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Recognition: Are There Multiple Mechanisms for Filtering Complex Stimuli?

  • Theodore Holmes Bullock

Summary

A basic system-level problem is how brains recognize stimuli, using that verb operationally, without implying or denying conscious awareness. This is similar but not necessarily equivalent to asking how they represent stimuli. I approach it here by considering only a subset of behavioral recognitions, namely the biologically important stimuli, such as species-typical communications, that usually trigger all-or-none recognition (offspring, mate, food, predator, home), with or without specific motor response. This is in contrast to the subset, postponed here, of more ambiguous, protracted, or sophisticated stimuli, such as unfamiliar faces, voices, or objects (Mozart, Rembrandt, haute cuisine, tickle).

The prevailing model, hereafter called the ensemble pattern model is the same for both subsets of behavior. It equates representation and recognition and ascribes them to a specific spatiotemporal configuration of activity in a large population of diversely reacting neurons. This is mainly because it is considered to be a plausible and cautious extrapolation of the fact that familiar brains consist of many units, suggestive of theoretical networks. By assuming that prior processing has somehow extracted scalar values for a number of features (vectors), and has normalized these or compensated for the wide range of angles of view, distances, lighting and other variables tolerated by the behavioral recognition, modern network theory proposes that the ensemble pattern represents the particular set of values of these vectors. This model is not likely to be testable in the brain in the near future though increasingly realistic neural network simulations can gradually suggest whether the brain could use the same principles. Its main features are that the relevant neurons respond in a variety of ways and are distributed in space and time in specific patterns. The problem of readout is usually not addressed: what determines that the criteria have been met for the specific pattern of activation representing a given perception? It may cause a specific motor response, but that can be frustrated or altered and cannot be considered the recognition process. Since the recognition is not attributed to a small set, whatever motor output is specific to it cannot employ a command unit or set of similar units but must depend on a large array of specified connections to parts of the motor systems. Since the subset of "either¡ªor" recognitions are, within limits, independent of distance, angle of view or absolute pitch and other sensory parameters, a wide range of afferent configurations must light up the whole of the essential ensemble. This model is quite plausible in certain respects but faces the problems mentioned as well as others stemming from the dynamic behavior of neurons. It has not been formally stated in a generally applicable and testable way.

A contrasting view is not a model in an explanatory sense. I will call it the unpatterned or small set recognition mechanism. It argues that known cells with complex stimulus requirements for vigorous firing are probably useful in perceptual categorization and sometimes in commanding motor patterns. It also interprets the data to mean that these cells are not just one element in a complex pattern of many cells for that level of stimulus complexity but are sufficient, barring excessive interfering activity. They are presumed to depend on specific input configurations like those in the ensemble pattern model and may in turn become members of ensembles leading to higher level recognitions. They are known to occur for a widening list of cases, for example, stimuli that trigger the jamming avoidance response in certain electric fish, prey-like small moving objects arousing interest or capture by toads, subspecies-specific bird song, and faces. A small set of similar but not necessarily identical neurons, which we may call recognition cells, are each connected to more or less all the relevant converging inputs and diverging outputs.

Keywords

Auditory Cortex Inferotemporal Cortex Specific Vocalization Realistic Neural Network Specific Motor Response 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer Science+Business Media New York 1993

Authors and Affiliations

  • Theodore Holmes Bullock
    • 1
  1. 1.Department of Neurosciences 0201University of California, San DiegoLa JollaUSA

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