Impact of Dendritic Size and Dendritic Topology on Burst Firing in Pyramidal Cells

Neurons display a wide range of intrinsic firing patterns. A particularly relevant pattern for neuronal signaling and synaptic plasticity is burst firing, the generation of clusters of action potentials with short interspike intervals. Besides ion-channel composition, dendritic morphology appears to be an important factor modulating firing pattern. However, the underlying mechanisms are poorly understood, and the impact of morphology on burst firing remains insufficiently known. Dendritic morphology is not fixed but can undergo significant changes in many pathological conditions. Using computational models of neocortical pyramidal cells, we here show that not only the total length of the apical dendrite but also the topological structure of its branching pattern markedly influences inter- and intraburst spike intervals and even determines whether or not a cell exhibits burst firing. We found that there is only a range of dendritic sizes that supports burst firing, and that this range is modulated by dendritic topology. Either reducing or enlarging the dendritic tree, or merely modifying its topological structure without changing total dendritic length, can transform a cell's firing pattern from bursting to tonic firing. Interestingly, the results are largely independent of whether the cells are stimulated by current injection at the soma or by synapses distributed over the dendritic tree. By means of a novel measure called mean electrotonic path length, we show that the influence of dendritic morphology on burst firing is attributable to the effect both dendritic size and dendritic topology have, not on somatic input conductance, but on the average spatial extent of the dendritic tree and the spatiotemporal dynamics of the dendritic membrane potential. Our results suggest that alterations in size or topology of pyramidal cell morphology, such as observed in Alzheimer's disease, mental retardation, epilepsy, and chronic stress, could change neuronal burst firing and thus ultimately affect information processing and cognition

Influence of the dentritic morphology on electrophysiological responses of thalamocortical neurons

Les neurones thalamiques de relai ont un rôle exclusif dans la transformation et de transfert de presque toute l'information sensorielle dans le cortex. L'intégration synaptique et la réponse électrophysiologique des neurones thalamiques de relai sont déterminées non seulement par l’état du réseau impliqué, mais ils sont également contrôlés par leurs propriétés intrinsèques tels les divers canaux ioniques voltage-dépendants ainsi que l’arborisation dendritique élaboré. Par conséquent, investiguer sur le profil complexe de morphologie dendritique et sur les propriétés dendritiques actives révèle des renseignements importants sur la fonction d'entrée-sortie de neurones thalamiques de relai. Dans cette étude, nous avons reconstruit huit neurones thalamocorticaux (TC) du noyau VPL de chat adulte. En se basant sur ces données morphologiques complètes, nous avons développé plusieurs modèles multicompartimentaux afin de trouver un rôle potentiellement important des arbres dendritiques des neurones de TC dans l'intégration synaptique et l’intégration neuronale. L'analyse des caractéristiques morphologiques des neurones TC accordent des valeurs précises à des paramètres géométriques semblables ou différents de ceux publiés antérieurement. En outre, cette analyse fait ressortir de tous nouveaux renseignements concernant le patron de connectivité entre les sections dendritiques telles que l'index de l'asymétrie et la longueur de parcours moyen (c'est-à-dire, les paramètres topologiques). Nous avons confirmé l’étendue des valeurs rapportée antérieurement pour plusieurs paramètres géométriques tels que la zone somatique (2956.24±918.89 m2), la longueur dendritique totale (168017.49±4364.64 m) et le nombre de sous-arbres (8.3±1.5) pour huit neurones TC. Cependant, contrairement aux données rapportées antérieurement, le patron de ramification dendritique (avec des cas de bifurcation 98 %) ne suit pas la règle de puissance de Rall 3/2 pour le ratio géométrique (GR), et la valeur moyenne de GR pour un signal de propagation est 2,5 fois plus grande que pour un signal rétropropagé. Nous avons également démontré une variabilité significative dans l'index de symétrie entre les sous-arbres de neurones TC, mais la longueur du parcours moyen n'a pas montré une grande variation à travers les ramifications dendritiques des différents neurones. Nous avons examiné la conséquence d’une distribution non-uniforme des canaux T le long de l'arbre dendritique sur la réponse électrophysiologique émergeante, soit le potentiel Ca 2+ à seuil bas (low-threshold calcium spike, LTS) des neurones TC. En appliquant l'hypothèse du «coût minimal métabolique», nous avons constaté que le neurone modélisé nécessite un nombre minimal de canaux-T pour générer un LTS, lorsque les canaux-T sont situés dans les dendrites proximales. Dans la prochaine étude, notre modèle informatique a illustré l'étendue d'une rétropropagation du potentiel d'action et de l'efficacité de la propagation vers des PPSEs générés aux branches dendritiques distales. Nous avons démontré que la propagation dendritique des signaux électriques est fortement contrôlée par les paramètres morphologiques comme illustré par les différents paliers de polarisation obtenus par un neurone à équidistance de soma pendant la propagation et la rétropropagation des signaux électriques. Nos résultats ont révélé que les propriétés géométriques (c.-à-d. diamètre, GR) ont un impact plus fort sur la propagation du signal électrique que les propriétés topologiques. Nous concluons que (1) la diversité dans les propriétés morphologiques entre les sous-arbres d'un seul neurone TC donne une capacité spécifique pour l'intégration synaptique et l’intégration neuronale des différents dendrites, (2) le paramètre géométrique d'un arbre dendritique fournissent une influence plus élevée sur le contrôle de l'efficacité synaptique et l'étendue du potentiel d'action rétropropagé que les propriétés topologiques, (3) neurones TC suivent le principe d’optimisation pour la distribution de la conductance voltage-dépendant sur les arbres dendritiques.Thalamic relay neurons have an exclusive role in processing and transferring nearly all sensory information into the cortex. The synaptic integration and the electrophysiological response of thalamic relay neurons are determined not only by a state of the involved network, but they are also controlled by their intrinsic properties; such as diverse voltage-dependent ionic channels as well as by elaborated dendritic arborization. Therefore, investigating the complex pattern of dendritic morphology and dendritic active properties reveals important information on the input-output function of thalamic relay neurons. In this study, we reconstructed eight thalamocortical (TC) neurons from the VPL nucleus of adult cats. Based on these complete morphological data, we developed several multi-compartment models in order to find a potentially important role for dendritic trees of TC neurons in the synaptic integration and neuronal computation. The analysis of morphological features of TC neurons yield precise values of geometrical parameters either similar or different from those previously reported. In addition, this analysis extracted new information regarding the pattern of connectivity between dendritic sections such as asymmetry index and mean path length (i.e., topological parameters). We confirmed the same range of previously reported value for several geometric parameters such as the somatic area (2956.24±918.89 m2), the total dendritic length (168017.49±4364.64 m) and the number of subtrees (8.3±1.5) for eight TC neurons. However, contrary to previously reported data, the dendritic branching pattern (with 98% bifurcation cases) does not follow Rall’s 3/2 power rule for the geometrical ratio (GR), and the average GR value for a forward propagation signal was 2.5 times bigger than for a backward propagating signal. We also demonstrated a significant variability in the symmetry index between subtrees of TC neurons, but the mean path length did not show a large variation through the dendritic arborizations of different neurons. We examined the consequence of non-uniform distribution of T-channels along the dendritic tree on the prominent electrophysiological response, the low-threshold Ca2+ spike (LTS) of TC neurons. By applying the hypothesis of “minimizing metabolic cost”, we found that the modeled neuron needed a minimum number of T-channels to generate low-threshold Ca2+ spike (LTS), when T-channels were located in proximal dendrites. In the next study, our computational model illustrated the extent of an action potential back propagation and the efficacy of forward propagation of EPSPs arriving at the distal dendritic branches. We demonstrated that dendritic propagation of electrical signals is strongly controlled by morphological parameters as shown by different levels of polarization achieved by a neuron at equidistance from the soma during back and forward propagation of electrical signals. Our results revealed that geometrical properties (i.e. diameter, GR) have a stronger impact on the electrical signal propagation than topological properties. We conclude that (1) diversity in the morphological properties between subtrees of a single TC neuron lead to a specific ability for synaptic integration and neuronal computation of different dendrites, (2) geometrical parameter of a dendritic tree provide higher influence on the control of synaptic efficacy and the extent of the back propagating action potential than topological properties, (3) TC neurons follow the optimized principle for distribution of voltage-dependent conductance on dendritic trees

Computational Properties of Cerebellar Nucleus Neurons: Effects of Stochastic Ion Channel Gating and Input Location

The function of the nervous system is shaped by the refined integration of synaptic inputs taking place at the single neuron level. Gain modulation is a computational principle that is widely used across the brain, in which the response of a neuronal unit to a set of inputs is affected in a multiplicative fashion by a second set of inputs, but without any effect on its selectivity. The arithmetic operations performed by pyramidal cells in cortical brain areas have been well characterised, along with the underlying mechanisms at the level of networks and cells, for instance background synaptic noise and dendritic saturation. However, in spite of the vast amount of research on the cerebellum and its function, little is known about neuronal computations carried out by its cellular components. A particular area of interest are the cerebellar nuclei, the main output gate of the cerebellum to the brain stem and cortical areas. The aim of this thesis is to contribute to an understanding of the arithmetic operations performed by neurons in the cerebellar nuclei. Focus is placed on two putative determinants, the location of the synaptic input and the presence of channel noise. To analyse the effect of channel noise, the known voltage-gated ion channels of a cerebellar nucleus neuron model are translated to stochastic Markov formalisms and their electrophysiologial behaviour is compared to their deterministic Hodgkin-Huxley counterparts. The findings demonstrate that in most cases, the behaviour of stochastic channels matches the reference deterministic models, with the notable exception of voltage-gated channels with fast kinetics. Two potential explanations are suggested for this discrepancy. Firstly, channels with fast kinetics are strongly affected by the artefactual loss of gating events in the simulation that is caused by the use of a finite-length time step. While this effect can be mitigated, in part, by using very small time steps, the second source of simulation artefacts is the rectification of the distribution of open channels, when channel kinetics characteristics allow the generation of a window current, with an temporal-averaged equilibrium close to zero. Further, stochastic gating is implemented in a realistic cerebellar nucleus neuronal model. The resulting stochastic model exhibits probabilistic spiking and a similar output rate as the corresponding deterministic cerebellar nucleus neuronal model. However, the outcomes of this thesis indicate the computational properties of the cerebellar nucleus neuronal model are independent of the presence of ion channel noise. The main result of this thesis is that the synaptic input location determines the single neuron computational properties, both in the cerebellar nucleus and layer Vb pyramidal neuronal models. The extent of multiplication increases systematically with the distance from the soma, for the cerebellar nucleus, but not for the layer Vb pyramidal neuron, where it is smaller than it would be expected for the distance from the soma. For both neurons, the underlying mechanism is related to the combined effect of nonlinearities introduced by dendritic saturation and the synaptic input noise. However, while excitatory inputs in the perisomatic areas in the cerebellar nucleus undergo additive operations and the distal areas multiplicative, in the layer Vb pyramidal neuron the integration of the excitatory driving input is always multiplicative. In addition, the change in gain is sensitive to the synchronicity of the excitatory synaptic input in the layer Vb pyramidal neuron, but not in the cerebellar nucleus neuron. These observations indicate that the same gain control mechanism might be utilized in distinct ways, in different computational contexts and across different areas, based on the neuronal type and its function

A combined experimental and computational approach to investigate emergent network dynamics based on large-scale neuronal recordings

Sviluppo di un approccio integrato computazionale-sperimentale per lo studio di reti neuronali mediante registrazioni elettrofisiologich

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)