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Neural Network Approaches to Eyeblink Classical Conditioning

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Eyeblink Classical Conditioning: Volume 2

Conclusions

We have described our efforts to further the understanding of the neural substrates of rabbit eyeblink conditioning (in particular the cerebellum and hippocampus) through the use of neural networks. Over the years, the initial theoretical assumptions of the computational models have been maintained while more anatomically and physiologically detailed instantiations of these models have been developed. These neural networks have increased our understanding of how classical conditioning occurs in the cerebellum and hippocampus while making novel predictions which are guiding ongoing and future empirical studies. Neural networks result in simulations of various lesion conditions which offer subtle distinctions which may not be evident in a purely qualitative theory.

Neural networks also provide a ‘tool kit’ which can be applied to various data-sets outside of the realm of basic lesion studies which were the basis for the original models. A case in point is the application of the basic Gluck and Myers (1993) hippocampal model to the septo-hippocampal literature in which a manipulation of a basic component (hippocampal learning rate) can account for a variety of cholinergic drug effects (Myers et al., 1996; Myers et al., 1998). In this way, neural networks can be a tool that can be applied to exploring many different aspects of classical eyeblink conditioning in particular and learning and memory in general.

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Authors and Affiliations

  1. Rutgers University, New Brunswick, New Jersey, USA

    M. Todd Allen, Catherine E. Myers & Mark Gluck

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  1. M. Todd Allen
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  2. Catherine E. Myers
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Editors and Affiliations

  1. Temp1e University, Pennsylvania, USA

    Diana S. Woodruff-Pak

  2. Indiana University, Indiana, USA

    Joseph E. Steinmetz

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Allen, M.T., Myers, C.E., Gluck, M. (2002). Neural Network Approaches to Eyeblink Classical Conditioning. In: Woodruff-Pak, D.S., Steinmetz, J.E. (eds) Eyeblink Classical Conditioning: Volume 2. Springer, Boston, MA. https://doi.org/10.1007/0-306-46897-2_10

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  • DOI: https://doi.org/10.1007/0-306-46897-2_10

  • Publisher Name: Springer, Boston, MA

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