Publications

2019
C. Pehlevan and D. B. Chklovskii, “Neuroscience-inspired online unsupervised learning algorithms,” IEEE Signal Processing Magazine, vol. 36, no. 6, pp. 88-96, 2019. PDF
C. Pehlevan, “A Spiking Neural Network with Local Learning Rules Derived From Nonnegative Similarity Matching,” in International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019. PDF
2018
V. Minden, C. Pehlevan, and D. B. Chklovskii, “Biologically Plausible Online Principal Component Analysis Without Recurrent Neural Dynamics,” in 2018 52nd Asilomar Conference on Signals, Systems, and Computers, 2018.
A. Giovannucci, V. Minden, C. Pehlevan, and D. B. Chklovskii, “Efficient Principal Subspace Projection of Streaming Data Through Fast Similarity Matching,” in IEEE International Conference on Big Data , 2018.
G. Guralnik, Z. Guralnik, and C. Pehlevan, “Holography, Fractals and the Weyl Anomaly,” arXiv preprint arXiv:1802.05362, 2018.
C. Pehlevan, F. Ali, and B. P. Ölveczky, “Flexibility in motor timing constrains the topology and dynamics of pattern generator circuits,” Nature communications, vol. 9, no. 1, pp. 977, 2018.
A. Sengupta, M. Tepper, C. Pehlevan, A. Genkin, and D. Chklovskii, “Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks,” in Advances in neural information processing systems (NeurIPS), 2018.
C. Pehlevan, A. M. Sengupta, and D. B. Chklovskii, “Why do similarity matching objectives lead to Hebbian/anti-Hebbian networks?Neural computation, vol. 30, no. 1, pp. 84–124, 2018.
2017
C. Pehlevan, S. Mohan, and D. B. Chklovskii, “Blind nonnegative source separation using biological neural networks,” Neural computation, vol. 29, no. 11, pp. 2925–2954, 2017.
C. Pehlevan, A. Genkin, and D. B. Chklovskii, “A clustering neural network model of insect olfaction,” in 2017 51st Asilomar Conference on Signals, Systems, and Computers, 2017, pp. 593–600.
C. Pehlevan and A. Sengupta, “Resource-efficient perceptron has sparse synaptic weight distribution,” in Signal Processing and Communications Applications Conference (SIU), 2017 25th, 2017, pp. 1–4.
2016
R. Abbasi-Asl, C. Pehlevan, B. Yu, and D. Chklovskii, “Do retinal ganglion cells project natural scenes to their principal subspace and whiten them?” in Signals, Systems and Computers, 2016 50th Asilomar Conference on, 2016, pp. 1641–1645.
C. Pehlevan, P. Paoletti, and L. Mahadevan, “Integrative neuromechanics of crawling in D. melanogaster larvae,” Elife, vol. 5, pp. e11031, 2016.
Y. Chen, C. Pehlevan, and D. B. Chklovskii, “Self-calibrating neural networks for dimensionality reduction,” in Signals, Systems and Computers, 2016 50th Asilomar Conference on, 2016, pp. 1488–1495.
2015
T. M. Otchy, et al., “Acute off-target effects of neural circuit manipulations,” Nature, vol. 528, no. 7582, pp. 358, 2015.
C. Pehlevan, T. Hu, and D. B. Chklovskii, “A Hebbian/Anti-Hebbian Neural Network for Linear Subspace Learning: A Derivation from Multidimensional Scaling of Streaming Data,” Neural computation, vol. 27, no. 7, pp. 1461–1495, 2015.
C. Pehlevan and D. Chklovskii, “A normative theory of adaptive dimensionality reduction in neural networks,” in Advances in neural information processing systems (NeurIPS), 2015, pp. 2269–2277.
C. Pehlevan and D. B. Chklovskii, “Optimization theory of hebbian/anti-hebbian networks for pca and whitening,” in Communication, Control, and Computing (Allerton), 2015 53rd Annual Allerton Conference on, 2015, pp. 1458–1465.
2014
Z. Guralnik, C. Pehlevan, and G. Guralnik, “On exact statistics and classification of ergodic systems of integer dimension,” Chaos, vol. 24, no. 2, pp. 023125, 2014.
C. Pehlevan and D. B. Chklovskii, “A Hebbian/Anti-Hebbian Network Derived from Online Non-Negative Matrix Factorization Can Cluster and Discover Sparse Features,” in Signals, Systems and Computers, 2014 48th Asilomar Conference on, 2014, pp. 769–775.

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