D. Obeid, J. Zavatone-Veth, and C. Pehlevan, “Statistical structure of the trial-to-trial timing variability in synfire chains,” biorXiv , 2020.
K. Vogt, et al., “Internal state configures olfactory behavior and early sensory processing in Drosophila larva,” biorXiv, 2020.
J. Grimaud, W. Dorrell, C. Pehlevan, and V. Murthy, “Bilateral alignment of receptive fields in the olfactory cortex points to non-random connectivity,” bioRxiv, 2020.
S. Qin, N. Mudur, and C. Pehlevan, “Supervised Deep Similarity Matching,” arXiv preprint arXiv:2002.10378, 2020.
B. Bordelon, A. Canatar, and C. Pehlevan, “Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks,” arXiv preprint arXiv:2002.02561, 2020.
A. Erdogan and C. Pehlevan, “Blind Bounded Source Separation Using Neural Networks with Local Learning Rules,” in International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020.
H. Sikka*, W. Zhong*, J. Yin, and C. Pehlevan, “A closer look at disentangling in β-VAE,” in 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019.
D. Obeid, H. Ramambason, and C. Pehlevan, “Structured and Deep Similarity Matching via Structured and Deep Hebbian Networks,” in Advances in Neural Information Processing Systems (NeurIPS) , 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.
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.
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.
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.
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.