The electrophysiological properties of somatic and dendritic membranes of CA1 pyramidal neurons were investigated using the rat in vitro hippocampal slice preparation. A comprehensive analysis of extracellular field potentials, current-source density (CSD) and intracellular activity has served to identify the site of origin of action potential (AP) discharge in CA1 pyramidal neurons. 1) Action potential discharge of CA1 pyramidal cells was evoked by suprathreshold stimulation of the alveus (antidromic) or afferent synaptic inputs in stratum oriens (SO) or stratum radiatum (SR). Laminar profiles of the "stimulus evoked" extracellular field potentials were recorded at 25µm intervals along the dendro-somatic axis of the pyramidal cell and a 1-dimensional CSD analysis applied. 2) The shortest latency population spike response and current sink was recorded in stratum pyramidale or the proximal stratum oriens, a region corresponding to somata and axon hillocks of CA1 pyramidal neurons. A biphasic positive/negative spike potential (current source/sink) was recorded in dendritic regions, with both components increasing in peak latency through the dendritic field with distance from the border of stratum pyramidale. 3) A comparative intracellular analysis of evoked activity in somatic and dendritic membranes revealed a basic similarity in the pattern of AP discharge at all levels of the dendro-somatic axis. Stimulation of the alveus, SO, or SR evoked a single spike while injection of depolarizing current evoked a repetitive train of spikes grouped for comparative purposes into three basic patterns of AP discharge. 4) Both current and stimulus evoked intracellular spikes displayed a progressive decline in amplitude and increase in halfwidth with distance from the border of stratum pyramidale. 5) The only consistent voltage threshold for intracellular spike discharge was found in the region of the cell body, with no apparent threshold for spike activation in dendritic locations. 6) Stimulus evoked intradendritic spikes were evoked beyond the peak of the population spike recorded in stratum pyramidale, and aligned with the biphasic extradendritic field potential shown through laminar profile analysis to conduct with increasing latency from the cell body layer. The evoked characteristics of action potential discharge in CA1 pyramidal cells are interpreted to indicate the initial generation of a spike in the region of the soma-axon hillock and a subsequent retrograde spike invasion of dendritic arborizations. / Medicine, Faculty of / Cellular and Physiological Sciences, Department of / Graduate

Wing-Keung Sim. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 109-114). / Abstracts in English and Chinese. / Abstract --- p.2 / 摘要 --- p.3 / Acknowledgements --- p.4 / Contents --- p.5 / List of Figures --- p.8 / List of Tables --- p.9 / Introductions --- p.10 / Chapter 1.1 --- Motivation & Aims --- p.10 / Chapter 1.2 --- Contributions --- p.11 / Chapter 1.3 --- Structure of Thesis --- p.11 / Electrophysiological Spike Discrimination --- p.13 / Chapter 2.1 --- Introduction --- p.13 / Chapter 2.2 --- Cellular Physiology --- p.13 / Chapter 2.2.1 --- Action Potential --- p.13 / Chapter 2.2.2 --- Recording of Spikes Activities --- p.15 / Chapter 2.2.3 --- Demultiplexing of Multi-Neuron Recordings --- p.17 / Chapter 2.3 --- Application of Clustering for Mixed Spikes Train Separation --- p.17 / Chapter 2.3.1 --- Design Principles for Spike Discrimination Procedures --- p.17 / Chapter 2.3.2 --- Clustering Analysis --- p.18 / Chapter 2.3.3 --- Comparison of Clustering Techniques --- p.19 / Chapter 2.4 --- Literature Review --- p.19 / Chapter 2.4.1 --- Template Spike Matching --- p.19 / Chapter 2.4.2 --- Reduced Feature Matching --- p.20 / Chapter 2.4.3 --- Artificial Neural Networks --- p.21 / Chapter 2.4.4 --- Hardware Implementation --- p.21 / Chapter 2.5 --- Summary --- p.22 / Correlation of Perceived Headphone Sound Quality with Physical Parameters --- p.23 / Chapter 3.1 --- Introduction --- p.23 / Chapter 3.2 --- Sound Quality Evaluation --- p.23 / Chapter 3.3 --- Headphone Characterization --- p.26 / Chapter 3.3.1 --- Frequency Response --- p.26 / Chapter 3.3.2 --- Harmonic Distortion --- p.26 / Chapter 3.3.3 --- Voice-Coil Driver Parameters --- p.27 / Chapter 3.4 --- Statistical Correlation Measurement --- p.29 / Chapter 3.4.1 --- Correlation Coefficient --- p.29 / Chapter 3.4.2 --- t Test for Correlation Coefficients --- p.30 / Chapter 3.5 --- Summary --- p.31 / Algorithms --- p.32 / Chapter 4.1 --- Introduction --- p.32 / Chapter 4.2 --- Principal Component Analysis --- p.32 / Chapter 4.2.1 --- Dimensionality Reduction --- p.32 / Chapter 4.2.2 --- PCA Transformation --- p.33 / Chapter 4.2.3 --- PCA Implementation --- p.36 / Chapter 4.3 --- Traditional Clustering Methods --- p.37 / Chapter 4.3.1 --- Online Template Matching (TM) --- p.37 / Chapter 4.3.2 --- Online Template Matching Implementation --- p.40 / Chapter 4.3.3 --- K-Means Clustering --- p.41 / Chapter 4.3.4 --- K-Means Clustering Implementation --- p.44 / Chapter 4.4 --- Unsupervised Neural Learning --- p.45 / Chapter 4.4.1 --- Neural Network Basics --- p.45 / Chapter 4.4.2 --- Artificial Neural Network Model --- p.46 / Chapter 4.4.3 --- Simple Competitive Learning (SCL) --- p.47 / Chapter 4.4.4 --- SCL Implementation --- p.49 / Chapter 4.4.5 --- Adaptive Resonance Theory Network (ART). --- p.50 / Chapter 4.4.6 --- ART2 Implementation --- p.53 / Chapter 4.6 --- Summary --- p.55 / Experimental Design --- p.57 / Chapter 5.1 --- Introduction --- p.57 / Chapter 5.2 --- Electrophysiological Spike Discrimination --- p.57 / Chapter 5.2.1 --- Experimental Design --- p.57 / Chapter 5.2.2 --- Extracellular Recordings --- p.58 / Chapter 5.2.3 --- PCA Feature Extraction --- p.59 / Chapter 5.2.4 --- Clustering Analysis --- p.59 / Chapter 5.3 --- Correlation of Headphone Sound Quality with physical Parameters --- p.61 / Chapter 5.3.1 --- Experimental Design --- p.61 / Chapter 5.3.2 --- Frequency Response Clustering --- p.62 / Chapter 5.3.3 --- Additional Parameters Measurement --- p.68 / Chapter 5.3.4 --- Listening Tests --- p.68 / Chapter 5.3.5 --- Confirmation Test --- p.69 / Chapter 5.4 --- Summary --- p.70 / Results --- p.71 / Chapter 6.1 --- Introduction --- p.71 / Chapter 6.2 --- Electrophysiological Spike Discrimination: A Comparison of Methods --- p.71 / Chapter 6.2.1 --- Clustering Labeled Spike Data --- p.72 / Chapter 6.2.2 --- Clustering of Unlabeled Data --- p.78 / Chapter 6.2.3 --- Remarks --- p.84 / Chapter 6.3 --- Headphone Sound Quality Control --- p.89 / Chapter 6.3.1 --- Headphones Frequency Response Clustering --- p.89 / Chapter 6.3.2 --- Listening Tests --- p.90 / Chapter 6.3.3 --- Correlation with Measured Parameters --- p.90 / Chapter 6.3.4 --- Confirmation Listening Test --- p.92 / Chapter 6.4 --- Summary --- p.93 / Conclusions --- p.97 / Chapter 7.1 --- Future Work --- p.98 / Chapter 7.1.1 --- Clustering Analysis --- p.98 / Chapter 7.1.2 --- Potential Applications of Clustering Analysis --- p.99 / Chapter 7.2 --- Closing Remarks --- p.100 / Appendix --- p.101 / Chapter A.1 --- Tables of Experimental Results: (Spike Discrimination) --- p.101 / Chapter A.2 --- Tables of Experimental Results: (Headphones Measurement) --- p.104 / Bibliography --- p.109 / Publications --- p.114

Thesis (Ph. D.)--Cornell University, January, 2007. / Vita. Includes bibliographical references.

Extracellular potentials due to firing of action potentials are computed around cortical neurons and populations of cortical neurons. These extracellular potentials are calculated as a sum of contributions from ionic currents passing through the cell membrane at various locations using Maxwell's equations in the quasi-static limit. These transmembrane currents are found from simulations of anatomically reconstructed cortical neurons implemented as multi-compartmental models in the simulation tool NEURON. Extracellular signatures of action potentials of single neurons are calculated both in the immediate vicinity of the neuron somas and along vertical axes. For the neuronal populations only vertical axis distributions are considered. The vertical-axis calculations were performed to investigate the contributions of action potential firing to laminar-electrode recordings. Results for high-pass (750 - 3000 Hz) filtered potentials are also given to mimic multi-unit activity (MUA) recordings. Extracellular traces from single neurons and populations (both synchronous and asynchronous) of neurons are shown for three different neuron types: layer 3 pyramid, layer 4 stellate and layer 5 pyramid cell. The layer 3 cell shows a 'closed-field' configuration, while the layer 5 pyramid demonstrates an 'open-field' appearance for singe neuron simulations which is less apparent in population simulations. The layer 4 stellate cell seems to fall somewhere in between the open- and closed-field scenarios. Comparing single neuron and synchronous populations, the amplitudes of the extracellular traces increase as population radii increase, though the shapes are generally similar. Asynchronous populations produce small amplitudes due to a time convolution of various neuron contributions. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2005

Thesis--Copenhagen University. / Summary in Danish. Bibliography: p. 153-161.

Beginning with the introduction of electrically powered prostheses more than 65 years ago surface electromyographic (sEMG) signals recorded from residual muscles in amputated limbs have served as the primary source of upper-limb myoelectric prosthetic control. The majority of these devices use one or more neural interfaces to translate the sEMG signal amplitude into voltage control signals that drive the mechanical components of a prosthesis. In so doing, users are able to directly control the speed and direction of prosthetic actuation by varying the level of muscle activation and the associated sEMG signal amplitude. Consequently, in spite of decades of development, myoelectric prostheses are prone to highly variable functional control, leading to a relatively high-incidence of prosthetic abandonment among 23-35% of upper-limb amputees. Efforts to improve prosthetic control in recent years have led to the development and commercialization of neural interfaces that employ pattern recognition of sEMG signals recorded from multiple locations on a residual limb to map different intended movements. But while these advanced algorithms have made strident gains, there still exists substantial need for further improvement to increase the reliability of pattern recognition control solutions amongst the variability of muscle co-activation intensities. In efforts to enrich the control signals that form the basis for myoelectric control, I have been developing advanced algorithms as part of a next generation neural interface research and development, referred to as Motor Unit Drive (MU Drive), that is able to non-invasively extract the firings of individual motor units (MUs) from sEMG signals in real-time and translate the firings into smooth biomechanically informed control signals. These measurements of motor unit firing rates and recruitment naturally provide high-levels of motor control information from the peripheral nervous system for intact limbs and therefore hold the greater promise for restoring function for amputees. The goal for my doctoral work was to develop advanced algorithms for the MU Drive neural interface system, that leverage MU features to provide intuitive control of multiple degrees-of-freedom. To achieve this goal, I targeted 3 research aims: 1) Derive real-time MU-based control signals from motor unit firings, 2) Evaluate feasibility of motor unit action potential (MUAP) based discrimination of muscle intent 3) Design and evaluate MUAP-based motion Classification of motions of the arm and hand.

There have been many different studies performed in order to examine various properties of neurons. One of the most important properties of neurons is an ability to originate and propagate action potential. The action potential is a source of communication between different neural structures located in different anatomical regions. Many different studies use modeling to describe the action potential and its properties. These models mathematically describe physical properties of neurons and analyze and explain biological and electrochemical processes such as action potential initiation and propagation. Therefore, one of the most important functions of neurons is an ability to provide communication between different neural structures located in different anatomical regions. This is achieved by transmitting electrical signals from one part of the body to another. For example, neurons transmit signals from the brain to the motor neurons (efferent neurons) and from body tissues back to the brain (afferent neurons). This communication process is extremely important for a being to function properly. One of the most valuable studies in neuroscience was conducted by Alan Hodgkin and Andrew Huxley. In their work, Alan Hodgkin and Andrew Huxley used a giant squid axon to create a mathematical model which analyzes and explains the ionic mechanisms underlying the initiation and propagation of action potentials. They received the 1963 Nobel Prize in Physiology/Medicine for their valuable contribution to medical science. The Hodgkin and Huxley model is a mathematical model that describes how the action potential is initiated and how it propagates in a neuron. It is a set of nonlinear ordinary differential equations that approximates the electrical characteristics of excitable cells such as neurons and cardiomyocytes. This work focuses on modeling the Hodgkin and Huxley model using MATLAB extension - Simulink. This tool provides a graphical editor, customizable block libraries, and solvers for modeling and simulating dynamic systems. Simulink model is used to describe the mechanisms and underlying processes involved in action potential initiation and propagation.

Thesis (Sc.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1979. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Vita. / Includes bibliographies. / by Binseng Wang. / Sc.D.

Diss. (sammanfattning) Umeå : Umeå universitet, 2009. / Härtill 3 uppsatser.

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (p. 113-121).