When we learn a new motor skill, we have to contend with both the variability inherentin our sensors and the task. The sensory uncertainty can be reduced by using information about the distribution of previously experienced tasks.Here we impose a distribution on a novel sensorimotor task and manipulate the variability of the sensory feedback. We show that subjects internally represent both the distribution of the task as well as their sensory uncertainty. Moreover, they combine these two sources of information in a way that is qualitatively predicted by optimal Bayesian processing. We further analyze if the subjects can represent multimodal distributions such as mixtures of Gaussians. The results show that the CNS employs probabilistic models during sensorimotor learning even when the priors are multimodal.

We discuss an idea for collecting data in a relatively efficient manner. Our point of view is Bayesian and information-theoretic: on any given trial, we want to adaptively choose the input in such a way that the mutual information betweenthe (unknown) state of the system and the (stochastic) output is maximal, given any prior information (including data collected on any previous trials). We prove a theorem that quantifies the effectiveness ofthis strategy and give a few illustrative examples comparing the performance of this adaptive technique to that of the more usual nonadaptive experimentaldesign. For example, we are able to explicitly calculate the asymptotic relative efficiency of the "staircase method" widely employed inpsychophysics research, and to demonstrate the dependence of this efficiency on the form of the "psychometric function" underlying the output responses.

These experimentally-determined rules (usually called spike-time dependent plasticity or STDP rules), which are constantly being refined,18,3o have inspired substantialfurther theoretical work on their modeling and interpretation.2·9,l0,22·28·29·33 Figurel(Dl-Gl)* depict some of the main STDP findings/ of which the best-investigated are shown in figure l(Dl;El), and are variants of a'standard' STDP rule. Earlier work considered rate-based rather than spikebased temporalrules, and so we adopt the broader term'time dependent plasticity' or TDP. Note the strong temporal asymmetry in both the standard rules. Although the theoretical studies have provided us with excellent tools for modeling thedetailed consequences of different time-dependent rules, and understanding characteristicssuch as long-run stability and the relationship with non-temporal learning rules such as BCM,6 specifically computational ideas about TDP are rather thinner on the ground. Two main qualitative notions explored in various of the works cited above are that the temporal asymmetries inTDP rules are associated with causality or prediction. However, looking specifically at the standard STDP rules, models interested in prediction *We refer to graphs in this figure by row and column.

The connectivity of the nervous system of the nematode Caenorhabditis eleganshas been described completely, but the analysis of the neuronal basisof behavior in this system is just beginning. Here, we used an optimization algorithm to search for patterns of connectivity sufficient tocompute the sensorimotor transformation underlying C. elegans chemotaxis, a simple form of spatial orientation behavior in which turning probabilityis modulated by the rate of change of chemical concentration.

Dopamine exerts two classes of effect on the sustained neural activity in prefrontal cortex that underlies working memory. Direct release in the cortex increases the contrast of prefrontal neurons, enhancing the robustness ofstorage. Release of dopamine in the striatum is associated with salient stimuli and makes medium spiny neurons bistable; this modulation ofthe output of spiny neurons affects prefrontal cortex so as to indirectly gate access to working memory and additionally damp sensitivity tonoise. Existing models have treated dopamine in one or other structure, or have addressed basal ganglia gating of working memory exclusive ofdopamine effects. In this paper we combine these mechanisms and explore their joint effect. We model a memory-guided saccade task to illustrate how dopamine's actions lead to working memory that is selective forsalient input and has increased robustness to distraction.

We address in this paper the question of how the knowledge of the marginal distribution P (x) can be incorporated in a learning algorithm. We suggest three theoretical methods for taking into account this distribution for regularization and provide links to existing graph-based semi-supervised learning algorithms. We also propose practical implementations.

The problem of extracting the relevant aspects of data was addressed throughthe information bottleneck (IB) method, by (soft) clustering one variable while preserving information about another - relevance - variable. An interesting question addressed in the current work is the extension of these ideas to obtain continuous representations that preserve relevant information, rather than discrete clusters.We give a formal definition of the general continuous IB problem and obtain an analytic solution for the optimal representation forthe important case of multivariate Gaussian variables.

We compute approximate analytical bootstrap averages for support vector classificationusing a combination of the replica method of statistical physics and the TAP approach for approximate inference. We test our method on a few datasets and compare it with exact averages obtained by extensive Monte-Carlo sampling.

We argue that K-means and deterministic annealing algorithms for geometric clusteringcan be derived from the more general Information Bottleneck approach.If we cluster the identities of data points to preserve information about their location, the set of optimal solutions is massively degenerate. But if we treat the equations that define the optimal solution as an iterative algorithm, then a set of "smooth" initial conditions selects solutions with the desired geometrical properties. In addition to conceptual unification,we argue that this approach can be more efficient and robust than classic algorithms.

A general linear response method for deriving improved estimates of correlations in the variational Bayes framework is presented.