David Schwab Assistant Professor of Physics & Astronomy

Research Interests

Theoretical Biological Physics, Collective Computation in Cell Populations and Neural Networks, Modeling of Cellular Sensory and Signaling Networks, Neural Information Processing, Learning and Adaptation.

Our lab works in the area of biological physics, focusing on applications of statistical physics and nonlinear dynamics to problems in biology. The lab's current interests include how computations such as memory and attention are performed by networks of neurons, how cellular populations communicate and coordinate their behavior during development, how neural systems encode stimuli efficiently and implement error correction in the face of noise, and the role of spatial structure in evolution.

To approach these questions, we employ a diverse set of analytical and computational tools including statistical mechanics, dynamical systems theory, machine learning, and information theory. Furthermore, we collaborate closely with experimental colleagues working on a variety of systems ranging from the social amoebae Dictyostelium discoideum to retinal ganglion cell processing of visual input. Among the products of these partnerships is the design of novel experiments and the creation of innovative data analysis methods.

Selected Publications

Specific Wiring of Distinct Amacrine Cells in the Directionally Selective Retinal Circuit Permits Independent Coding of Direction and Size. Hoggarth A, McLaughlin AJ, Ronellenfitch K, Trenholm S, Vasandani R, Sethuramanujam S, Schwab D, Briggman KL, and Awatramani GB. Neuron. 2015 April 8;86(1):276-291.

From intracellular signaling to population oscillations: bridging size‐ and time‐scales in collective behavior. Sgro AE, SchwabDJ, Noorbakhsh J, Mestler T, Mehta P, and Gregor T. Molecular Systems Biology. 2015 January 1;11(1):779.

Nonlinear dendritic integration of electrical and chemical synaptic inputs drives fine-scale correlations. Trenholm S, McLaughlin AJ, Schwab DJ, Turner MH, Smith RG, Rieke F, and Awatramani GB. Nature Neuroscience. 2014 December;17(12):1759-1766.

Quantifying the Role of Population Subdivision in Evolution on Rugged Fitness Landscapes. Bitbol A-F and Schwab DJ. PLoS Computational Biology. 2014 August 14;10(8):e1003778.

Zipf’s Law and Criticality in Multivariate Data without Fine-TuningSchwab DJ, Nemenman I, and Mehta P. Physical Review Letters. 2014 August 8;113(6):068102.

Constant Growth Rate Can Be Supported by Decreasing Energy Flux and Increasing Aerobic Glycolysis. Slavov N, Budnik BA,Schwab D, Airoldi EM, and van Oudenaarden A. Cell Reports. 2014 May 8;7(3):705-714.

Dynamic Tuning of Electrical and Chemical Synaptic Transmission in a Network of Motion Coding Retinal Neurons. Trenholm S, McLaughlin AJ, Schwab DJ, and Awatramani GB. Journal of Neuroscience. 2013 September 11;33(37):14927-14938.

Lag normalization in an electrically coupled neural network. Trenholm S, Schwab DJ, Balasubramanian V, and Awatramani GB.Nature Neuroscience. 2013 February;16(2):154-156.

Kuramoto model with coupling through an external mediumSchwab DJ, Plunk GG, and Mehta P. Chaos. 2012 December;22(4):043139.

Energetic costs of cellular computation. Mehta P and Schwab DJ. PNAS. 2012 October 30;109(44):17978-17982.

View all publications by David Schwab listed in the National Library of Medicine (PubMed).