Information Blending in Virtual Associative Networks: A New Paradigm for Sensor Integration

Yufik, Yan M.; Malhotra, Raj

doi:10.1142/s0218213099000191

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…The self-adaptive resource optimization framework (Yufik, 1998b, 2002; Yufik and Malhotra, 1999; Yufik and Sheridan, 2002) offers a simple account of cognitive processes, highlighting the crucial role of Markov blanket induction in neuronal systems, as a pivotal optimization mechanism.…”

Section: Discussion and Suggestions For Further Researchmentioning

confidence: 99%

Life and Understanding: The Origins of “Understanding” in Self-Organizing Nervous Systems

Yufik¹,

Friston

2016

Front. Syst. Neurosci.

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This article is motivated by a formulation of biotic self-organization in Friston (2013), where the emergence of “life” in coupled material entities (e.g., macromolecules) was predicated on bounded subsets that maintain a degree of statistical independence from the rest of the network. Boundary elements in such systems constitute a Markov blanket; separating the internal states of a system from its surrounding states. In this article, we ask whether Markov blankets operate in the nervous system and underlie the development of intelligence, enabling a progression from the ability to sense the environment to the ability to understand it. Markov blankets have been previously hypothesized to form in neuronal networks as a result of phase transitions that cause network subsets to fold into bounded assemblies, or packets (Yufik and Sheridan, 1997; Yufik, 1998a). The ensuing neuronal packets hypothesis builds on the notion of neuronal assemblies (Hebb, 1949, 1980), treating such assemblies as flexible but stable biophysical structures capable of withstanding entropic erosion. In other words, structures that maintain their integrity under changing conditions. In this treatment, neuronal packets give rise to perception of “objects”; i.e., quasi-stable (stimulus bound) feature groupings that are conserved over multiple presentations (e.g., the experience of perceiving “apple” can be interrupted and resumed many times). Monitoring the variations in such groups enables the apprehension of behavior; i.e., attributing to objects the ability to undergo changes without loss of self-identity. Ultimately, “understanding” involves self-directed composition and manipulation of the ensuing “mental models” that are constituted by neuronal packets, whose dynamics capture relationships among objects: that is, dependencies in the behavior of objects under varying conditions. For example, movement is known to involve rotation of population vectors in the motor cortex (Georgopoulos et al., 1988, 1993). The neuronal packet hypothesis associates “understanding” with the ability to detect and generate coordinated rotation of population vectors—in neuronal packets—in associative cortex and other regions in the brain. The ability to coordinate vector representations in this way is assumed to have developed in conjunction with the ability to postpone overt motor expression of implicit movement, thus creating a mechanism for prediction and behavioral optimization via mental modeling that is unique to higher species. This article advances the notion that Markov blankets—necessary for the emergence of life—have been subsequently exploited by evolution and thus ground the ways that living organisms adapt to their environment, culminating in their ability to understand it.

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Section: Discussion and Suggestions For Further Researchmentioning

confidence: 99%

Life and Understanding: The Origins of “Understanding” in Self-Organizing Nervous Systems

Yufik¹,

Friston

2016

Front. Syst. Neurosci.

Self Cite

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“…The computational architecture of associative cortices readily affords self-partitioning in associative networks allowing near-optimal behavior. In Gnostron, the partitioning quality is defined by a simple criterion: choose a particular optimization algorithm and compare results obtained before (baseline) and after partitioning into packets [a stripped down, proof-of-concept system for target recognition obtained close to two orders of magnitude complexity reduction with acceptably small error amplitude ( Malhotra and Yufik, 1999 ; Yufik and Malhotra, 1999 )]. Figure 13 generalizes the gnostron proposal.…”

Section: Machine Situational Understandingmentioning

confidence: 99%

Situational Understanding in the Human and the Machine

Yufik¹,

Malhotra

2021

Front. Syst. Neurosci.

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The Air Force research programs envision developing AI technologies that will ensure battlespace dominance, by radical increases in the speed of battlespace understanding and decision-making. In the last half century, advances in AI have been concentrated in the area of machine learning. Recent experimental findings and insights in systems neuroscience, the biophysics of cognition, and other disciplines provide converging results that set the stage for technologies of machine understanding and machine-augmented Situational Understanding. This paper will review some of the key ideas and results in the literature, and outline new suggestions. We define situational understanding and the distinctions between understanding and awareness, consider examples of how understanding—or lack of it—manifest in performance, and review hypotheses concerning the underlying neuronal mechanisms. Suggestions for further R&D are motivated by these hypotheses and are centered on the notions of Active Inference and Virtual Associative Networks.

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“…It has been shown that grouping by packets (i.e., via forming associative networks partitioned into internally cohesive and externally weakly coupled neuronal groups) is near-optimal, i.e., maximizes rewards while minimizing the number of unproductive allocations. Packet mechanism was shown to yield near-optimal results and orders of magnitude reduction in the amount of computation in large-scale resource optimization tasks (e.g., target recognition) [9], [10].…”

Section: Gnostron: a Framework For Machine Understandingmentioning

confidence: 99%