manifestations of one such principle: automatic self-tuning by competitive networks. My other chapter in the book illustrates some applications of a second principle: gating properties of chemical transmitters. A third principle is reviewed elsewhere ( Grossberg [ 1971], [ 1974], [ 1978b]): pattern learning and growth properties by networks containing fast (STM) and slow (LTM) feedback interactions. These several principles are joined together to generate adaptive resonances and reset operations in the service of global code development and error-correction.
Various other articles in this book have probed aspects of these concepts from several experimental and theoretical perspectives. Thus the principles are starting to enjoy the multiple emergence that often characterizes major conceptual progress in a science. We are, happily, approaching an era when it will be appropriate to identify a small number of laws on which the brain sciences can be based. It is imperative that we not confuse the invariant structure of these laws with the endless list of minor experimental or numerical variations that can draw both experimental and theoretical neuroscience to the brink of conceptual solipsism. A law must be identified before it can be classified, just as a single Schrödinger or Laplace equation can be identified despite its appearance in a vast number of physically distinct examples. The possibility of achieving such coherence is vested in mathematics for the brain sciences no less than for science in general.
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