Integrated Calendar

We’ve all watched as ice forms in thin sheets on our car’s windshield (ok that sentence is probably more relevant for the crowds in Ithaca NY than Rehovot Israel but you get the point). What would it look like to shrink down to the size of an atom and slow things down so that you could watch as molecules join one another to form a crystal? We have recently gotten a glimpse of this process by looking at freezing using a model system that can be observed directly through the microscope. Using colloidal suspensions that consist of micron sized solid particles suspended in a solvent, we have reproduced the conditions that lead to crystallization. The particles are Brownian so that the suspension as a whole behaves as a thermal system governed by the laws of statistical mechanics. In this talk I will describe how we use various experimental techniques to investigate the structure and dynamics of these systems and gain an understanding of epitaxial growth, defect nucleation, and defect translation in crystals.

We have taken an engineering approach to extending the lifespan of C. elegans. Specifically, our goal was to use bioengineering in the nematode C. elegans to generate animals that are long-lived but that develop normally, are fertile, and are generally healthy throughout most of their life. By examining the literature describing various mechanisms that may drive aging, we created a list of candidate genes or components to be expressed in worms and extend lifespan. These included genes derived from a 100 times longer-living vertebrate, zebrafish, encoding novel molecular functions not normally present in worms. Thus, our approach to extending lifespan is unique in that we expanded the pool of components to include functions not found in the C. elegans genome. Next, we used a modular approach to further extend lifespan by co-expressing a number of genes in combinations. While expressing individual genes extended lifespan between 30-50%, combining two genes furthered this extension to 60-80%. Combining three genes resulted in 80-100% lifespan extension and the combination of four genes resulted in 130% extension, also yielding information about the extent of cross-talk between the different processes that drive aging . These results suggest that a modular approach could be used as a scheme to build worms having progressively longer lifespans. applying an engineering approach to aging is a powerful strategy that goes beyond the constraints of the native genome to create animals with increased lifespan and healthspan.

We consider networks whose dynamics is described by a rate equation, with transitions that are induced by a bath (wnm) plus a driving source (Intensity*gnm). By "sparsity" or "glassiness" we mean that the couplings (gnm) are distributed over several orders of magnitude. Novel physics arise in the analysis of the energy absorption and the non-equilibrium steady state.
Keywords: Resistor network picture; Percolation; Variable range hopping; Semi-linear response; Fluctuation-Dissipation relation; Sinai diffusion.
[1] D. Hurowitz, D. Cohen, EPL 93, 60002 (2011).
[2] D. Hurowitz, S. Rahav, D. Cohen, EPL 98, 20002 (2012).
[3] Y. de Leeuw, D. Cohen, arXiv:1206.2495

The spiking of dopamine neurons in animals, and apparently analogous BOLD signals at dopaminergic targets in humans, appear to report predictions of future reward. Prominent computational theories of these responses suggest that they both support and reflect trial-and-error learning about which actions have been successful, based on simple associations with past rewards. This is essentially a neural implementation of Thorndike's (1911) behaviorist principle that reinforced behaviors should be repeated. However, it has long been known that organisms are not condemned merely to repeat previously successful actions, but instead that even rodents' decisions can under some circumstances reflect other sorts of knowledge about task structure and contingencies. The neural and computational bases for these additional effects, and their interaction with the putative reinforcement systems in the basal ganglia, are poorly understood.
Such interactions are of considerable practical importance because, for instance, disorders of compulsion in humans, such as substance abuse, are thought to arise from runaway reinforcement processes unfettered by more deliberative influences.

I first discuss how such extra-reinforcement effects – e.g., planning novel routes based on cognitive maps, or incorporating "counterfactual" feedback about foregone actions – can be incorporated in the framework of existing computational theories, via algorithms for “model-based reinforcement learning." Rather than learning about actions' past successes directly, such algorithms learn a representation of the task structure, and can use it to evaluate candidate actions via mental simulation of their consequences. This computational characterization allows reasoning about (and explaining empirical data concerning) under which circumstances the brain might efficiently adopt either this strategy or the reinforcement one. It also allows quantifying and dissociating either strategy's effects on decision making and associated neural signaling.

Next, I discuss human fMRI experiments characterizing these influences in learning tasks. By fitting computational models to decision behavior and BOLD signals, we demonstrate that neither choices nor (putatively dopamine-related) BOLD signals in striatum can be explained by past reinforcement alone, but instead that both reflect additional learning and reasoning about task structure and contingencies. That such influences are prominent even at the level of striatum challenges current models of the computations there and suggest that the system is a common target for many different sorts of learning. Additional experiments examine individual variation in the tendency to employ either system; the patterns of both spontaneous and experimentally induced variation suggest that the dominance of model-based decision influence over simpler reinforcement systems employs cognitive control mechanisms that have previously been studied in other areas of cognitive neuroscience. Finally, I report results showing that patients with several disorders involving compulsion show abnormally reinforcement-bound choices on our tasks, supporting a link between these neurocomputational learning mechanisms and pathological habits.