Inferior temporal cortex (IT) neurons have large receptive fields when a single effective object stimulus is shown against a blank background, but have much smaller receptive fields when the object is placed in a natural scene. Thus, translation invariant object recognition is reduced in natural scenes, and this may help object selection. We describe a model which accounts for this by competition within an attractor in which the neurons are tuned to different objects in the scene, and the fovea has a higher cortical magnification factor than the peripheral visual field. Furthermore, weshow that top-down object bias can increase the receptive field size, facilitating object search in complex visual scenes, and providing a model of object-based attention. The model leads to the prediction that introduction of a second object into a scene with blank background will reduce the receptive field size to values that depend on the closeness of the second object to the target stimulus. We suggest that mechanisms of this type enable the output of IT to be primarily about one object, so that the areas that receive from IT can select the object as a potential target for action.

Hebbian learning rules are generally formulated as static rules.

Neural activity appears to be a crucial component for shaping the receptive fieldsof cortical simple cells into adjacent, oriented subregions alternately receivingON-and OFFcenter excitatory geniculate inputs. It is known that the orientation selective responses of V1 neurons are refined by visual experience. After eye opening, the spatiotemporal structure of neural activity in the early stages of the visual pathway depends both on the visual environment and on how the environment is scanned. We have used computational modeling to investigate how eye movements might affect the refinement of the orientation tuning of simple cells in the presence ofa Hebbian scheme of synaptic plasticity. Levels of correlation between theactivity of simulated cells were examined while natural scenes were scanned so as to model sequences of saccades and fixational eye movements, such as microsaccades, tremor and ocular drift. The specific patterns of activity required for a quantitatively accurate development of simple cell receptive fields with segregated ON and OFF subregions were observed during fixational eye movements, but not in the presence of saccades or with static presentation of natural visual input.

The usual observable at the level of neuronal populations is the populationaveraged instantaneousfiring rate A(t), with A(t)6.t

Acetylcholine (ACh) has been implicated in a wide variety of tasks involving attentional processes and plasticity. Following extensive animal studies, it has previously been suggested that ACh reports on uncertainty and controls hippocampal, cortical and cortico-amygdalar plasticity. We extend this view and consider its effects on cortical representational inference, arguing that ACh controls the balance between bottom-up inference, influenced by input stimuli, and top-down inference, influenced by contextual information. We illustrate our proposal using a hierarchical hidden Markovmodel.

Driven by the progress in the field of single-trial analysis of EEG, there is a growing interest in brain computer interfaces (BCIs), i.e., systems that enable human subjects to control a computer only by means of their brain signals. In a pseudo-online simulation our BCI detects upcoming finger movements in a natural keyboard typing condition and predicts their laterality. Thiscan be done on average 100-230 ms before the respective key is actually pressed, i.e., long before the onset of EMG. Our approach is appealing for its short response time and high classification accuracy ( 96%) in a binary decision where no human training is involved. We compare discriminative classifiers like Support Vector Machines (SVMs) and different variants of Fisher Discriminant that possess favorable regularization propertiesfor dealing with high noise cases (inter-trial variablity).

In this paper we explore two quantitative approaches to the modelling of counterfactual reasoning - a linear and a noisy-OR model - based on information containedin conceptual dependency networks. Empirical data is acquired in a study and the fit of the models compared to it. We conclude byconsidering the appropriateness of nonparametric approaches to counterfactual reasoning, and examining the prospects for other parametric approachesin the future.

Animal data on delayed-reward conditioning experiments shows a striking property - the data for different time intervals collapses into a single curve when the data is scaled by the time interval. This is called the scalar property of interval timing. Here a simple model of a neural clock is presented and shown to give rise to the scalar property. The model is an accumulator consisting of noisy, linear spiking neurons. It is analytically tractable and contains only three parameters.

The Association for the Advancement of Artificial Intelligence (AAAI) presented the AAAI-02 Workshop Program on Sunday and Monday, 28-29 July 2002 at the Shaw Convention Center in Edmonton, Alberta, Canada. The AAAI-02 workshop program included 18 workshops covering a wide range of topics in AI. The workshops were Agent-Based Technologies for B2B Electronic-Commerce; Automation as a Caregiver: The Role of Intelligent Technology in Elder Care; Autonomy, Delegation, and Control: From Interagent to Groups; Coalition Formation in Dynamic Multiagent Environments; Cognitive Robotics; Game-Theoretic and Decision-Theoretic Agents; Intelligent Service Integration; Intelligent Situation-Aware Media and Presentations; Meaning Negotiation; Multiagent Modeling and Simulation of Economic Systems; Ontologies and the Semantic Web; Planning with and for Multiagent Systems; Preferences in AI and CP: Symbolic Approaches; Probabilistic Approaches in Search; Real-Time Decision Support and Diagnosis Systems; Semantic Web Meets Language Resources; and Spatial and Temporal Reasoning.

Send applications and inquiries to May Cheh; National Library of Medicine, 8600 Rockville Pike, Mail Stop 54, Bethesda, MD 20894-6075; Email: cheh@nlm.nih.gov