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R J MacGregor

Publications and source records attributed to R J MacGregor.

8 recordsLinked to original sources
PubMed1991

Sequential configuration model for firing patterns in local neural networks.

This paper presents a sequential configuration model to represent the coordinated firing patterns of memory traces in groups of neurons in local networks. Computer simulations are used to study the dynamic properties of memory traces selectively retrieved from networks in which multiple memory traces have been embedded according to the sequential configuration model. Distinct memory traces which utilize the same neurons, but differ only in temporal sequencing are selectively retrievable. Firing patterns of constituent neurons of retrieved memory traces exhibit the main properties of neurons observed in multi microelectrode recordings. The paper shows how to adjust relative synaptic weightings so as to control the disruptive influences of cross-talk in multipy-embedded networks. The theoretical distinction between (primarily anatomical) beds and (primarily physiological) realizations underlines the fundamentally stochastic nature of network firing patterns, and allows the definition of 4 degrees of clarity of retrieved memory traces.

Animals↗
PubMed1991

Cross-talk theory of memory capacity in neural networks.

The present paper presents a theory for the mechanics of cross-talk among constituent neurons in networks in which multiple memory traces have been embedded, and develops criteria for memory capacity based on the disruptive influences of this cross-talk. The theory is based on interconnection patterns defined by the sequential configuration model of dynamic firing patterns. The theory accurately predicts the memory capacities observed in computer simulated nets, and predicts that cortical-like modules should be able to store up to about 300-900 selectively retrievable memory traces before disruption by cross-talk is likely. It also predicts that the cortex may has designed itself for modules of 30,000 neurons to at least in part to optimize memory capacity.

Animals↗
PubMed1988

Theory of dynamic similarity in neuronal systems.

1. The techniques of dynamic similarity from the engineering science of fluid mechanics are applied to neuronal systems to suggest how to scale down critical parameters (such as numbers of constituent cells and synapses, synaptic strengths, thresholds, etc.) from naturally occurring systems to computer models. 2. The interconnectivity of a prototypical neuronal junction is defined in terms of the total number of projecting fibers, receiving cells, synapses, and directly connected cell fiber pairs. Critical derivative parameters are defined in terms of these, including: a global convergence factor, alpha ij, which is the ratio of the numbers of projecting fibers to receiving cells; and an interconnectivity completeness parameter or microscopic convergence/divergence parameter, gamma ij, which measures both the percentage of cells to which a given sending fiber projects (and the percentage of fibers from which a given cell receives) and the percentage of cell fiber combinations which are directly connected. 3. Analysis of the differential equations governing neuroelectric activity in constituent neurons suggests the definition of a sensitivity parameter complex, sigma ij (with components eta ij and mu ij) for each ij junction. These numbers represent the ratio of synaptic drive to current leakage in nonactive neurons. 4. A model for quasi-steady firing suggests the definition of a parameter, rho *j, which may be used to characterize the level of activity in a given neuronal population in terms of its synaptic drive and system parameters. It may be considered as the neuronal analog of the Reynolds number in fluid mechanics. 5. The analysis implies that computer models of neuronal systems should be scaled so as to keep the parameters alpha ij, gamma ij, and sigma ij for every junction at the same values as in the corresponding junctions of naturally occurring system being modeled. Equations for a scaling factor, chi, numbers of constituent synapses, thresholds, etc., are provided. The scaling method is illustrated by a computer simulation example and by application to the junction of the perforant path fibers to the granule cells of the hippocampus. 6. The analysis shows that there is a fundamental trade-off in scaled down computer models between verisimilitude at the level of network interconnectivity and verisimilitude at the level of individual neuronal dynamics. 7. The approach of dynamic similarity is discussed with respect to compression of free parameters and predictive comparison of naturally occurring systems.

Action Potentials↗
PubMed1979

Self-catheterization for decompensated bladder: a review of 100 cases.

The non-neurogenic decompensated bladder is a poorly defined entity. In an attempt to elucidate this condition 100 consecutive patients with non-neurogenic decompensated bladders were studied, etiology was sought and treatment with intermittent self-catheterization was done. Of the patients 34 per cent were able to resume voiding. Bacteriuria and pyuria were decreased from 90 to 18 per cent. Complications were few and over-all acceptance was excellent. A definition of non-neurogenic decompensated bladder was established.

Adolescent↗
PubMed1977

Systems simulation: theory of monosynaptic transfer between neuron populations.

This paper presents a theory for the input-output transformation involved in diffuse monosynaptic activation in a neural tissue or organ system of one population of neurons by another. Main findings are: (1) highly diffuse monosynaptic linkages act very much like filters, selectively sensitive to synchronized clusters of action potentials among the fibers of the input population; (2) partially diffuse monosynaptic linkages are capable of effecting either an amplification or diminution of the number of pulses involved in a single synchronized cluster, depending on parameters of the system; and (3) partially diffuse and spatially organized monosynaptic linkages are capable of effecting a spatial inversion of fine-grained spatial patterns. Theoretical predictions are clarified by mathematical analysis and computer simulation.

Action Potentials↗