Systematic errors in connectivity inferred from activity in strongly coupled recurrent circuits. Nature Neurosci. (2020). 
link | bioRxiv link

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]
link

M. Klukas, M. Lewis, I.R. Fiete. Flexible representation and memory of higher-dimensional cognitive variables with grid cells. PLoS Comp. Biol. (2020).
link

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]
link | bioRxiv link

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]
link | pdf

C. Roth, I. Kanitscheider, I.R. Fiete. Kernel RNN Learning (KeRNL). Proceedings of ICLR (2019).
link 

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).
link | pdf

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).
link

The International Brain Laboratory (consortium including the Fiete lab). An International Laboratory for Systems and Computational Neuroscience. Neuron (2017).
link |pdf

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).
link

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).
link

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).
link | pdf

R. Chaudhuri and I.R. Fiete. Computational principles of memory. Nature Neurosci. 19, 394-403 (2016).
link | pdf

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]
link | pdf

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).
link | pdf

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).
link | pdf

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).
pdf

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).
link | pdf | SI pdf

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).
link | pdf

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).
pdf

Y. Burak and I. R. Fiete. Fundamental limits on persistent activity in networks of noisy neurons. PNAS 109 (43): 17645-17650 (2012).
pdf | SI pdf

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).
pdf

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).
pdf | SI pdf

I. R. Fiete. Losing phase. Neuron 66(3): 331-34 (2010).
pdf

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).
pdf

Y. Burak and I. R. Fiete. Accurate path integration in continuous attractor network models of grid cells. PLoS Comp. Biol. 5(2) (2009).
pdf

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.)
pdf

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

  • Related preview: S. Cachero and G. Jefferis. Drosophila olfaction: The end of stereotypy? Neuron 59(6): 843-845 (2008).
    pdf

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).
pdf

I. R. Fiete, Y. Burak and T. Brookings. What grid cells encode about rat position. J. Neuroscience 28, 6856-6871 (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

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

Categorized by topic

Biologically plausible gradient learning

Akhilan Boopathy & I. R. Fiete. Gradient-trained weights in wide neural networks align layerwise to error-scaled input correlations. arXiv (2021). link

C. Roth, I. Kanitscheider, I.R. Fiete. Kernel RNN Learning (KeRNL). Proceedings of ICLR (2019).
link 

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.)
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

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

Unsupervised learning for structure emergence

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).
pdf

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).
link | pdf | SI pdf

Continuous attractors in the brain

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]
link | bioRxiv link

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]
link | pdf

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).
link | pdf

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]
link | pdf

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).
link | pdf

Exponential-capacity codes, error-correcting codes with modular representations

M. Yim, L. Sadun, I.R. Fiete, T. Taillefumier. Place cell capacity and volatility with grid-like inputs. Elife (2021). pdf

M. Klukas, M. Lewis, I.R. Fiete. Flexible representation and memory of higher-dimensional cognitive variables with grid cells. PLoS Comp. Biol. (2020).
link

R. Chaudhuri, I.R. Fiete. Bipartite expander Hopfield networks as self-decoding high-capacity error correcting codes. Proc. NeurIPS (2019). link

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).
link | pdf

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).
pdf

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).
pdf

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).
pdf | SI pdf

I. R. Fiete, Y. Burak and T. Brookings. What grid cells encode about rat position. J. Neuroscience 28, 6856-6871 (2008).
pdf

Inferring + measuring neural connectivity

L.F. Abbott, D.D. Bock, E.M. Callaway, W. Denk, C. Dulac, A.L. Fairhall, I.R. Fiete, K.M. Harris, M. Helmstaedter, V. Jain, N. Kasthuri, Y. LeCun, J.W. Lichtman, P.B. Littlewood, L. Luo, J.H.R. Maunsell, R.C. Reid, B.R. Rosen, G.M. Rubin, T.J. Sejnowski, H.S. Seung, K. Svoboda, D.W. Tank, D. Tsao, D.C. Van Essen. The mind of a mouse. Cell 182(6) (2020). link

A. Das and I.R. Fiete. Systematic errors in connectivity inferred from activity in strongly coupled recurrent circuits. Nature Neurosci. (2020). 
link | bioRxiv link

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).
link

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

  • Related preview: S. Cachero and G. Jefferis. Drosophila olfaction: The end of stereotypy? Neuron 59(6): 843-845 (2008).
    pdf

Memory

R. Chaudhuri and I.R. Fiete. Computational principles of memory. Nature Neurosci. 19, 394-403 (2016).
link | pdf

Y. Burak and I. R. Fiete. Fundamental limits on persistent activity in networks of noisy neurons. PNAS 109 (43): 17645-17650 (2012).
pdf | SI pdf

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).
link

R. Schaeffer, B. Bordelon, M. Khona, W. Pan, I.R. Fiete. Efficient online inference for nonparametric mixture models. Conference on Uncertainty in AI, In Proc. Machine Learning Research (2021). pdf

Decision making

The International Brain Laboratory, et al. Standardized and reproducible measurement of decision-making in mice. eLife 10:e63711 (2021). link

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

B. Kriener*, R. Chaudhuri*, I.R. Fiete. Robust parallel decision-making in neural circuits with nonlinear inhibition. PNAS (2020). [*co-first author]
link

The International Brain Laboratory (consortium including the Fiete lab). An International Laboratory for Systems and Computational Neuroscience. Neuron (2017).
link |pdf

Navigation circuits

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]
link

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).
link

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).
link

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).
link | pdf

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).
link | pdf

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).
link | pdf | SI pdf

‘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).
pdf

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).
pdf | SI pdf

I. R. Fiete, Y. Burak and T. Brookings. What grid cells encode about rat position. J. Neuroscience 28, 6856-6871 (2008).
pdf

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).
pdf

Y. Burak and I. R. Fiete. Accurate path integration in continuous attractor network models of grid cells. PLoS Comp. Biol. 5(2) (2009).
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. Losing phase. Neuron 66(3): 331-34 (2010).
pdf

Experimental condensed-matter physics

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