Timezone: »
Poster
Optimal Neural Tuning Curves for Arbitrary Stimulus Distributions: Discrimax, Infomax and Minimum $L_p$ Loss
Zhuo Wang · Alan A Stocker · Daniel Lee
Wed Dec 05 07:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor
In this work we study how the stimulus distribution influences the optimal coding of an individual neuron. Closed-form solutions to the optimal sigmoidal tuning curve are provided for a neuron obeying Poisson statistics under a given stimulus distribution. We consider a variety of optimality criteria, including maximizing discriminability, maximizing mutual information and minimizing estimation error under a general $L_p$ norm. We generalize the Cramer-Rao lower bound and show how the $L_p$ loss can be written as a functional of the Fisher Information in the asymptotic limit, by proving the moment convergence of certain functions of Poisson random variables. In this manner, we show how the optimal tuning curve depends upon the loss function, and the equivalence of maximizing mutual information with minimizing $L_p$ loss in the limit as $p$ goes to zero.
Author Information
Zhuo Wang (Facebook Reality Labs)
Alan A Stocker (University of Pennsylvania)
Daniel Lee (Samsung Research/Cornell University)
More from the Same Authors
-
2020 Expo Talk Panel: Building Neural Interfaces: When Real and Artificial Neurons Meet »
Ricardo Monti · Nathalie T.H Gayraud · Jeffrey Seely · Zhuo Wang · Tugce Tasci · Rebekkah Hogan -
2017 : Poster Session (encompasses coffee break) »
Beidi Chen · Borja Balle · Daniel Lee · iuri frosio · Jitendra Malik · Jan Kautz · Ke Li · Masashi Sugiyama · Miguel A. Carreira-Perpinan · Ramin Raziperchikolaei · Theja Tulabandhula · Yung-Kyun Noh · Adams Wei Yu -
2017 Poster: Generative Local Metric Learning for Kernel Regression »
Yung-Kyun Noh · Masashi Sugiyama · Kee-Eung Kim · Frank Park · Daniel Lee -
2016 Poster: Human Decision-Making under Limited Time »
Pedro Ortega · Alan A Stocker -
2016 Poster: Efficient Neural Codes under Metabolic Constraints »
Zhuo Wang · Xue-Xin Wei · Alan A Stocker · Daniel Lee -
2016 Poster: Maximizing Influence in an Ising Network: A Mean-Field Optimal Solution »
Christopher W Lynn · Daniel Lee -
2014 Workshop: Novel Trends and Applications in Reinforcement Learning »
Csaba Szepesvari · Marc Deisenroth · Sergey Levine · Pedro Ortega · Brian Ziebart · Emma Brunskill · Naftali Tishby · Gerhard Neumann · Daniel Lee · Sridhar Mahadevan · Pieter Abbeel · David Silver · Vicenç Gómez -
2013 Poster: Optimal integration of visual speed across different spatiotemporal frequency channels »
Matjaz Jogan · Alan A Stocker -
2013 Poster: Optimal Neural Population Codes for High-dimensional Stimulus Variables »
Zhuo Wang · Alan A Stocker · Daniel Lee -
2012 Poster: Diffusion Decision Making for Adaptive k-Nearest Neighbor Classification »
Yung-Kyun Noh · Frank Park · Daniel Lee -
2012 Poster: Efficient coding connects prior and likelihood function in perceptual Bayesian inference »
Xue-Xin Wei · Alan A Stocker -
2010 Poster: Learning via Gaussian Herding »
Yacov Crammer · Daniel Lee -
2010 Poster: Generative Local Metric Learning for Nearest Neighbor Classification »
Yung-Kyun Noh · Byoung-Tak Zhang · Daniel Lee -
2008 Poster: Extended Grassmann Kernels for Subspace-Based Learning »
Jihun Hamm · Daniel Lee -
2007 Session: Session 8: Neuroscience I »
Alan A Stocker -
2007 Oral: Blind channel identification for speech dereverberation using l1-norm sparse learning »
Yuanqing Lin · Jingdong Chen · Youngmoo E Kim · Daniel Lee -
2007 Poster: Blind channel identification for speech dereverberation using l1-norm sparse learning »
Yuanqing Lin · Jingdong Chen · Youngmoo E Kim · Daniel Lee -
2007 Poster: A Bayesian Model of Conditioned Perception »
Alan A Stocker · Eero Simoncelli