Biomedical subjects

William B Levy

Publications and source records attributed to William B Levy.

6 recordsLinked to original sources

PubMed2006

Metabolic energy cost of action potential velocity.

The action potential of the unmyelinated nerve is metabolically expensive. Using the energetic cost per unit length for the biophysically modeled action potential of the squid giant axon, we analyze this cost and identify one possible optimization. The energetic cost arising from an action potential is divided into three separate components: 1) the depolarization of the rising phase; 2) the hyperpolarization of the falling phase; and 3) the largest component, the overlapping of positive and negative currents, which has no electrical effect. Using both the Hodgkin-Huxley (HH) model and an improved version of the HH model (HHSFL), we investigate the variation of these three components as a function of easily evolvable parameters, axon diameter and ion channel densities. Assuming conduction velocity is well designed for each organism, the energy component associated with the rising phase attains a minimum near the biological values of the diameter and channel densities. This optimization is explained by the membrane capacitance per unit length. The functional capacitance is the sum of the intrinsic membrane capacitance and the gating capacitance associated with the sodium channel, and this capacitance minimizes at nearly the same values of diameter and channel density. Because capacitance is temperature independent and because this result is independent of the assumed velocity, the result generalizes to unmyelinated mammalian axons. That is, channel density is arguably an evolved property that goes hand-in-hand with the evolutionary stability of the sodium channel.

Action Potentials↗

PubMed2005

Interpreting hippocampal function as recoding and forecasting.

A model of hippocampal function, centered on region CA3, reproduces many of the cognitive and behavioral functions ascribed to the hippocampus. Where there is precise stimulus control and detailed quantitative data, this model reproduces the quantitative behavioral results. Underlying the model is a recoding conjecture of hippocampal computational function. The expanded conjecture includes a special role for randomization and, as recoding progresses with experience, the occurrence of sequence learning and sequence compression. These functions support the putative higher-order hippocampal function, i.e. production of representations readable by a linear decoder and suitable for both neocortical storage and forecasting. Simulations confirm the critical importance of randomly driven recoding and the neurocognitive relevance of sequence learning and compression. Two forms of sequence compression exist, on-line and off-line compression: both are conjectured to support neocortical encoding of context and declarative memory as described by .

Cognition↗

PubMed2005

When logic fails: implicit transitive inference in humans.

Transitive inference (TI) in animals (e.g., choosing A over C on the basis of knowing that A is better than B and B is better than C) has been interpreted by some as reflecting a declarative logical inference process. We invert this anthropomorphic interpretation by providing evidence that humans can exhibit TI-like behavior on the basis of simpler associative mechanisms that underlie many theories of animal learning. In this study, human participants were trained on a five-pair TI problem (A+B-, B+C-, C+D-, D+E-, E+F-) and, unlike in previous human TI studies, were prevented from becoming explicitly aware of the logical hierarchy, so they could not employ logical reasoning. They were then tested with three problems: B versus D, B versus E, and C versus E. Participants only reliably chose B over E, whereas the other test conditions yielded chance performance. This result is inconsistent with the use of logical reasoning and is instead consistent with an account developed to explain earlier TI studies with rats that found the same pattern of results. In this account, choice performance is based on differential associative strengths across the stimulus items that develop over training, despite equal overt reinforcement.

Awareness↗

PubMed2004

Analysis of the optimal channel density of the squid giant axon using a reparameterized Hodgkin-Huxley model.

A reparameterized Hodgkin-Huxley-type model is developed that improves the 1952 model's fit to the biological action potential. In addition to altering Na(+) inactivation and K(+) activation kinetics, a voltage-dependent gating-current mechanism has been added to the model. The resulting improved model fits the experimental trace nearly exactly over the rising phase, and it has a propagation velocity that is within 3% of the experimentally measured value of 21.2 m/s (at 18.5 degrees C). Having eliminated most inaccuracies associated with the velocity-dependent rising phase of the action potential, the model is used to test Hodgkin's maximum velocity hypothesis, which asserts that channel density has evolved to maximize conduction velocity. In fact the predicted optimal channel density is more than twice as high as the actual squid channel density. When the available capacitance is reduced to approximate more modern serial Na(+)-channel models, the optimal channel density is 4 times the actual value. We suggest that, although Hodgkin's maximum velocity hypothesis is acceptable as a first approximation, the microscopic optimization perspective of natural selection will not explain the channel density of the squid unless other constraints are taken into account, for example, the metabolic costs of velocity.

Animals↗

PubMed2004

Configural representations in transverse patterning with a hippocampal model.

The hippocampus is necessary in both humans and rats for learning configural representations in tasks such as transverse patterning. The transverse patterning task, (A+B-, B+C-, C+A-), requires representing individual stimuli in the context of other stimuli. This paper extends a previous application to rat data [INNS World Congress on Neural Networks, 1995; Biol Cybern 6 (1998a) 203] by applying a model of the CA3 region of the hippocampus to human data. A decision function is also added that enables the system to choose among training items. Analysis of the simulations show that configural representations are formed by unique neural codes that depend on temporal and stimuli context. Based on the simulations, we hypothesize that configural representations in biological networks depend on a proper balance of input and context representations. Furthermore, the division of labor between functions in the model is a specific working hypothesis that in learning this task the hippocampus specializes in sequence prediction and the decision function evaluates the predictions.

Hippocampus↗

PubMed2002

Energy-efficient neuronal computation via quantal synaptic failures.

Organisms evolve as compromises, and many of these compromises can be expressed in terms of energy efficiency. For example, a compromise between rate of information processing and the energy consumed might explain certain neurophysiological and neuroanatomical observations (e.g., average firing frequency and number of neurons). Using this perspective reveals that the randomness injected into neural processing by the statistical uncertainty of synaptic transmission optimizes one kind of information processing relative to energy use. A critical hypothesis and insight is that neuronal information processing is appropriately measured, first, by considering dendrosomatic summation as a Shannon-type channel (1948) and, second, by considering such uncertain synaptic transmission as part of the dendrosomatic computation rather than as part of axonal information transmission. Using such a model of neural computation and matching the information gathered by dendritic summation to the axonal information transmitted, H(p*), conditions are defined that guarantee synaptic failures can improve the energetic efficiency of neurons. Further development provides a general expression relating optimal failure rate, f, to average firing rate, p*, and is consistent with physiologically observed values. The expression providing this relationship, f approximately 4(-H(p*)), generalizes across activity levels and is independent of the number of inputs to a neuron.

Computer Simulation↗