Spike propagation in axons under stretch growth conditions in cultured neurons from dorsal root ganglion
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Computational software NEURON was used to simulate the stretch growth neurons in order to investigate the ability of dorsal root ganglion neurons to generate and propagate action potentials after a period of rapid axon stretch growth in vitro, and under what stimulating parameters can evoke action potentials. In the simulation, we found the stretch growth neuron had higher spike amplitude than from the static culture neuron in the soma and all axonal branch. In addition, the conduction velocity was also faster in the stretch growth axon. When the stimulating frequency was less than 15 Hz or the stimulating voltage was lower than 15 mV, no spike was evoked. Increasing stimulating frequency from 15 Hz to 5000 Hz or stimulating voltage from 15 mV to 100 mV had almost no effect on the spike amplitude. Interestingly, the first spike time and absolute refractory period (ARP) in different axonal branches and somas decreased stepwise with incremental increase in the stimulating frequency. It is concluded that the stretch growth neuron had higher amplitude and faster conduction velocity than the static culture neuron. In addition, some stimulating parameters had been analyzed in this study, which provided guidelines for electrophysiological experiments in future.Keywords:
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Abstract The Neuron model envisions a three-compartment neuron with dendrite, soma, and axon compartments. This chapter describes the equations used to emulate macroscopic conductances arising from a large variety of sodium and potassium channels. In addition, equations for the electrotonic spread of current between compartments are presented.
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Abstract B uchthal , F. and L. E ngbæek , Refractory Period and conduclion velocity of the striated muscle fibre . Acta physiol. scand. 1963. 59 . 199–220. —Refractory period and conduction velocity of transmembrane potentials were determined in single frog muscle fibres by intracellular stimulation and recording at 14, 20 and 25° C. At the end of the absolutely refractory period the latency of the potential evoked by the second stimulus was substantially increased mainly due to a reduction in conduction velocity, delayed firing causing at most 10 per cent of the total delay. The absolutely refractory period terminated at the onset of the negative after potential, recovery in excitability, amplitude and conduction velocity in the relatively refractory period occurred within a time interval in which the membrane potential did not change more than about 3 mV. At 2–3 times the absolutely refractory period excitability and conduction velocity had a supernormal phase. At the absolutely refractory period the level of depolarization required to initiate a propagated response had increased from 40 to more than 60 mV (recorded 0.25–0.6 mm from the stimulating electrode). A local response appeared at a time interval of 70–80 per cent of the absolutely refractory period of the propagated response.
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The elementary processing units in brain are neurons which are connected to each other in many shapes and sizes. A typical neuron can be divided into functionally three distinct parts called Dendrites, Soma and Axon. Dendrites play the role of input device that collect signals from other neurons and transmits them to soma. Soma performs a Non-linear operation, i.e. if input exceeds a certain threshold, an output signal is generated. This output signal is taken over by an output device, the Axon, which delivers the signal to other neurons. This is the basic function of a biological neuron. A biological neuron model which is also known as Spiking Neuron Model is a mathematical description of properties of neuron that is to be designed accurately to describe and predict the biological processes. So there comes the concept of modelling and analysis of neurons. Modelling and analysis of neurons was performed by different researchers on First, Second and Third generation of neurons. The Third generation of neurons are also called as spiking neurons. The focus of this work is to implement different types of spiking neuron models developed by Izhikevich which is a mathematical model and Hodgkin and Huxley which is a biological model. Comparison between these two models in terms of Design implementation has been done. These both model simulations are done in MATLAB and they are modelled using digital logic circuits in Verilog Hardware Description Language (HDL) and simulated in ModelSIM RTL simulator. These models are then implemented in Xilinx FPGA and checked for the functionality.
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Event Abstract Back to Event Evaluating dendritic impact using complex and reduced models of medium spiny neurons Robert Lindroos1, Jan Pieczkowski1, 2*, Kai Du3 and Jeanette Hellgren Kotaleski1, 3 1 KTH - Royal Institute of Technology, School of Computer Science and Communication, Sweden 2 University of Edinburgh, School of Informatics, United Kingdom 3 Karolinska Institute, Department of Neuroscience, Sweden Current advances in both experimental and theoretical fields have found that synaptic signals are not simply relayed passively to the soma or the axon; instead, dendrites, the main structure to receive synaptic inputs, can act as "computing units", performing arithmetic operations by themselves. However, to model neurons with active dendrites will lead to dramatically increased computing costs. In contrast, simple point-like artificial neuron models do not capture the full dynamics of individual neurons as they do not take into account dendritic computation. This lost accuracy, on the other hand, might play an important role in the overall dynamics of neural networks. To bridge this gap between the point-neuron models and very complex neuron models and to better understand how dendritic computation might affect signal integration at more macroscopic levels, we recently developed a biophysically detailed model of medium spiny neuron (MSN) in dorsal striatum with 634 compartments. An early version of this model has been confirmed to reproduce experimental findings [Evans et al. (2012)]. We derived a series of simplified versions of the model with a reduced number of compartments but conserved 3-dimensional morphology. With the complex model and its reduced offsprings, we explore the importance of dendritic morphology and synaptic topology on the input-output relationship of MSNs. For this purpose, we adopt a novel method by [Chen et al. (2011)], which combines metric space analysis and multidimensional scaling analysis, to quantify the impact of the dendrites. We also apply this method, as well as select techniques from information theory to verify the reduced models' behaviour. References 1. Evans, R.C.; Morera-Herreras, T.; Cui, Y.; Du, K.; Sheehan, T.; Hellgren Kotaleski, J.; Venance, L.; Blackwell, K.T. (2012). The effects of NMDA subunit composition on calcium influx and spike timing-dependent plasticity in striatal medium spiny neurons. PLoS Computational Biology 8(4) 2. Chen, J.-Y. (2010). A Simulation Study Investigating the Impact of Dendritic Morphology and Synaptic Topology on Neuronal Firing Patterns. Neural Computation 22 Keywords: Dendrite, Information Theory, compartmental models, Medium Spiny Neuron, spike train analysis, metric space, multidimensional scaling Conference: Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013. Presentation Type: Poster Topic: Computational neuroscience Citation: Lindroos R, Pieczkowski J, Du K and Hellgren Kotaleski J (2013). Evaluating dendritic impact using complex and reduced models of medium spiny neurons. Front. Neuroinform. Conference Abstract: Neuroinformatics 2013. doi: 10.3389/conf.fninf.2013.09.00118 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 08 Apr 2013; Published Online: 11 Jul 2013. * Correspondence: Mr. Jan Pieczkowski, KTH - Royal Institute of Technology, School of Computer Science and Communication, Stockholm, Sweden, janpi@kth.se Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Robert Lindroos Jan Pieczkowski Kai Du Jeanette Hellgren Kotaleski Google Robert Lindroos Jan Pieczkowski Kai Du Jeanette Hellgren Kotaleski Google Scholar Robert Lindroos Jan Pieczkowski Kai Du Jeanette Hellgren Kotaleski PubMed Robert Lindroos Jan Pieczkowski Kai Du Jeanette Hellgren Kotaleski Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.
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With the use of a double stimulus technique, nerve fibre velocity range measurements were performed over a single conduction distance in 13 normal subjects and over two conduction distances in another 12 normal subjects. The velocity ranges were found to be dependent upon the conduction distance, owing to unknown refractory period delays. Refractory period values were calculated for the 12 subjects and also direct refractory period distribution measurements were made on 15 normal subjects using a twin stimulus and automatic subtraction technique. Corrections to the velocity range measurements were made upon differing assumptions as to the correlation between refractory period and fibre conduction velocity. It was concluded that a single median value refractory period obtained from the distribution was the best correction to use, based upon the hypothesis that for group A fibres the random scatter of refractory period values is far greater than any variation due to a correlation between refractory period and fibre conduction velocity. It was found important to recognize that calculated values of velocity range are a function not only of the spread of fibre conduction velocities but also of refractory periods.
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The rudimentary cells of the central nervous system are the neurons which are connected to each other. An ordinary neuron consists of three different parts Dendrites, Soma and Axon. Each part is having its role in transferring the information. The connection between the neurons can be either Dendrite-Axon or Dendrite-Dendrite or Axon-Axon. Dendrites have the pivotal role in collecting the signals from other neurons and transmitting them to soma which implies that the dendrites act as an input device to the neuron. Soma performs a Non-linear operation, i.e. if input exceeds a certain threshold, an output signal is generated. The Axon performs the role of an output device which takes the processed signal from soma and transmitting it to the other neurons. This is the basic function of a biological neuron. A biological neuron model which is also known as Spiking Neuron Model is a mathematical description of properties of neuron that is to be designed accurately to describe and predict the biological processes. So there comes the concept of modelling and analysis of neurons. Modelling and analysis of neurons was performed by different researchers on First, Second and Third generation of neurons. The Third generation of neurons are also called as spiking neurons. The objective of this work is to implement different types of spiking neuron models developed by Hodgkin and Huxley which is a biological model. The spiking neuron model simulations are done in MATLAB and they are modelled using digital logic circuits in Verilog Hardware Description Language (HDL) and simulated in ModelSIM RTL simulator. These models are then implemented in Xilinx FPGA and checked for the functionality.
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We studied the relationship between the atrioventricular nodal functional refractory period (FRP) and refractoriness by mathematical analysis and by measurement during antegrade Wenckebach cycles in 16 dogs. The FRP relates directly to the conduction time of the control beat, and inversely to the coordinates of the point on the A'-H' vs. A-A refractory curve where the slope is -1. The FRP can vary without any change in refractoriness as measured by the effective refractory period (ERP) or the refractory curve. In 16 dogs the ERP and the FRP were measured during 4:3 Wenckebach cycles. Because of changes in the control conduction times, the FRP declined and did not reflect the progressive increase in refractoriness recorded during Wenckebach cycles. The FRP is a complex parameter and does not reliably measure refractoriness.
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The neuron is roughly divided into three parts: soma, dendrite, and an axon. In this paper, a multi-compartment neuron model the dynamics of which is described by an asynchronous cellular automaton is presented. It is shown that the model can reproduce typical propagations of action potentials from dendrites to a soma (forward propagation) and from a soma to dendrites (backward propagation).
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