Biological Grounding of Recruitment Learning and Vicinal Algorithms in Long-Term Potentiation

• Authors: Lokendra Shastri
• Venue: Emergent Neural Computational Architectures Based on Neuroscience - Towards Neuroscience-Inspired Computing
• Year: 2001

Biological networks are capable of gradual learning based on observing a large number of exemplars over time as well as of rapidly memorizing specific events as a result of a single exposure. The focus of research in neural networks has been on gradual learning, and the modeling of one-shot memorization has received relatively little attention. Nevertheless, the development of biologically plausible computational models of rapid memorization is of considerable value, since such models would enhance our understanding of the neural processes underlying episodic memory formation. A few researchers have attempted the computational modeling of rapid (one-shot) learning within a framework described variably as recruitment learning and vicinal algorithms. Here it is shown that recruitment learning and vicinal algorithms can be grounded in the biological phenomena of long-term potentiation and long-term depression. Toward this end, a computational abstraction of LTP and LTD is presented, and an "algorithm" for the recruitment of binding-detector (or coincidence-detector) cells is described and evaluated using biologically realistic data.