355 research outputs found

    Neuronal morphologies built for reliable physiology in a rhythmic motor circuit

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    © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in eLife 8 (2019): e41728. doi: 10.7554/eLife.41728.It is often assumed that highly-branched neuronal structures perform compartmentalized computations. However, previously we showed that the Gastric Mill (GM) neuron in the crustacean stomatogastric ganglion (STG) operates like a single electrotonic compartment, despite having thousands of branch points and total cable length >10 mm (Otopalik et al., 2017a; 2017b). Here we show that compact electrotonic architecture is generalizable to other STG neuron types, and that these neurons present direction-insensitive, linear voltage integration, suggesting they pool synaptic inputs across their neuronal structures. We also show, using simulations of 720 cable models spanning a broad range of geometries and passive properties, that compact electrotonus, linear integration, and directional insensitivity in STG neurons arise from their neurite geometries (diameters tapering from 10-20 µm to < 2 µm at their terminal tips). A broad parameter search reveals multiple morphological and biophysical solutions for achieving different degrees of passive electrotonic decrement and computational strategies in the absence of active properties.We thank Jennifer Bestman for assistance in spinning disk and confocal microscopy; the Marine Resources Center at the Marine Biological Laboratories for acquiring and maintaining animals; Louie Kerr at the Central Microscopy Facility; Dana Mock-Munoz de Luna for administrative support; Kam-ran Kodhakhah, Heather Rhodes, and the 2017 Grass Fellows for their support and feedback; and lastly, Edward Dougherty at the Brandeis University Confocal Imaging Lab for support and microscope maintenance. This study was funded by the Grass Foundation and NINDS awards to F31NS092126 to AO and R35NS097343 to EM

    Asymmetry in Signal Propagation between the Soma and Dendrites Plays a Key Role in Determining Dendritic Excitability in Motoneurons

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    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

    Aerospace medicine and biology. A continuing bibliography with indexes, supplement 224

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    This bibliography lists 127 reports, articles, and other documents introduced into the NASA scientific and technical information system in September 1981

    Generation of the complex spike in cerebellar Purkinje cells.

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    Each neuron of the nervous system is a machine specialised to appropriately transform its synaptic inputs into a pattern of spiking output. This is achieved through the combination of specialisations in synaptic properties and location, passive cell geometry and placement of particular active ion channels. The challenge presented to the neuroscientist is to, within each cell type, identify such specialisations in input distribution and resulting active events, and assess their relative importance in the generation of action potential output patterns. The Purkinje cell, in particular its response to climbing fibre (CF) input, is an excellent setting in which to attempt to meet this challenge. The Purkinje cell receives a single, easily isolated CF axon, which makes hundreds of synapses across the cell's highly branched, active dendritic tree, resulting in the generation of prominent dendritic calcium spikes and a distinctive, reproducible burst of fast action potentials (the complex spike) at the soma. In this thesis I have separated out the importance of the size of this input, its location and the active dendritic spikes it triggers in the generation of the complex spike. I have found that, to a large extent, the complex spike pattern is determined by the size of the CF input alone. I have characterised the complex spike (its number of spikes, their timing, height and reliability) at both constant physiological frequency and across a range of paired- pulse depression causing intervals. By alternating between whole cell current and voltage clamp in the same cell, I have recorded both the complex spikes and EPSCs generated at certain paired pulse intervals. In this way I have been able to construct the EPSC - complex spike 'input - output' relationship. This demonstrated that there is a straightforward linear transformation between the EPSC input amplitude and the number and timing of spikes in the complex spike. This applies across cells, explaining a large amount of the inter-cell variability in complex spike pattern. Input location and dendritic spikes have surprisingly little influence over the Purkinje cell complex spike. I found that complex spikes generated by dendritically distributed CF input can be reproduced by using conductance clamp to inject CF-like synaptic conductance at the soma. Both CF input and somatic EPSG injection produced complex spike waveforms that can only be easily explained by a model in which spikelets are initiated at a distant site and variably propagated to the soma. By using simultaneous somatic and dendritic recording I have demonstrated that this distant site initiation site is not in the dendrites. Somatic EPSG injection reproduced complex spikes independently of dendritic spikes, and extra dendritic spikes triggered by CF stimulation were associated with only 0.24 0.09 extra somatic spikelets in the complex spike. Rather, I have found that dendritic spikes, generated reliably by the dendritic location of CF inputs, have a role in regulating the post-complex spike pause. An extra dendritic spike generates a 3.4 0.7 mV deeper AHP and a 52 11 % longer pause before spontaneous spiking resumed. In this way, I have identified specialisations that encode the size, and thus timing, of CF inputs in the complex spike burst, whilst allowing the dendritic excitation of Purkinje cells (which is strongly associated synaptic and intrinsic plasticity) to be simultaneously encoded in the post-complex spike pause. This may reflect the complex spike's proposed dual role in both controlling ongoing movement and correcting for motor errors

    Neural field models with a dendritic dimension

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    Neural field models (NFMs) describe the spatio-temporal evolution of neuronal populations as a continuous excitable medium. The resulting tissue-level description can be employed to fit data from macroscopic recordings of electrocortical brain ac-tivity like the electroencephalogram (EEG) and local field potentials (LFPs). The standard neural field approach models the cortex as a two-dimensional sheet, ne-glecting the actual cortical depth. Although a small number of studies have con-sidered the anatomical cortical layers to model different connectivity patterns, their mathematical description does not commonly use the cortical depth to determine the model dynamics. Therefore, within the framework of neural field theory, the impact of dendrites on brain activity remains far from being exhaustively explored. In the present work, we extend the geometry of a two-dimensional (2D) NFM to incorporate a dendritic dimension for the excitatory neural populations, repre-senting the cortical depth. Dendritic trees are modelled as linear cables, spatially discretized in multiple subsections (compartments). Spatio-temporal patterns of the new cortical model are studied for systems consisting of either a single or multiple microcolumns. A powerful approximation, extended from the one for the 2D NFM, is introduced to predict the power spectral density of the mean membrane potential from the Jacobian matrix of the linearized system evaluated at a singular point. Our numerical analysis reveals a variety of dynamics, ranging from those characterized by "flat" power spectra without alpha rhythmicity due to signal loss over the tree, up to sharp alpha resonances corresponding to proximity to a Hopf bifurcation. The research focuses on the identification of plausible EEG dynamics, e.g., those exhibit-ing a dominant alpha activity, conceived as the central rhythm of spontaneous EEG. Crucial to this endeavour has been the careful tuning of key dendritic parameters introduced with the three-dimensional (3D) geometry, such as the "synaptic factor" (i.e. synaptic conductance) and the membrane length constant, and wider parameter sweeps using the Particle Swarm Optimization (PSO) technique. The dynamics are mainly studied for a single microcolumn systems with different dendritic configurations (e.g. varying conductance and length constant) during synchronous and asynchronous synaptic activation in either a single or multiple dendritic domains. Our results explain the impact of key dendritic parameters on the 3D NFM dynamics. Heuristics characterizing these effects can be regarded as representative of the well-known phenomenon of "dendritic democracy", classically indicating the normalisation of post-synaptic somatic potentials compensating for dendritic filtering activity. While several experimental studies have investigated the genesis of this compensation, to date this phenomenon has not be explored concerning a potential interplay with the alpha rhythm. Our findings suggest that physiological conditions enhancing the onset of action potentials in active models also promote alphoid dynamics in our passive neural field models including the dendritic dimension. In particular, synaptic strength has to increase with distance from the soma. We found several parameter configurations giving rise to alpha rhythmicity in the 3D geometry, Dynamical analysis highlights the impact of the key dendritic parameters at different cortical depths on the genesis of alpha rhythm, providing a clearer insight into the dendritic mechanisms and cortical dynamics. Indeed, the model can be used as a valid starting point for NF studies aiming to encompass further dendritic properties, implement more detailed connctivity schemes and incorporate data from depth electrode recordings

    Using the Green's function to simplify and understand dendrites

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    Neurons are endowed with dendrites: tree-like structures that collect and transform inputs. These arborizations are believed to substantially enhance the computational repertoire of neurons. While it has long been known that dendrites are not iso-potential units, only in the last few decades it was shown experimentally that dendritic branches can transform local inputs in a non-linear fashion. This finding led to the subunit hypothesis, which states that within the dendritic tree, inputs arriving in one branch are transformed non-linearly and independently from what happens in other branches. Recent progress in experimental recording techniques shows that this localized dendritic integration contributes to shaping behavior. While it is generally accepted that the dendritic tree induces multiple subunits, many questions remain unanswered. For instance, it is not known how much separation there needs to be between different branches to be able to function as subunits. Consequently, there is no information on how many subunits can coexist along a dendritic arborization. It is also not known what the input-output relation of these subunits would be, or whether these subunits can be modified by input patterns. As a consequence, assessing the effects of dendrites on the workings of networks of neurons remains mere guesswork. During this work, we choose a theory-driven approach to advance our knowledge about dendrites. Theory can help us understand dendrites by deriving accurate, but conceptually simple models of dendrites that still capture their main computational effects. These models can then be analyzed and fully understood, which in turn teaches us how actual dendrites function computationally. Such simple models typically require less computer operations to simulate than highly detailed dendrite models. Hence, they may also increase the speed of network simulations that incorporate dendrites. The Green's function forms the basis for our theory driven approach. We first explored whether it could be used to reduce the cost of simulating dendrite models. One mathematically interesting finding in this regard is that, because this function is defined on a tree graph, the number of equations can be reduced drastically. Nevertheless, we were forced to conclude that reducing dendrites in this way does not yield new information about the subunit hypothesis. We then focused our attention on another way of decomposing the Green's function. We found that the dendrite model obtained in this way reveals much information on the dendritic subunits. In particular, we found that the occurrence of subunits is well predicted by the ratio of input over transfer impedance in dendrites. This allowed us to estimate the number of subunits that can coexist on dendritic trees. We also found that this ratio can be modified by other inputs, in particular shunting conductances, so that the number of subunits on a dendritic tree can be modified dynamically. We finally were able to show that, due to this dynamical increase of the number of subunits, individual branches that would otherwise respond to inputs as a single unit, could become sensitive to different stimulus features. We believe that this model can be implemented in such a way that it simulates dendrites in a highly efficient manner. Thus, after incorporation in standard neural network simulation software, it can substantially improve the accessibility of dendritic network simulations to modelers

    Distribution of Ih Channels and their Function in the Stomatogastric Ganglion

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    Generation of rhythmic patterns in the absence of descending commands is an essential and powerful trait of many motor networks. Cyclic rhythmic discharges of motoneurons in repeated motor activities like locomotion, mastication and respiration require underlying circuits of neurons, which are called central pattern generators (CPG). This study examined the possible roles of Ih cation channels in the pyloric network of the stomatogastric nervous system, a rhythmically active network of motoneurons that controls movements of the lobster foregut. Of specific interest were the H-current�s involvement in maintaining firing properties, the distribution of Ih channels within the stomatogastric ganglion, and a potential role for Ih in regulation of synaptic strength. I was able to confirm a homeostatic interaction of Ih with A-type potassium channels, where the over-expression of the IA shal gene after RNA injection evoked a compensatory increase of Ih in different motoneuron types. I observed an additional, non-Ih component of the hyperpolarization activated current, which was more likely to occur in shal-RNA and gfp-RNA injected neurons, compared to untreated neurons. Further, I showed that the homeostatic response of Ih increase is unidirectional; overexpression of the Ih protein PIIH did not lead to an increase of IA. In an immunocytochemical study, I found high concentrations of Ih protein localized in the fine neuropil of the stomatogastric ganglion, an area which is rich in synaptic contacts. Finally, I demonstrate a potential role for Ih in regulating synaptic transmission, for which I found evidence in electrophysiological experiments, where the amplitude of inhibitory postsynaptic potentials decreased with increasing activation of Ih
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