The production system formulation plays an important role in models of cognition. However, there do not exist neurally plausible realizations of production systems that can support fast and automatic processing of productions involving variables and n-ary relations. In this paper we show that the neurally plausible model for rapid reasoning over facts and rules involving n-ary predicates and variables proposed by Ajjanagadde and Shastri can be interpreted as such a production system. This interpretation is significant because it suggests neurally motivated constraints on the capacity of the working memory of a production system capable of fast parallel processing. It shows that a large number of rules — even those containing variables — may fire in parallel and a large number of facts may reside in the working memory, provided no predicate is instantiated more than a smaU number of times (% 3) and the number of distinct entities referenced by the

Although the connectionist approach has lead to elegant solutions to a number of problems in cognitive science and artificial intelligence, its suitability for dealing with iHX)blems in knowledge representation and inference has often been questioned. This paper partially answers this criticism by demonstrating that effective solutions to certain problems in knowledge representation and limited inference can be found by adopting a connectionist approach.The paper iM-esents a connectionist realization of semantic networks, i.e. it describes h o w knowledge about concepts, their properties, and the hierarchical relationship between them may be encoded as an interpreter-free massively parallel network of simple processing elements that can solve an interesting class of inheritance and recognition problems extremely fast - in time proprotional to the depth of the conceptual hierarchy. The connectionist realization is based on an evidential formulation that leads to principled solutions to the problems of exceptions, multiple inheritance, and conflicting information during inheritance, and the best match or partial match computation during recognition.

Our ability to remember events and situations in our daily life demonstrates our ability to rapidly acquire new memories. There is a broad consensus that the hippocampal system (HS) plays a critical role in the formation and retrieval of such memories. A computational model is described that demonstrates how the HS may rapidly transform a transient pattern of activity representing an event or a situation into a persistent stuctural encoding via long-term potentiation and long-term depression.

We describe an alternative approach to hand- printed word recognition using a hybrid of proce- dural and connectionist techniques. We utilize two connectionist components: one to concurrently make recognition and segmentation hypotheses, and another to perform refined recognition of segmented characters. Both networks are governed by a pro- cedural controller which incorporates systematic do- main knowledge and procedural algorithms to guide recognition. W e employ an approach wherein an image is pro- cessed over time by a spatiotemporal connectionist network. The scheme offers several attractive fea- tures including shift-invariance and retention of lo- cal spatial relationships along the dimension being temporalized, a reduction in the number of free pa- rameters, and the ability to process arbitrarily long images. Recognition results on a set of real-world isolated ZIP code digits are comparable to the best reported to date, with a 9 6 . 0 % recognition rate and a rate of 9 9 . 0 % w h e n 9.5% of the images are rejected.

The ability to represent structured knowledge and use that knowledge in a systematic way is a very important ingredient of cognition. A n often heard criticism of connection ism is that connectionists ystems cannot possess that ability. The work reported in this paper demonstrates that a connectionist system can not only represent structured knowledge aind display systematic behavior, but can also do so with extreme efficiency. The paper describes a connectionist system that can represent knowledge expressed as rules and fads involving muhi-place predicates, and draw limited,but sound, inferences based on this knowledge.The system is extremely efficient - in fact, optimal,as it draws conclusions in time proportion alto the length of the proof. Central to this ability of the system is a solution to the variable binding problem. The solution makes use of the notion of a phased clock and exploits the time dimension to create and propagate variable bindings.