Reinforcement learning models have long promised to unify computational, psychologicaland neural accounts of appetitively conditioned behavior. However,the bulk of data on animal conditioning comes from free-operant experiments measuring how fast animals will work for reinforcement. Existingreinforcement learning (RL) models are silent about these tasks, because they lack any notion of vigor. They thus fail to address thesimple observation that hungrier animals will work harder for food, as well as stranger facts such as their sometimes greater productivity evenwhen working for irrelevant outcomes such as water. Here, we develop an RL framework for free-operant behavior, suggesting that subjects choose how vigorously to perform selected actions by optimally balancing the costs and benefits of quick responding.

We propose a new Bayesian method for spatial cluster detection, the "Bayesian spatial scan statistic," and compare this method to the standard (frequentist) scan statistic approach. We demonstrate that the Bayesian statistic has several advantages over the frequentist approach, including increased power to detect clusters and (since randomization testing is unnecessary) much faster runtime. We evaluate the Bayesian and frequentist methodson the task of prospective disease surveillance: detecting spatial clusters of disease cases resulting from emerging disease outbreaks. Wedemonstrate that our Bayesian methods are successful in rapidly detecting outbreaks while keeping number of false positives low.

We present a nonlinear, simple, yet effective, feature subset selection method for regression and use it in analyzing cortical neural activity. Our algorithm involves a feature-weighted version of the k-nearest-neighbor algorithm. It is able to capture complex dependency of the target function onits input and makes use of the leave-one-out error as a natural regularization. We explain the characteristics of our algorithm on synthetic problemsand use it in the context of predicting hand velocity from spikes recorded in motor cortex of a behaving monkey. By applying feature selectionwe are able to improve prediction quality and suggest a novel way of exploring neural data.

An analog focal-plane processor having a 128 128 photodiode array has been developed for directional edge filtering. It can perform 4 4-pixel kernel convolution for entire pixels only with 256 steps of simple analog processing.Newly developed cyclic line access and row-parallel processing scheme in conjunction with the "only-nearest-neighbor interconnects" architecturehas enabled a very simple implementation. A proof-of-conceptchip was fabricated in a 0.35-m 2-poly 3-metal CMOS technology and the edge filtering at a rate of 200 frames/sec.

This paper presents a novel technique for analyzing electromagnetic imaging data obtained using the stimulus evoked experimental paradigm. The technique is based on a probabilistic graphical model, which describes thedata in terms of underlying evoked and interference sources, and explicitly models the stimulus evoked paradigm.

This paper provides a system-level analysis of a scalable distributed sensing modelfor networked sensors. In our system model, a data center acquires datafrom a bunch of L sensors which each independently encode their noisy observations of an original binary sequence, and transmit their encoded data sequences to the data center at a combined rate R, which is limited. Supposing that the sensors use independent LDGM rate distortion codes,we show that the system performance can be evaluated for any given finite R when the number of sensors L goes to infinity. The analysis shows how the optimal strategy for the distributed sensing problem changesat critical values of the data rate R or the noise level.

Long-distance language modeling is important not only in speech recognition and machine translation, but also in high-dimensional discrete sequence modeling in general.

There is a longstanding controversy on the site of the cerebellar motor learning. Different theories and experimental results suggest that either the cerebellar flocculus or the brainstem learns the task and stores the memory. With a dynamical system approach, we clarify the mechanism of transferring the memory generated in the flocculus to the brainstem and that of so-called savings phenomena. The brainstem learning must comply with a sort of Hebbian rule depending on Purkinje-cell activities. In contrast to earlier numerical models, our model is simple but it accommodates explanationsand predictions of experimental situations as qualitative features of trajectories in the phase space of synaptic weights, without fine parameter tuning.

The network topology of neurons in the brain exhibits an abundance of feedback connections, but the computational function of these feedback connections is largely unknown. We present a computational theory that characterizes the gain in computational power achieved through feedback in dynamical systems with fading memory. It implies that many such systems acquire through feedback universal computational capabilities for analog computing with a non-fading memory. In particular, we show that feedback enables such systems to process time-varying input streams in diverse ways according to rules that are implemented through internal states of the dynamical system. In contrast to previous attractor-based computational models for neural networks, these flexible internal states are high-dimensional attractors of the circuit dynamics, that still allow the circuit state to absorb new information from online input streams. In this way one arrives at novel models for working memory, integration of evidence, and reward expectation in cortical circuits. We show that they are applicable to circuits of conductance-based Hodgkin-Huxley (HH) neurons with high levels of noise that reflect experimental data on invivo conditions.

We extend radial basis function (RBF) networks to the scenario in which multiple correlated tasks are learned simultaneously, and present the corresponding learningalgorithms. We develop the algorithms for learning the network structure, in either a supervised or unsupervised manner. Training data may also be actively selected to improve the network's generalization totest data. Experimental results based on real data demonstrate the advantage of the proposed algorithms and support our conclusions.