I. Nemenman

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Thermodynamic Computing pdf

Christof Teuscher, James P. Crutchfield, Seth Lloyd, 2019, ArXiv.

Complexity through nonextensivity

Naftali Tishby, W. Bialek, I. Nemenman, 2001, physics/0103076.

Optimal Signal Processing in Small Stochastic Biochemical Networks

I. Nemenman, E. Ziv, C. Wiggins, 2006, PloS one.

Information Transduction Capacity of Noisy Biochemical Signaling Networks

I. Nemenman, R. Cheong, A. Rhee, 2011, Science.

Entropy and information in neural spike trains: progress on the sampling problem.

William Bialek, Ilya Nemenman, Rob de Ruyter van Steveninck, 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

Inference of entropies of discrete random variables with unknown cardinalities

I. Nemenman, 2002, physics/0207009.

Limits to the precision of gradient sensing with spatial communication and temporal integration

I. Nemenman, A. Levchenko, A. Mugler, 2015, Proceedings of the National Academy of Sciences.

Cell–cell communication enhances the capacity of cell ensembles to sense shallow gradients during morphogenesis

I. Nemenman, A. Levchenko, P. Amar, 2015, Proceedings of the National Academy of Sciences.

Genome-wide Identification of Post-translational Modulators of Transcription Factor Activity in Human B-Cells

Mariano J. Alvarez, Adam A. Margolin, K. Basso, 2009, Nature Biotechnology.

Automated adaptive inference of phenomenological dynamical models

Bryan C. Daniels, Bryan C. Daniels, Ilya Nemenman, 2015, Nature Communications.

The simplicity of completion time distributions for common complex biochemical processes

I. Nemenman, B. Munsky, G. Bel, 2009, Physical biology.

Reverse engineering cellular networks

Adam A. Margolin, I. Nemenman, A. Califano, 2006, Nature Protocols.

Neural Coding of Natural Stimuli: Information at Sub-Millisecond Resolution

William Bialek, Ilya Nemenman, Geoffrey D. Lewen, 2008, PLoS computational biology.

Information theory and learning: a physical approach

Ilya Nemenman, I. Nemenman, 2000, ArXiv.

Mesoscopic statistical properties of multistep enzyme-mediated reactions.

Bryan C. Daniels, N A Sinitsyn, I. Nemenman, 2008, IET systems biology.

Multivariate dependence and genetic networks inference.

Adam A. Margolin, I. Nemenman, A. Califano, 2010, IET systems biology.

Population-expression models of immune response

I. Nemenman, R. Antia, S. Stromberg, 2012, Physical biology.

Potential and field singularity at a surface point charge

Ilya Nemenman, Ilya Mandel, I. Nemenman, 2003, math-ph/0306039.

A large number of receptors may reduce cellular response time variation

Ilya Nemenman, Lina Merchan, I. Nemenman, 2012, Physical biology.

Constrained brain volume in an efficient coding model explains the fraction of excitatory and inhibitory neurons in sensory cortices

C. Rozell, I. Nemenman, Arish Alreja, 2020, bioRxiv.

Cellular noise and information transmission.

I. Nemenman, A. Levchenko, 2014, Current opinion in biotechnology.

Cell-cell communication enhances the capacity of cell ensembles to sense shallow gradients during morphogenesis

I. Nemenman, A. Levchenko, P. Amar, 2015, bioRxiv.

Effects of receptor correlations on molecular information transmission.

I. Nemenman, M. Tchernookov, Vijay Singh, 2016, Physical review. E.

Of fishes and birthdays: Efficient estimation of polymer configurational entropies

M. Wall, C. Strauss, I. Nemenman, 2015, 1502.02364.

Gain control in molecular information processing: lessons from neuroscience

I. Nemenman, 2011, Physical biology.

Fitness in time-dependent environments includes a geometric phase contribution

I. Nemenman, S. Tanase-Nicola, 2011, Journal of The Royal Society Interface.

Networks Information Transduction Capacity of Noisy Biochemical Signaling

Ilya Nemenman, Andre Levchenko, Alex Rhee, 2011 .

Universal Properties of Concentration Sensing in Large Ligand-Receptor Networks.

Ilya Nemenman, I. Nemenman, Vijay Singh, 2019, Physical review letters.

RESEARCHARTICLE Growth of bacteria in 3-d colonies

B. Levin, I. Nemenman, A. Mugler, 2022 .

Single variant bottleneck in the early dynamics of H. influenzae bacteremia in neonatal rats questions the theory of independent action

B. Levin, I. Nemenman, Xinxian Shao, 2016, bioRxiv.

Role of spatial averaging in multicellular gradient sensing

I. Nemenman, A. Levchenko, A. Mugler, 2015, Physical biology.

Receptor crosstalk improves concentration sensing of multiple ligands

T. Mora, I. Nemenman, Gautam Reddy, 2018, bioRxiv.

Coarse-graining stochastic biochemical networks: quasi-stationary approximation and fast simulations using a stochastic path integral technique

N. Hengartner, I. Nemenman, N. Sinitsyn, 2008, 0808.4016.

Physical limit to concentration sensing in a changing environment

T. Mora, I. Nemenman, 2019, bioRxiv.

Neural Coding of Natural Stimuli: Information at Sub-Millisecond Resolution

W. Bialek, I. Nemenman, R. R. de Ruyter van Steveninck, 2008, PLoS computational biology.

Low probability states, data statistics, and entropy estimation

I. Nemenman, A. Roman, Dami'an G. Hern'andez, 2022, ArXiv.

Entropy and Inference, Revisited

W. Bialek, I. Nemenman, F. Shafee, 2001, NIPS.

On the interaction of point charges in an arbitrary domain

I. Nemenman, I. Mandel, A. Silbergleit, 2001, physics/0105052.

On the detectability of quantum spacetime foam with gravitational-wave interferometers

I. Nemenman, J. Overduin, D. Santiago, 1999, gr-qc/9909017.

M N / 0 51 00 30 v 2 2 M ar 2 00 6 Genome-wide Discovery of Modulators of Transcriptional Interactions in Human B Lymphocytes

Adam A. Margolin, I. Nemenman, A. Califano, 2006 .

Complexity through nonextensivity

Naftali Tishby, W. Bialek, I. Nemenman, 2001, physics/0103076.

Coarse-graining stochastic biochemical networks: adiabaticity and fast simulations

I. Nemenman, N. Hengartner, N. Sinitsyn, 2008 .

Generative probabilistic matrix model of data with different low-dimensional linear latent structures

I. Nemenman, P. Fleig, 2022, 2212.02987.

Avalanche scaling in large neural populations with distributed coupling to multiple dynamical latent variables

Mia C. Morrell, I. Nemenman, A. Sederberg, 2023, ArXiv.

Coincidences And Entropies of Random Variables with Very Large Alphabets

I. Nemenman, 2005 .

Inference of entropies of discrete random variables with unknown cardinalities

I. Nemenman, 2002, physics/0207009.

Randomly connected networks generate emergent selectivity and predict decoding properties of large populations of neurons

I. Nemenman, A. Sederberg, 2019, bioRxiv.

Director Field Model of the Primary Visual Cortex for Contour Detection

Ilya Nemenman, Martin Tchernookov, Rebecca Butterfield, 2013, PloS one.

Fluctuation-Dissipation Theorem and Models of Learning

Ilya Nemenman, I. Nemenman, 2004, Neural Computation.

Process pathway inference via time series analysis

Chris Wiggins, Ilya Nemenman, I. Nemenman, 2002, physics/0206031.

Predictability, Complexity, and Learning

Naftali Tishby, Ilya Nemenman, William Bialek, 2000, Neural Computation.

Reverse-engineering biological networks from large data sets

Joseph L. Natale, David Hofmann, Damian G. Hern'andez, 2017, bioRxiv.

ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context

Chris Wiggins, Ilya Nemenman, Andrea Califano, 2004, BMC Bioinformatics.

Increased adaptability to sudden environmental change can more than make up for the two-fold cost of males

Caroline M Holmes, Daniel B. Weissman, Caroline M. Holmes, 2018, bioRxiv.

Specificity and completion time distributions of biochemical processes.

Brian Munsky, Ilya Nemenman, Golan Bel, 2009, The Journal of chemical physics.

The Berry phase and the pump flux in stochastic chemical kinetics

I. Nemenman, N. Sinitsyn, 2006, q-bio/0612018.

Genome-Wide Discovery of Modulators of Transcriptional Interactions in Human B Lymphocytes

Kai Wang, Ilya Nemenman, Andrea Califano, 2006, RECOMB.

Reverse Engineering of the Yeast Transcriptional Network Using the ARACNE algorithm

Ilya Nemenman, Andrea Califano, Adam A. Margolin, 2004 .

Learning Continuous Distributions: Simulations With Field Theoretic Priors

William Bialek, Ilya Nemenman, W. Bialek, 2000, NIPS.

Genotype to Phenotype Mapping and the Fitness Landscape of the E. coli lac Promoter

Ilya Nemenman, Jakub Otwinowski, I. Nemenman, 2012, PloS one.

Luria–Delbrück, revisited: the classic experiment does not rule out Lamarckian evolution

Anzar Abbas, Ilya Nemenman, Caroline M Holmes, 2017, Physical biology.

Speeding up Evolutionary Search by Small Fitness Fluctuations

Ilya Nemenman, Jakub Otwinowski, Sorin Tanase-Nicola, 2010, Journal of statistical physics.

Fitness in fluctuating environments

I. Nemenman, Sorin Tanase Nicola, 2011 .

Determinants of bistability in induction of the Escherichia coli lac operon.

W S Hlavacek, M E Wall, M. Wall, 2008, IET systems biology.

Information theory and adaptation

Ilya Nemenman, I. Nemenman, 2010, 1011.5466.

Quantifying evolvability in small biological networks.

A Mugler, E Ziv, I Nemenman, 2008, IET systems biology.

Quantifying evolvability in small biological

E. Ziv, I. Nemenman, I. Nemenman, 2009 .

Reconstruction of Metabolic Networks from High‐Throughput Metabolite Profiling Data

Ilya Nemenman, William S Hlavacek, Michael E Wall, 2007, Annals of the New York Academy of Sciences.

Zipf's law and criticality in multivariate data without fine-tuning.

David J Schwab, Ilya Nemenman, Pankaj Mehta, 2013, Physical review letters.

Model Cortical Association Fields Account for the Time Course and Dependence on Target Complexity of Human Contour Perception

Ilya Nemenman, Garrett T. Kenyon, Steven P. Brumby, 2011, PLoS Comput. Biol..

Precise spatial memory in local random networks.

Joseph L. Natale, Ilya Nemenman, H. George E. Hentschel, 2019, Physical review. E.

Precise spatial spatial memory in local random networks

Joseph L. Natale, H. Hentschel, I. Nemenman, 2019, bioRxiv.

Coincidences and Estimation of Entropies of Random Variables with Large Cardinalities

Ilya Nemenman, I. Nemenman, 2011, Entropy.

Chance, long tails, and inference in a non-Gaussian, Bayesian theory of vocal learning in songbirds

David Hofmann, Baohua Zhou, Ilya Nemenman, 2017, Proceedings of the National Academy of Sciences.

Chance, long tails, and inference: a non-Gaussian, Bayesian theory of vocal learning in songbirds

David Hofmann, Ilya Nemenman, Samuel J. Sober, 2017, bioRxiv.

Inferring phenomenological models of first passage processes

I. Nemenman, Catalina Rivera, David Hofmann, 2020, PLoS Comput. Biol..

Predictive Information in a Nonequilibrium Critical Model

Ilya Nemenman, I. Nemenman, M. Tchernookov, 2012, 1212.3896.

Millisecond-Scale Motor Encoding in a Cortical Vocal Area

Ilya Nemenman, Claire Tang, Diala Chehayeb, 2014, PLoS biology.

Estimation of mutual information for real-valued data with error bars and controlled bias

Ilya Nemenman, Caroline M. Holmes, I. Nemenman, 2019, bioRxiv.

How is sensory information processed?

Ilya Nemenman, I. Nemenman, 2004, ArXiv.

Latent dynamical variables produce signatures of spatiotemporal criticality in large biological systems

Ilya Nemenman, Audrey J. Sederberg, Mia C. Morrell, 2020, bioRxiv.

Unsupervised Bayesian Ising Approximation for decoding neural activity and other biological dictionaries

I. Nemenman, S. Sober, D. G. Hernández, 2022, eLife.

Minimal subtraction and the Callan-Symanzik equation

M. Raamsdonk, I. Nemenman, V. Periwal, 1998, hep-th/9802181.

Time-dependent corrections to effective rate and event statistics in Michaelis-Menten kinetics.

I. Nemenman, N. Sinitsyn, 2010, IET systems biology.

Adiabatic coarse-graining and simulations of stochastic biochemical networks

Ilya Nemenman, Nicolas Hengartner, N A Sinitsyn, 2009, Proceedings of the National Academy of Sciences.

On the influence of the gravitating vacuum on the dynamics of homogeneous isotropic models in gauge theories of gravity

I. Nemenman, A. V. Minkevich, 1995 .

Stereotypical Escape Behavior in Caenorhabditis elegans Allows Quantification of Effective Heat Stimulus Level

Ilya Nemenman, William S. Ryu, Kawai Leung, 2016, PLoS Comput. Biol..

Efficient Inference of Parsimonious Phenomenological Models of Cellular Dynamics Using S-Systems and Alternating Regression

Bryan C. Daniels, Ilya Nemenman, Bryan C. Daniels, 2014, PloS one.

Explicit Green's function of a boundary value problem for a sphere and trapped flux analysis in Gravity Probe B experiment

I. Nemenman, A. Silbergleit, I. M. Nemenman, 1999, math-ph/9901006.

Growth of bacteria in 3-d colonies

Ilya Nemenman, Andrew Mugler, Bruce R. Levin, 2016, PLoS Comput. Biol..

The Fifth Annual q-bio Conference on Cellular Information Processing

Brian Munsky, Ilya Nemenman, Tomasz Lipniacki, 2012 .

Neural criticality from effective latent variables

Mia C. Morrell, I. Nemenman, A. Sederberg, 2023, ArXiv.

Novel Computing Paradigms: Quo Vadis?

Christof Teuscher, Ilya Nemenman, F. J. Alexander, 2008 .

Motor control by precisely timed spike patterns

Ilya Nemenman, Caroline M Holmes, Caroline M. Holmes, 2016, Proceedings of the National Academy of Sciences.

Occam factors and model-independent Bayesian learning of continuous distributions

William Bialek, Ilya Nemenman, W. Bialek, 2000, Physical review. E, Statistical, nonlinear, and soft matter physics.

Simple biochemical networks allow accurate sensing of multiple ligands with a single receptor

Ilya Nemenman, Vijay Singh, I. Nemenman, 2017, PLoS Comput. Biol..

Neural Coding of Natural Stimuli: Information at Sub-Millisecond Resolution

William Bialek, Ilya Nemenman, Robert R. de Ruyter van Steveninck, 2007, BMC Neuroscience.

On the Sufficiency of Pairwise Interactions in Maximum Entropy Models of Networks

Ilya Nemenman, Lina Merchan, I. Nemenman, 2015, Journal of Statistical Physics.

Inferring Local Structure from Pairwise Correlations

I. Nemenman, Mahajabin Rahman, 2023, ArXiv.

Universal geometric theory of mesoscopic stochastic pumps and reversible ratchets.

Ilya Nemenman, N A Sinitsyn, I. Nemenman, 2007, Physical review letters.

Receptor crosstalk improves concentration sensing of multiple ligands

Thierry Mora, Ilya Nemenman, Gautam Reddy, 2018, bioRxiv.

Automated, predictive, and interpretable inference of Caenorhabditis elegans escape dynamics

Bryan C. Daniels, Ilya Nemenman, Bryan C. Daniels, 2018, Proceedings of the National Academy of Sciences.

Statistical properties of large data sets with linear latent features.

Ilya Nemenman, Philipp Fleig, I. Nemenman, 2021, Physical review. E.

Automated adaptive inference of coarse-grained dynamical models in systems biology

Bryan C. Daniels, Bryan C. Daniels, Ilya Nemenman, 2014, 1404.6283.

Chance, long tails, and inference in a non-Gaussian, Bayesian theory of vocal learning in songbirds

I. Nemenman, S. Sober, I. Pinkoviezky, 2017, Proceedings of the National Academy of Sciences.

Unsupervised Bayesian Ising Approximation for revealing the neural dictionary in songbirds

Ilya Nemenman, Samuel J. Sober, Dami'an G. Hern'andez, 2019, bioRxiv.

Intrinsic Motivation in Dynamical Control Systems

Naftali Tishby, D. Polani, I. Nemenman, 2022, arXiv.org.

Estimation of mutual information for real-valued data with error bars and controlled bias

Ilya Nemenman, Caroline M. Holmes, I. Nemenman, 2019, bioRxiv.

Cellular Noise and Information Transmission This Review Comes from a Themed Issue on Systems Biology Emergence of Biological Noise in Single Cell Behavior

Christian M. Metallo, I. Nemenman, V. Sourjik, 2022 .

Genome-Wide Discovery of Modulators of Transcriptional Interactions in Human B Lymphocytes

Adam A. Margolin, I. Nemenman, A. Califano, 2006, RECOMB.

Simultaneous Dimensionality Reduction: A Data Efficient Approach for Multimodal Representations Learning

I. Nemenman, Eslam Abdelaleem, Ahmed Roman, 2023, 2310.04458.

Maximum Likelihood Estimators For Colony Forming Units

I. Nemenman, Nicole M. Vega, K. Martini, 2023, bioRxiv.

Deep Variational Multivariate Information Bottleneck - A Framework for Variational Losses

I. Nemenman, Eslam Abdelaleem, K. M. Martini, 2023, ArXiv.

Extrinsic vs Intrinsic Criticality in Systems with Many Components

I. Nemenman, Wave Ngampruetikorn, David J. Schwab, 2023, ArXiv.

Unsupervised Bayesian Ising Approximation for revealing the neural dictionary in songbirds

I. Nemenman, S. Sober, Dami'an G. Hern'andez, 2019, bioRxiv.

Millisecond Spike Timing Codes for Motor Control

L. Ting, S. Sponberg, I. Nemenman, 2018, Trends in Neurosciences.

Quantitative Biology: Theory, Computational Methods, and Models

Terrence J. Sejnowski, Alan S. Perelson, Marco S. Nobile, 2018 .

A dynamical model of C. elegans thermal preference reveals independent excitatory and inhibitory learning pathways

I. Nemenman, W. Ryu, A. Roman, 2023, Proceedings of the National Academy of Sciences of the United States of America.

Cell–cell communication enhances the capacity of cell ensembles to sense shallow gradients during morphogenesis

I. Nemenman, A. Levchenko, P. Amar, 2015, Proceedings of the National Academy of Sciences.

Ergodic and non-ergodic anomalous diffusion in coupled stochastic processes

Ilya Nemenman, Golan Bel, I. Nemenman, 2009, 0901.4785.

Gain control in molecular information processing: lessons from neuroscience

I. Nemenman, 2011, Physical biology.

Data Efficiency, Dimensionality Reduction, and the Generalized Symmetric Information Bottleneck

I. Nemenman, K. M. Martini, 2023, Neural Computation.