35 research outputs found
Asymmetry in Signal Propagation between the Soma and Dendrites Plays a Key Role in Determining Dendritic Excitability in Motoneurons
It is widely recognized that propagation of electrophysiological signals between the soma and dendrites of neurons differs depending on direction, i.e. it is asymmetric. How this asymmetry influences the activation of voltage-gated dendritic channels, and consequent neuronal behavior, remains unclear. Based on the analysis of asymmetry in several types of motoneurons, we extended our previous methodology for reducing a fully reconstructed motoneuron model to a two-compartment representation that preserved asymmetric signal propagation. The reduced models accurately replicated the dendritic excitability and the dynamics of the anatomical model involving a persistent inward current (PIC) dispersed over the dendrites. The relationship between asymmetric signal propagation and dendritic excitability was investigated using the reduced models while varying the asymmetry in signal propagation between the soma and the dendrite with PIC density constant. We found that increases in signal attenuation from soma to dendrites increased the activation threshold of a PIC (hypo-excitability), whereas increases in signal attenuation from dendrites to soma decreased the activation threshold of a PIC (hyper-excitability). These effects were so strong that reversing the asymmetry in the soma-to-dendrite vs. dendrite-to-soma attenuation, reversed the correlation between PIC threshold and distance of this current source from the soma. We propose the tight relation of the asymmetric signal propagation to the input resistance in the dendrites as a mechanism underlying the influence of the asymmetric signal propagation on the dendritic excitability. All these results emphasize the importance of maintaining the physiological asymmetry in dendritic signaling not only for normal function of the cells but also for biophysically realistic simulations of dendritic excitability. © 2014 Kim et al.1
Peculiarities of the Tail-Withdrawal Reflex Circuit in Aplysia: a Model Study
The circuit of the tail-withdrawal reflex in Aplysia opens up possibilities to construct model
systems allowing researchers to effectively investigate simple forms of learning and memory.
Using the Python interface of the NEURON software, we simulated this reflex circuit and
studied various characteristics of the latter. The phenomenon of spike frequency adaptation
(SFA) and the period-adding bifurcation of the minimum were found in sensory neurons,
when the latter were stimulated by square-wave stimuli. In all neurons of the circuit, variation
of the stimulus strength first increased and then decreased the number of spikes in a burst.
In addition, with decreases in the number of stimulated sensory neurons, a subliminal firing
other than that in an intact burst appeared at the outputs of interneurons and motor neuron.
Moreover, the potentials produced in the motor neuron induced corresponding oscillations
of the muscle fiber force, which was indicative of a procedure of excitement-contraction
coupling in the tail part of Aplysia. Finally, upon alteration of the conductance of synapses
between interneurons and motoneuron, the duration of long-lasting responses increased
regularly, implying synaptic plasticityОрганізація нервової мережі відсмикування „хвоста” в аплізії дозволяє побудувати модельну систему, за допомогою
якої можна ефективно досліджувати прості форми навчання
та пам’яті. Використовуючи інтерфейс Python та програмний засіб NEURON, ми змоделювали даний рефлекс та дослідили декілька властивостей модельної мережі. Феномени адаптації частоти розряду (SFA) та біфуркації з доданням
періоду при мінімумі частоти спостерігалися в сенсорних
нейронах в умовах стимуляції прямокутними стимулами.
В усіх нейронах мережі зміни сили стимуляції призводили спочатку до збільшення числа піків у пачках, а потім до
його зменшення. Окрім того, при зменшенні кількості стимульованих сенсорних нейронів на виходах інтернейронів
та моторного нейрона з’являлася підпорогова кайма, що відрізнялася від такої в інтактних пачок. Більш того, потенціали, продуковані моторним нейроном, індукували відповідні
осциляції сили, розвинутої м’язовим волокном, що свідчило
про сполучення процесів збудження/скорочення у хвостовій
частині аплізії. Нарешті, при змінах провідності синапсів
між інтернейронами та мотонейронами тривалість „довгих”
імпульсних відповідей закономірно збільшувалася, що вказувало на прояви синаптичної пластичності
Viewpoint aggregation via relational modeling and analysis: a new approach to systems physiology
The key to understanding any system, including physiologic and pathologic systems, is to obtain a truly comprehensive view of the system. The purpose of this dissertation was to develop foundational analytical and modeling tools, which would enable such a comprehensive view to be obtained of any physiological or pathological system by combining experimental, clinical, and theoretical viewpoints. Specifically, we focus on the development of analytical and modeling techniques capable of predicting and prioritizing the mechanisms, emergent dynamics, and underlying principles necessary in order to obtain a comprehensive system understanding. Since physiologic systems are inherently complex systems, our approach was to translate the philosophy of complex systems into a set of applied and quantitative methods, which focused on the relationships within the system that result in the system's emergent properties and behavior. The result was a set of developed techniques, referred to as relational modeling and analysis that utilize relationships as either a placeholder or bridging structure from which unknown aspects of the system can be effectively explored. These techniques were subsequently tested via the construction and analysis of models of five very different systems: synaptic neurotransmitter spillover, secondary spinal cord injury, physiological and pathological axonal transport, and amyotrophic lateral sclerosis and to analyze neurophysiological data of in vivo cat spinal motoneurons. Our relationship-based methodologies provide an equivalent means by which the different perspectives can be compared, contrasted, and aggregated into a truly comprehensive viewpoint that can drive research forward.Ph.D.Committee Chair: Lee, Robert; Committee Member: Kemp, Melissa; Committee Member: Prinz, Astrid; Committee Member: Ting, Lena; Committee Member: Wiesenfeld, Kur
Recommended from our members
An infrastructure for neural network construction
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.After many years of research the area of Artificial Intelligence is still searching for ways to construct a truly intelligent system. One criticism is that current models are not 'rich' or complex enough to operate in many and varied real world situations. One way to tackle this criticism is to look at intelligent systems that already exist in nature and examine these to determine what complexities exist in these systems and not in the current Al models. The research begins by presenting an overview of the current knowledge of Biological Neural Networks, as examples of intelligent systems existing in nature, and how they function. Artificial Neural networks are then discussed and the thesis examines their similarities and dissimilarities with their biological counterparts. The research suggests ways that Artificial Neural Networks may be improved by borrowing ideas from Biological Neural Networks. By introducing new concepts drawn from the biological realm, the construction of the Artificial Neural Networks becomes more difficult. To solve this difficulty, the thesis introduces the area of Evolutionary Algorithms as a way of constructing Artificial Neural Networks.
An intellectual infrastructure is developed that incorporates concepts from Biological
Neural Networks into current models of Artificial Neural Networks and two models are developed to explore the concept that increased complexity can indeed add value to the current models of Artificial Neural Networks. The outcome of the thesis shows that increased complexity can have benefits in terms of learning speed of an Artificial Neural Network and in terms of robustness to damage
An infrastructure for neural network construction
After many years of research the area of Artificial Intelligence is still searching for ways to construct a truly intelligent system. One criticism is that current models are not 'rich' or complex enough to operate in many and varied real world situations. One way to tackle this criticism is to look at intelligent systems that already exist in nature and examine these to determine what complexities exist in these systems and not in the current Al models. The research begins by presenting an overview of the current knowledge of Biological Neural Networks, as examples of intelligent systems existing in nature, and how they function. Artificial Neural networks are then discussed and the thesis examines their similarities and dissimilarities with their biological counterparts. The research suggests ways that Artificial Neural Networks may be improved by borrowing ideas from Biological Neural Networks. By introducing new concepts drawn from the biological realm, the construction of the Artificial Neural Networks becomes more difficult. To solve this difficulty, the thesis introduces the area of Evolutionary Algorithms as a way of constructing Artificial Neural Networks. An intellectual infrastructure is developed that incorporates concepts from Biological Neural Networks into current models of Artificial Neural Networks and two models are developed to explore the concept that increased complexity can indeed add value to the current models of Artificial Neural Networks. The outcome of the thesis shows that increased complexity can have benefits in terms of learning speed of an Artificial Neural Network and in terms of robustness to damage.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
A computational framework for similarity estimation and stimulus reconstruction of Hodgkin-Huxley neural responses
Periodic stimuli are known to induce chaotic oscillations in the squid giant axon for a certain range of frequencies, a behaviour modelled by the Hodgkin-Huxley equations. Inthe presence of chaotic oscillations, similarity between neural responses depends on their temporal nature as firing times and amplitudes together reflect the true dynamics of theneuron. This thesis presents a method to estimate similarity between neural responses exhibiting chaotic oscillations by using both amplitude fluctuations and firing times. It isobserved that identical stimuli have similar effect on the neural dynamics and therefore, as the temporal inputs to the neuron are identical, the occurrence of similar dynamicalpatterns result in a high estimate of similarity, which correlates with the observed temporal similarity.The information about a neural activity is encoded in a neural response and usually the underlying stimulus that triggers the activity is unknown. Thus, this thesis also presents anumerical solution to reconstruct stimuli from Hodgkin-Huxley neural responses while retrieving the neural dynamics. The stimulus is reconstructed by first retrieving themaximal conductances of the ion channels and then solving the Hodgkin-Huxley equations for the stimulus. The results show that the reconstructed stimulus is a good approximationof the original stimulus, while the retrieved the neural dynamics, which represent the voltage-dependent changes in the ion channels, help to understand the changes in neuralbiochemistry. As high non-linearity of neural dynamics renders analytical inversion of a neuron an arduous task, a numerical approach provides a local solution to the problem ofstimulus reconstruction and neural dynamics retrieval
Functional organization of cutaneous reflex pathways during locomotion and reorganization following peripheral nerve and/or spinal cord lesions
Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal
25th Annual Computational Neuroscience Meeting: CNS-2016
Abstracts of the 25th Annual Computational Neuroscience
Meeting: CNS-2016
Seogwipo City, Jeju-do, South Korea. 2–7 July 201