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presented again, as in Fig. 7B, the output of the cell will be governed not only by the input but also by the feedback connections, a subset of which were strengthened (Fig. 7B, filled synapses) by the initial presentation of the stimulus. Thus, for output cell z, a component of its activity will be derived from input a but components will also come from synapses 1, 3, 5, and 6. If each of the initially strong and newly modified synapses is assumed to contribute equally to the firing of output cell z, it would be reasonable that the activity would be five times greater than that activity produced by input a before the learning. After learning, we see that the output is an amplified version of the input but that the basic features of the pattern are preserved.

Note that the memory for the pattern does not reside in any one synapse or in any one cell; rather, it is distributed throughout the network at multiple sites. The properties of these types of autoassociation networks have been examined by James Anderson, Teuvo Kohonen, David Mar, Edmond Rolls, David Wilshaw, and their colleagues and found to exhibit a number of phenomena that would be desirable for a biologic recognition memory system. For example, the autoassociation networks exhibit pattern completion. If a partial input pattern is

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