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Advances in SHRUTI - A neurally motivated model of relational knowledge representation and rapid inference using temporal synchrony
- Applied Intelligence
, 1999
"... We are capable of drawing a variety of inferences effortlessly, spontaneously, and with remarkable efficiency — as though these inferences are a reflex response of our cognitive apparatus. This remarkable human ability poses a challenge for cognitive science and computational neuroscience: How can a ..."
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Cited by 65 (19 self)
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We are capable of drawing a variety of inferences effortlessly, spontaneously, and with remarkable efficiency — as though these inferences are a reflex response of our cognitive apparatus. This remarkable human ability poses a challenge for cognitive science and computational neuroscience: How can a network of slow neuron-like elements represent a large body of systematic knowledge and perform a wide range of inferences with such speed? The connectionist model Shruti attempts to address this challenge by demonstrating how a neurally plausible network can encode a large body of semantic and episodic facts, systematic rules, and knowledge about entities and types, and yet perform a wide range of explanatory and predictive inferences within a few hundred milliseconds. Relational structures (frames, schemas) are represented in Shruti by clusters of cells, and inference in Shruti corresponds to a transient propagation of rhythmic activity over such cell-clusters wherein dynamic bindings are represented by the synchronous firing of appropriate cells. Shruti encodes mappings across relational structures using high-efficacy links that enable the propagation of rhythmic activity, and it encodes items in long-term memory as coincidence and conincidence-error detector circuits that become active in response to the occurrence (or non-occurrence) of appropriate coincidences in the on going flux of rhythmic activity.
Foundations of Assisted Cognition Systems
, 2003
"... this report. Kautz [79] modeled plan recognition logically in a manner that allowed goals and plans to be described at various levels of abstraction. Etzioni et al. [94, 95, 92, 93] developed a version space algorithm for plan recognition that is provably sound and polynomial time [94, 93]. Weld et ..."
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Cited by 27 (4 self)
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this report. Kautz [79] modeled plan recognition logically in a manner that allowed goals and plans to be described at various levels of abstraction. Etzioni et al. [94, 95, 92, 93] developed a version space algorithm for plan recognition that is provably sound and polynomial time [94, 93]. Weld et al. developed goal recognition algorithms using inductive logic programming [90] and version-space algebra [89, 168, 88] in the context of programming by demonstration
Biological Grounding of Recruitment Learning and Vicinal Algorithms in Long-term Potentiation
, 1999
"... 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 memori ..."
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Cited by 27 (8 self)
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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 longterm 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.
A Model of Rapid Memory Formation in the Hippocampal System
- In Proceedings of the Meeting of the Cognitive Science Society
, 1997
"... Our ability to remember events and situations in our daily life demonstrates our ability to rapidly acquire new memories. There is a broad consensus that the hippocampal system (HS) plays a critical role in the formation and retrieval of such memories. A computational model is described that dem ..."
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Cited by 18 (9 self)
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Our ability to remember events and situations in our daily life demonstrates our ability to rapidly acquire new memories. There is a broad consensus that the hippocampal system (HS) plays a critical role in the formation and retrieval of such memories. A computational model is described that demonstrates how the HSmay rapidly transform a transient pattern of activity representing an event or a situation into a persistent structural encoding via long-term potentiation and long-term depression. Introduction Our ability to remember events and situations in our daily life and acquire facts after reading a newspaper demonstrates our ability to rapidly acquire new memories. This form of memory has been the focus of considerable research in psychology and cognitive neuroscience and has been characterized variably as declarative, locale, and explicit. There is a broad consensus that this form of memory is distinct, both in its functional properties and its neural basis, from other for...
Recruitment of Binding and Binding-Error Detector Circuits Via Long-Term Potentiation
- NEUROCOMPUTING
, 1999
"... The memorization of events and situations (episodic memory) requires the rapid formation of neural circuits for detecting bindings and binding-errors. The formation of binding-error detectors, however, is problematic given their paradoxical behavior. A computational model is described that demonst ..."
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Cited by 16 (11 self)
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The memorization of events and situations (episodic memory) requires the rapid formation of neural circuits for detecting bindings and binding-errors. The formation of binding-error detectors, however, is problematic given their paradoxical behavior. A computational model is described that demonstrates how a transient pattern of activity representing an episode can lead to the rapid formation of circuits for detecting bindings and bindings-errors as a result of long-term potentiation within structures whose architecture and circuitry match those of the hippocampal formation, a neural structure known to be critical to episodic memory formation.
A Computational Model of Episodic Memory Formation in the Hippocampal System
- Neurocomputing
, 2001
"... 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 ..."
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Cited by 15 (3 self)
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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.
Learning Structured Representations
- Neurocomputing
, 2002
"... shruti is a connectionist model that demonstrates how a network of neuron-like elements can encode a large body of semantic, episodic, and causal knowledge, and rapidly make decisions and perform explanatory and predictive reasoning. To further ground this model in the functioning of the brain it ..."
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Cited by 1 (0 self)
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shruti is a connectionist model that demonstrates how a network of neuron-like elements can encode a large body of semantic, episodic, and causal knowledge, and rapidly make decisions and perform explanatory and predictive reasoning. To further ground this model in the functioning of the brain it must be shown that components of the model can be learned in a neurally plausible manner. Previous work has already demonstrated the rapid learning of episodic facts via cortico-hippocampal interactions.
A Computational Model of Hippocampus for encoding and retrieval of events
"... There has been considerable research in understanding the functional anatomy of the hippocampus and the role it plays in human memory. Models are being developed by researchers which explain some data and also predict results in novel situations. Following the same pattern of research a number of co ..."
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There has been considerable research in understanding the functional anatomy of the hippocampus and the role it plays in human memory. Models are being developed by researchers which explain some data and also predict results in novel situations. Following the same pattern of research a number of computational models of hippocampus have been developed. Each model focusses on one particular aspect of memory like recognition memory or episodic memory. Most of this work is inspired by the computational models of the hippocampus by Lokendra Shastri and Norman,O’Reilly. In this work we have come up with a computational model of the hippocampus which can perform the task of memorization of events and can respond to queries related to the memorized events. I.
A Model of Rapid Memory Formation in the Hippocampal System
"... Our ability to remember events and situations in our daily life demonstrates our ability to rapidly acquire new memories. There is a broad consensus that the hippocampal system (HS) plays a critical role in the formation and retrieval of such memories. A computational model is described that demonst ..."
Abstract
- Add to MetaCart
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Our ability to remember events and situations in our daily life demonstrates our ability to rapidly acquire new memories. There is a broad consensus that the hippocampal system (HS) plays a critical role in the formation and retrieval of such memories. A computational model is described that demonstrates how the HS may rapidly transform a transient pattern of activity representing an event or a situation into a persistent structural encoding via long-term potentiation and long-term depression.
