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M Zochowski

Publications and source records attributed to M Zochowski.

6 recordsLinked to original sources
PubMed2001

Fractal methods to analyze ion channel kinetics.

We describe the traditional nonfractal and the new fractal methods used to analyze the currents through ion channels in the cell membrane. We discuss the hidden assumptions used in these methods and how those assumptions lead to different interpretations of the same experimental data. The nonfractal methods assumed that channel proteins have a small number of discrete states separated by fixed energy barriers. The goal was to determine the parameters of the kinetic diagram, which are the number of states, the pathways between them, and the kinetic rate constants of those pathways. The discovery that these data have fractal characteristics suggested that fractal approaches might provide more appropriate tools to analyze and interpret these data. The fractal methods determine the characteristics of the data over a broad range of time scales and how those characteristics depend on the time scale at which they are measured. This is done by using a multiscale method to accurately determine the probability density function over many time scales and by determining how the effective kinetic rate constant, the probability of switching states, depends on the effective time scale at which it is measured. These fractal methods have led to new information about the physical properties of channel proteins in terms of the number of conformational substates, the distribution of energy barriers between those states, and how those energy barriers change with time. The new methods developed from the fractal paradigm shifted the analysis of channel data from determining the parameters of a kinetic diagram to determining the physical properties of channel proteins in terms of the distribution of energy barriers and/or their time dependence.

Animals↗
PubMed2000

Distributed and partially separate pools of neurons are correlated with two different components of the gill-withdrawal reflex in Aplysia.

We compared the spike activity of individual neurons in the Aplysia abdominal ganglion with the movement of the gill during the gill-withdrawal reflex. We discriminated four populations that collectively encompass approximately half of the active neurons in the ganglion: (1) second-order sensory neurons that respond to the onset and offset of stimulation of the gill and are active before the movement starts; (2) neurons whose activity is correlated with the position of the gill and typically have a tonic output during gill withdrawal; (3) neurons whose activity is correlated with the velocity of the movement and typically fire in a phasic manner; and (4) neurons whose activity is correlated with both position and velocity. A reliable prediction of the position of the gill is achieved only with the combined output of 15-20 neurons, whereas a reliable prediction of the velocity depends on the combined output of 40 or more cells.

Action Potentials↗
PubMed2000

Imaging membrane potential with voltage-sensitive dyes.

Membrane potential can be measured optically using a variety of molecular probes. These measurements can be useful in studying function at the level of an individual cell, for determining how groups of neurons generate a behavior, and for studying the correlated behavior of populations of neurons. Examples of the three kinds of measurements are presented. The signals obtained from these measurements are generally small. Methodological considerations necessary to optimize the resulting signal-to-noise ratio are discussed.

Animals↗
PubMed1994

Local noise in neural networks models with self-control.

We modify neural networks models of the Hopfield type so that they can recognize the degree of novelty of the input stimuli on a local level. The networks control themselves the quality of recognition and can also recognize locally the bits of information in the input patterns which do not agree with known patterns, i.e. stored memories. This task is achieved by introducing local variations of the noise level beta in the network. Noise level in a given location depends on the flip frequency of the neurons close to that location.

Mathematics↗