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

Shastri, Lokendra

doi:10.1007/3-540-44597-8_26

Cited by 18 publications

(18 citation statements)

References 34 publications

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“…It performs this reasoning with sparse, structured coding, like the best connectionist models to date (van der Velde and de Kamps, 2006), and uses simple computational units that could be built from neurons or collections of neurons. Much of this has already been discussed relative to SHRUTI, and our model is similar enough to SHRUTI that much of that argument holds (Shastri, 2001b). There seems to be no way to eliminate some common attentional mechanism like the central binder of § 2.2 (Simons and Chabris, 1995).…”

Section: Biological Plausibilitymentioning

confidence: 55%

A (Somewhat) New Solution to the Variable Binding Problem

2008

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To perform automatic, unconscious inference, the human brain must solve the "binding problem" by correctly grouping properties with objects. Temporal binding models like SHRUTI already suggest much of how this might be done in a connectionist and localist way by using temporal synchrony. We propose a set of alternatives to temporal synchrony mechanisms that instead use short signatures. This serves two functions: it allows us explore an additional biologically plausible alternative, and it allows us to extend and improve the capabilities of these models. These extensions model the human ability to both perform unification and handle multiple instantiations of logical terms. To verify our model's feasibility, we simulate it with a computer system modeling simple, neuron-like computations.

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Section: Biological Plausibilitymentioning

confidence: 55%

A (Somewhat) New Solution to the Variable Binding Problem

2008

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“…All of these incremental changes can be implemented in the SHRUTI model via relatively simple means involving the recruitment of nodes, by strengthening latent connections as a response to frequent simultaneous activations (Shastri, 2001;Shastri and Wendelken, 2003;Wendelken and Shastri, 2003).…”

Section: Mother(x)  Mother(xy)mentioning

confidence: 99%

The origin and evolution of language: a plausible, strong-AI account

Hobbs¹

2006

Action to Language via the Mirror Neuron System

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A large part of the mystery of the origin of language is the difficulty we experience in trying to imagine what the intermediate stages along the way to language could have been. An elegant, detailed, formal account of how discourse interpretation works in terms of a mode of inference called abduction, or inference to the best explanation, enables us to spell out with some precision a quite plausible sequence of such stages. In this chapter I outline plausible sequences for two of the key features of language − Gricean nonnatural meaning and syntax. I then speculate on the time in the evolution of modern humans each of these steps may have occurred. FRAMEWORKIn this chapter I show in outline how human language as we know it could have evolved incrementally from mental capacities it is reasonable to attribute to lower primates and other mammals. I do so within the framework of a formal computational theory of language understanding (Hobbs et al., 1993). In the first section I describe some of the key elements in the theory, especially as it relates to the evolution of linguistic capabilities. In the next two sections I describe plausible incremental paths to two key aspects of language − --meaning and syntax. In the final section I discuss various considerations of the time course of these processes. Strong AIIt is desirable for psychology to provide a reduction in principle of intelligent, or intentional, behavior to neurophysiology. Because of the extreme complexity of the human brain, more than the sketchiest account is not likely to be possible in the near future. Nevertheless, the central metaphor of cognitive science, "The brain is a computer", gives us hope. Prior to the computer metaphor, we had no idea of what could possibly be the bridge between beliefs and ion transport. Now we have an idea. In the long history of inquiry into the nature of mind, the

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“…Convergent presynaptic activity at s i 's can lead to associative LTP of naive s i 's and increase their weights by w ltp if the following conditions hold: (i) the total (convergent) activity arriving at s i 's exceeds p , (ii) this activity is synchronous, i.e., arrives with a maximum lead/lag of !, (iii) such synchronous activity repeats at least times, and (iv) the interval between two successive arrivals of convergent activity is at most iai . It has been shown [17] that recruitment learning algorithms [6,15] proposed for one-shot learning in connectionist networks can be firmly grounded in LTP.…”

Section: Long-term Potentiationmentioning

confidence: 99%

A computational model of episodic memory formation in the hippocampal system

Shastri¹

2001

Neurocomputing

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The memorization of events and situations (episodic memory) requires the rapid formation of a memory trace consisting of several functional components. A computational model is described that demonstrates how a transient pattern of activity representing an episode can lead to the rapid recruitment of appropriate circuits as a result of long-term potentiation within structures whose architecture and circuitry match those of the hippocampal formation, a neural structure known to play a critical role in the formation of such memories.

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

Cited by 18 publications

References 34 publications

A (Somewhat) New Solution to the Variable Binding Problem

A (Somewhat) New Solution to the Variable Binding Problem

The origin and evolution of language: a plausible, strong-AI account

A computational model of episodic memory formation in the hippocampal system

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