Biologically-Inspired Learning and Intelligence

Abstract

Since biologically-inspired intelligent systems with learning and decision-making capabilities vastly act upon comparison among inputs, the ability to select those inputs which satisfy certain conditions is of great significance in realization of such systems. Moreover intelligent systems need to operate with concurrency so as to reflect inherited capability of their biological counterparts like human. Due to difficulties in programmability, storage and design complexities, the analog implementation has been considerably less favored in most computational information processing systems. However, in the case of biologically-inspired computation, their suitability for concurrency, accuracy and capability in simulating the natural behavior of biological signals, analog neural information processing is regarded an attractive solution. Benefiting the full advantage involves comprehensive understanding and knowledge of what trade-offs can be established with design topologies available and theoretical necessities. On the other hand, fuzzy reasoning offers rule-based inferential manipulation on inputs where it expresses the input-output relationship in terms of clauses. Considering a nonlinear operation carried out by artificial neural networks based on experience, realization of rule-based clauses is much easier. This chapter introduces fundamental notions of fuzzy reasoning, and fuzzy-based analog design approaches. Rather than resorting on analytical derivation for the architecture of interest, the main focus is directed at suitability for use, which is expected to indicate possibility toward developing complex intelligent systems. It should be noted that the circuits having selectivity property in deciding maximum and/or minimum on inputs demonstrate their use in much broader field than inference, thus they have great importance in realization of information processing systems. The chapter presents a very compact selectivity circuit as decision maker for the minimum of its inputs. Further to it, a considerably simple yet elaborate membership structure is introduced. The circuit simplifies the fuzzy controller design. Since mostly decision making is performed on a (dis)similarity measure between inputs, e.g. the input and label patterns for respective categories, it is convenient to express the proximity in terms of a metric. The chapter also introduces important designs proposed for assessing the similarity in the Euclidean distance.