A Biological Grounding of Recruitment Learning and Vicinal Algorithms

[Lokendra Shastri]

Dear Connectionists,

The following report may be of interest to you.
Best wishes.

-- Lokendra Shastri

http://www.icsi.berkeley.edu/~shastri/psfiles/tr-99-009.ps.gz

------------------------------------------------------------------------------

	A Biological Grounding of Recruitment Learning and Vicinal Algorithms

				Lokendra Shastri
                   International Computer Science Institute
	                   Berkeley, CA 94704
				   TR-99-009
				  April, 1999

	Biological neural networks are capable of gradual learning based on
	observing a large number of exemplars over time as well as rapidly
	memorizing specific events as a result of a single exposure. The
	primary focus of research in connectionist modeling has been on
	gradual learning, but some researchers have also attempted the
	computational modeling of rapid (one-shot) learning within a
	framework described variably as recruitment learning and vicinal
	algorithms. While general arguments for the neural plausibility of
	recruitment learning and vicinal algorithms based on notions of
	neural plasticity have been presented in the past, a specific neural
	correlate of such learning has not been proposed. Here it is shown
	that recruitment learning and vicinal algorithms can be firmly
	grounded in the biological phenomena of long-term potentiation
	(LTP) and long-term depression (LTD). Toward this end, a
	computational abstraction of LTP and LTD is presented, and an
	``algorithm'' for the recruitment of binding-detector cells is
	described and evaluated using biologically realistic data. 
	It is shown that binding-detector cells of distinct bindings exhibit
	low levels of cross-talk even when the bindings overlap. In the
	proposed grounding, the specification of a vicinal algorithm 
	amounts	to specifying an appropriate network architecture and 
	suitable parameter values for the induction of LTP and LTD.

	KEYWORDS: one-shot learning; memorization; recruitment learning;
	          dynamic bindings; long-term potentiation; binding detection.