Adaptation is a widespread phenomenon in nervous systems, providing flexibility to function under varying external conditions. Here, we relate an adaptive property of a sensory system directly to its function as a carrier of information about input signals. We show that the input/output relation of a sensory system in a dynamic environment changes with the statistical properties of the environment. Specifically, when the dynamic range of inputs changes, the input/output relation rescales so as to match the dynamic range of responses to that of the inputs. We give direct evidence that the scaling of the input/output relation is set to maximize information transmission for each distribution of signals. This adaptive behavior should be particularly useful in dealing with the intermittent statistics of natural signals.

We show that the information carried by compound events in neural spike trains-patterns of spikes across time or across a population of cells-can be measured, independent of assumptions about what these patterns might represent. By comparing the information carried by a compound pattern with the information carried independently by its parts, we directly measure the synergy among these parts. We illustrate the use of these methods by applying them to experiments on the motion-sensitive neuron H1 of the fly's visual system, where we confirm that two spikes close together in time carry far more than twice the information carried by a single spike. We analyze the sources of this synergy and provide evidence that pairs of spikes close together in time may be especially important patterns in the code of H1.

A key property of neural systems is their ability to adapt selectively to stimuli with different features. Using multisite electrical recordings from networks of cortical neurons developing ex vivo, we show that neurons adapt selectively to different stimuli invading the network. We focus on selective adaptation to frequent and rare stimuli; networks were stimulated at two sites with two different stimulus frequencies. When both stimuli were presented within the same period, neurons in the network attenuated their responsiveness to the more frequent input, whereas their responsiveness to the rarely delivered stimuli showed a marked average increase. The amplification of the response to rare stimuli required the presence of the other, more frequent stimulation source. By contrast, the decreased response to the frequent stimuli occurred regardless of the presence of the rare stimuli. Analysis of the response of single units suggests that both of these effects are caused by changes in synaptic transmission. By using synaptic blockers, we find that the increased responsiveness to the rarely stimulated site depends specifically on fast GABAergic transmission. Thus, excitatory synaptic depression, the inhibitory sub-network, and their balance play an active role in generating selective gain control. The observation that selective adaptation arises naturally in a network of cortical neurons developing ex vivo indicates that this is an inherent feature of spontaneously organizing cortical networks.

By recruiting the essential HIS3 gene to the GAL regulatory system and switching to a repressing glucose medium, we confronted yeast cells with a novel challenge they had not encountered before along their history in evolution.Adaptation to this challenge involved a global transcriptional response of a sizeable fraction of the genome, which relaxed on the time scale of the population adaptation, of order of 10 generations.For a large fraction of the responding genes there is no simple biological interpretation, connecting them to the specific cellular demands imposed by the novel challenge.Strikingly, repeating the experiment did not reproduce similar transcription patterns neither in the transient phase nor in the adapted state in glucose.These results suggest that physiological selection operates on the new metabolic configurations generated by the non-specific large scale transcriptional response to eventually stabilize an adaptive state.

The recruitment of a gene to a foreign regulatory system is a major evolutionary event that can lead to novel phenotypes. However, the evolvability potential of cells depends on their ability to cope with challenges presented by gene recruitment. To study this ability, we combined synthetic gene recruitment with continuous culture and online measurements of the metabolic and regulatory dynamics over long timescales. The gene HIS3 from the histidine synthesis pathway was recruited to the GAL system, responsible for galactose utilization in the yeast S. cerevisiae. Following a switch from galactose to glucose-from induced to repressed conditions of the GAL system-in histidine-lacking chemostats (where the recruited HIS3 is essential), the regulatory system reprogrammed to adaptively tune HIS3 expression, allowing the cells to grow competitively in pure glucose. The adapted state was maintained for hundreds of generations in various environments. The timescales involved and the reproducibility of separate experiments render spontaneous mutations an unlikely underlying mechanism. Essentially all cells could adapt, excluding selection over a genetically variable population. The results reveal heritable adaptation induced by the exposure to glucose. They demonstrate that genetic regulatory networks have the potential to support highly demanding events of gene recruitment.

Long-term, repeated measurements of individual synaptic properties have revealed that synapses can undergo significant directed and spontaneous changes over time scales of minutes to weeks. These changes are presumably driven by a large number of activity-dependent and independent molecular processes, yet how these processes integrate to determine the totality of synaptic size remains unknown. Here we propose, as an alternative to detailed, mechanistic descriptions, a statistical approach to synaptic size dynamics. The basic premise of this approach is that the integrated outcome of the myriad of processes that drive synaptic size dynamics are effectively described as a combination of multiplicative and additive processes, both of which are stochastic and taken from distributions parametrically affected by physiological signals. We show that this seemingly simple model, known in probability theory as the Kesten process, can generate rich dynamics which are qualitatively similar to the dynamics of individual glutamatergic synapses recorded in long-term time-lapse experiments in ex-vivo cortical networks. Moreover, we show that this stochastic model, which is insensitive to many of its underlying details, quantitatively captures the distributions of synaptic sizes measured in these experiments, the long-term stability of such distributions and their scaling in response to pharmacological manipulations. Finally, we show that the average kinetics of new postsynaptic density formation measured in such experiments is also faithfully captured by the same model. The model thus provides a useful framework for characterizing synapse size dynamics at steady state, during initial formation of such steady states, and during their convergence to new steady states following perturbations. These findings show the strength of a simple low dimensional statistical model to quantitatively describe synapse size dynamics as the integrated result of many underlying complex processes.

The copy number of any protein fluctuates among cells in a population; characterizing and understanding these fluctuations is a fundamental problem in biophysics. We show here that protein distributions measured under a broad range of biological realizations collapse to a single non-Gaussian curve under scaling by the first two moments. Moreover in all experiments the variance is found to depend quadratically on the mean, showing that a single degree of freedom determines the entire distribution. Our results imply that protein fluctuations do not reflect any specific molecular or cellular mechanism, and suggest that some buffering process masks these details and induces universality.The protein content of a cell is a primary determinant of its phenotype. However, protein copy number is subject to large cell-to-cell variation even among genetically identical cells grown under uniform conditions ([1-3] and references therein). This variation has been the subject of intensive research in recent years ([4-7] and references therein). Much of this previous work was devoted to characterizing the stochastic properties of various processes underlying gene expression, such as transcription and translation [8], or different stages of the cell cycle [9], and understanding their effect on protein variation. However, gene expression is generally coupled to all aspects of cell physiology, such as growth [10], metabolism [11], aging [12], division [13,14] and epigenetic processes [15,16], as well as gene location and function [17], all of which were shown to affect protein variation. The emerging picture is of a plethora of correlated mechanisms at different levels of organization; how they integrate to shape the total protein variation in a dividing population remains an open question [11,14].In this work we addressed this question by a phenomenological approach. We measured distributions of highly expressed proteins in proliferating clonal populations of bacteria and yeast under natural conditions, where gene expression is coupled to other cellular processes. By designing an array of different metabolic and regulatory conditions as well as growth environments, we collected a compendium of measurements which systematically covers the major processes of gene expression and 2 cell division, and compared the measured distributions in a wide range of biological realizations. More specifically, our comparisons included: (a) Two archetypical microorganisms, bacteria and yeast, with two well-studied regulatory systems of essential metabolic pathways: the LAC operon in E. coli [18] and the GAL system in S. cerevisiae [19]. Both systems were studied under environmental conditions in which expression is strongly coupled to metabolism, namely they control the utilization of an essential sugar (lactose and galactose, respectively) as the sole carbon The spectrum of our experiments spans an array of "control parameters" p which covers many of the essential processes affecting protein content in cells. The two organisms used, E. coli an...

Synaptic plasticity - the directed modulation of synaptic connections by specific activity histories or physiological signals - is believed to be a major mechanism for the modification of neuronal network function. This belief, however, has a 'flip side': the supposition that synapses do not change spontaneously in manners unrelated to such signals. Contrary to this supposition, recent studies reveal that synapses do change spontaneously, and to a fairly large extent. Here we review experimental results on spontaneous synaptic remodeling, its relative contributions to total synaptic remodeling, its statistical characteristics, and its physiological importance. We also address challenges it poses and avenues it opens for future experimental and theoretical research.