Multiple time scales in neuronal adaptation
Neural systems adapt and respond with a wide range of time scales, and display dynamics that are history dependent. These phenomena are observed on several levels of organization of neural systems, but the connections between these behaviors on different levels remain largely unknown. We study adaptation in neuronal systems theoretically and experimentally in collaboration with Prof. Shimon Marom from the Medical School, using networks of cortical neurons as an experimental model system.

Phenotypic variability in dividing cell populations
Populations of microorganisms, even those that are genetically identical, are characterized by phenotypic (non-genetic) variability. We study variability in the expression of proteins in population using a combination of theory and experiments. Theoretically we use methods of statistical mechanics to understand the sources of variability, steady state distributions and relaxation to steady state following perturbations. These studies are tightly connected to our experiments on yeast populations, in collaboration with Prof. Erez Braun from the Dept. of Physics.

Physiological adaptation in gene regulation
The genetic regulatory system contains a large potential for evolutionary change in organisms. Underlying these changes are the adaptive physiological capabilities of the regulatory network. These capabilities are revealed by studying the dynamics and statistics of regulated gene expression in populations for many generations under controlled conditions. Experimentally we focus on metabolic regulation in yeast cells as a model system, and study adaptation and selection in continuous culture to shed light on these processes (with Prof. Erez Braun).

T. Friedlander and N. Brenner, “Cellular properties and population asymptotics in the population balance equation”, Phys. Rev. Lett. 101, 018104 (2008).

S. Stern, T. Dror, E. Stolovicki, N. Brenner and E. Braun, “Genome-wide transcriptional plasticity underlies cellular adaptation to novel challenge”, Molecular Systems Biology 3:106(2007). See commentary by E.V.Koonin, Molecular Systems Biology 3:107(2007)

N. Brenner and Y. Shokef, “Non-equilibrium statistical mechanics of dividing cell polulations”, Submitted (April 2007)

E. Stolovicki, T. Dror, N. Brenner and E. Braun, “Synthetic gene-recruitment reveals adaptive reprogramming of gene regulation in yeast”, Genetic 173, 75-85 (2006).

N. Brenner, K. Farkash and E. Braun, “A population view of gene regulation”, Phys. Biol. 3, 172-182 (2006).

History dependent multiple time scale dynamics in a single neuron model. G. Gilboa, R. Chen and N. Brenner, , J. Neurosci. 25, 6479 (2005).

Transient responses and adaptation to steady state in a eukaryotic gene regulation system. E. Braun and N. Brenner, Phys. Biol. 1, 67-76 (2004).

Selective adaptation in networks of cortical neurons. D. Eytan, N. Brenner and S. Marom, J. Neurosci. 23, 9349 (2003).

Statistical properties of spike trains: universal and stimulus-dependent aspects. N. Brenner, O. Agam, W. Bialek and R. de Ruyter van Steveninck, Phys. Rev. E 66, 031907 (2002).

Universality and Individuality in a Neural Code. E. Schneidman, N. Brenner, N. Tishby, R. de Ruyter van Steveninck, W. Bialek, Neural Information Processing Systems (NIPS) (2000).

Adaptive Rescaling Optimizes Information Transmission. N. Brenner, W. Bialek, R. de Ruyter van Steveninck, Neuron 26 : 695 (2000).

Synergy in a Neural Code. N. Brenner, S.P. Strong, R. Koberle, W. Bialek, R. de Ruyter van Steveninck, Neur. Comp. 12 :1 (2000).