Abstract|We present an object recognition system based

Abstract: Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficiency — as though these inferences are a reflex response of their cognitive apparatus. Furthermore, these inferences are drawn with reference to a large body of background knowledge. This remarkable human ability seems paradoxical given the results about the complexity of reasoning reported by researchers in artificial intelligence. It also poses a challenge for cognitive science and computational neuroscience: How can a system of simple and slow neuron-like elements represent a large body of systematic knowledge and perform a range of inferences with such speed? We describe a computational model that is a step toward addressing the cognitive science challenge and resolving the artificial intelligence paradox. We show how a connectionist network can encode millions of facts and rules involving n-ary predicates and variables, and perform a class of inferences in a few hundred msec. Efficient reasoning requires the rapid representation and propagation of dynamic bindings. Our model achieves this by i) representing dynamic bindings as the synchronous firing of appropriate nodes, ii) rules as interconnection patterns

This article describes an integrated theory of analogical access and mapping, instantiated in a

temporal relations requires the joint evaluation of responses from more than one neuron, only experiments that permit simultaneous measurements of responses 60528 Frankfurt from multiple units are considered. These include multi-Federal Republic of Germany electrode recordings from multiple individual cells, but also measurements of local field potentials (LFPs) and electroencephalographic (EEG) or magnetoencephalo-Most of our knowledge about the functional organization of neuronal systems is based on the analysis of the firing patterns of individual neurons that have been recorded one by one in succession. This approach permits as-sessment of event-related variations in discharge rate, but it precludes detection of any covariations in the amplitude or timing of distributed responses if these graphic (MEG) recordings. The signals of these latter

We study image segmentation on the basis of locally excitatory globally inhibitory oscillator networks (LEGION), whereby the phases of oscillators encode the binding of pixels. We introduce a potential for each oscillator so that only those oscillators with strong connections from their neighborhood can develop high potentials. Based on the concept of potential, a solution to remove noisy regions in an image is proposed for LEGION, so that it suppresses the oscillators corresponding to noisy regions, without affecting those corresponding to major regions. We show analytically that the resulting oscillator network separates an image into several major regions, plus a background consisting of all noisy regions, and illustrate network properties by computer simulation. The network exhibits a natural capacity in segmenting images. The oscillatory dynamics leads to a computer algorithm, which is applied successfully to segmenting real graylevel images. A number of issues regarding biological plausibility and perceptual organization are discussed. We argue that LEGION provides a novel and effective framework for image segmentation and figure-ground segregation. DeLiang Wang and David Terman Image Segmentation 1.

The nature and origin of the temporal irregularity in the electrical activity of cortical neurons in vivo are still not well understood. We consider the hypothesis that this irregularity is due to a balance of excitatory and inhibitory currents into the cortical cells. We study a network model with excitatory and inhibitory populations of simple binary units. The internal feedback is mediated by relatively large synaptic strengths, so that the magnitude of the total excitatory as well as inhibitory feedback is much larger than the neuronal threshold. The connectivity is random and sparse. The mean number of connections per unit is large but small compared to the total number of cells in the network. The network also receives a large, temporally regular input from external sources. An analytical solution of the mean-field theory of this model which is exact in the limit of large network size is presented. This theory reveals a new cooperative stationary state of large networks, which we term a balanced state. In this state, a balance between the excitatory and inhibitory inputs emerges dynamically for a wide range of parameters, resulting in a net input whose temporal fluctuations are of the same order as its mean. The internal synaptic inputs act as a strong negative feedback, which linearizes the population responses to the external drive despite the strong nonlinearity of the individual cells. This feedback also greatly stabilizes 1 the system's state and enables it to track a time-dependent input on time scales much shorter than the time constant of a single cell. The spatio-temporal statistics of the balanced state is calculated. It is shown that the auto-correlations decay on a short time scale yielding an approximate Poissonian temporal s...

We are capable of drawing a variety of inferences effortlessly, spontaneously, and with remarkable efficiency — as though these inferences are a reflex response of our cognitive apparatus. This remarkable human ability poses a challenge for cognitive science and computational neuroscience: How can a network of slow neuron-like elements represent a large body of systematic knowledge and perform a wide range of inferences with such speed? The connectionist model Shruti attempts to address this challenge by demonstrating how a neurally plausible network can encode a large body of semantic and episodic facts, systematic rules, and knowledge about entities and types, and yet perform a wide range of explanatory and predictive inferences within a few hundred milliseconds. Relational structures (frames, schemas) are represented in Shruti by clusters of cells, and inference in Shruti corresponds to a transient propagation of rhythmic activity over such cell-clusters wherein dynamic bindings are represented by the synchronous firing of appropriate cells. Shruti encodes mappings across relational structures using high-efficacy links that enable the propagation of rhythmic activity, and it encodes items in long-term memory as coincidence and conincidence-error detector circuits that become active in response to the occurrence (or non-occurrence) of appropriate coincidences in the on going flux of rhythmic activity.

A computational model of human memory for serial order is described (OSCillator-based Associative Recall [OSCAR]). In the model, successive list items become associated to successive states of a dynamic learning-context signal. Retrieval involves reinstatement of the learning context, successive states of which cue successive recalls. The model provides an integrated account of both item memory and order memory and allows the hierarchical representation of temporal order information. The model accounts for a wide range of serial order memory data, including differential item and order memory, transposition gradients, item similarity effects, the effects of item lag and separation in judgments of relative and absolute recency, probed serial recall data, distinctiveness effects, grouping effects at various temporal resolutions, longer term memory for serial order, list length effects, and the effects of vocabulary size on serial recall. The serial ordering of behavior is central to much, perhaps most, of human cognition (e.g., Lashley, 1951). Studies of memory for serial order have provided rich data on the psychological repre-sentation of serial order information and therefore offer a signifi-cant challenge to any model of serially ordered behavior. In this

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The seemingly effortless ability to perceive meaningful objects in an integrated scene actually depends on complex visual processes. The `binding problem' concerns the way in which we select and integrate the separate features of objects in the correct combinations. Experiments suggest that attention plays a central role in solving this problem. Some neurological patients show a dramatic breakdown in the ability to see several objects; their deficits suggest a role for the parietal cortex inthe binding process. However, indirect measures of priming and interference suggest that more information may be implicitly available than we can consciously access.