Memory Formation of Complex Patterns

The relationship between the synaptic changes and the behavior of conditioned reflexes is generally straightforward, because the locus for the plastic change is part of the mediating circuit. Thus, the change in the strength of the sensorimotor synapse can be related to the memory for sensitization (e.g., see Fig. 4). However, in most other examples of memory, it is considerably less clear how the synaptic changes are induced, and, once induced, how the information is retrieved. Neurobiologists have turned to artificial neural circuits to gain insights into these issues.

A simple network that can store and ''recognize'' patterns is illustrated in Fig. 7. The network is artificial, but it is nevertheless inspired by actual circuitry in the CA3 region of the hippocampus. In this example, six different input projections make synaptic connections with the dendrites of each of six postsynaptic neurons (Fig. 7A). The postsynaptic neurons serve as the output of the network. Input projections can carry multiple types of patterned information, and these patterns can be complex. But, to simplify the present discussion, consider that the particular input pathway in Fig. 7A carries information regarding the pattern of neural activity induced by a single brief flash of a spatial pattern of light. For example, activity in the top pathway (line a) might represent light falling on the temporal region, whereas activity in the pathway on the bottom (line f) might represent light falling on the nasal region of the retina. Thus, the spatial pattern of an image falling upon the retina could be reconstructed from the pattern of neuronal activity over the n (in this case, 6) input projections to the network. Three aspects of the circuit endow it with the ability to store and retrieve patterns. First, each of the input lines makes a sufficiently strong connection with its corresponding postsynaptic cell to reliably activate it. Second, each output cell (z to u) sends an axon collateral that makes an excitatory connection with itself as well as the other five output cells. This pattern of synaptic connectivity leads to a network of 36 synapses (42 when including the 6 input synapses). Third, each of the 36 synaptic connections is modifiable through an LTP-like mechanism (see earlier discussion). Specifically, the strength of a particular synaptic connection is initially weak, but it will increase if the presynaptic and post-synaptic neurons are active at the same time. The circuit configuration with the embedded synaptic ''learning rule'' leads to an autoassociation or autocorrelation matrix. The autoassociation is derived from the fact that the output is fed back to the input, where it associates with itself.

Now consider the consequences of presenting the patterned input to the network of Fig. 7A. The input pattern will activate the six postsynaptic cells in such a way as to produce an output pattern that will be a replica of the input pattern; in addition, however, the pattern will induce changes in the synaptic strength of the active synapses in the network. For example, synapse 3 will be strengthened, because both the postsynaptic cell, cell z, and the presynaptic cell, cell x, will be active at the same time. Note also that synapses 1, 5, and 6 will be strengthened. This is so because these input pathways to cell z are also active; thus, all synapses that are active at the same time as cell z will be strengthened. When the pattern is

Mechanisms of Learning and Memory

A. Before learning

B. After learning

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