scholarly journals An active dendritic tree can mitigate fan-in limitations in superconducting neurons

2021 ◽  
Vol 119 (24) ◽  
pp. 242601
Author(s):  
Bryce A. Primavera ◽  
Jeffrey M. Shainline
Keyword(s):  
Dendritic Tree

Early appearance of developmental alterations in the dendritic tree of the hippocampal granule cells in the Ts65Dn model of Down syndrome

Hippocampus ◽  
2021 ◽  
Author(s):  
Beatrice Uguagliati ◽  
Abdel‐Rahman Al‐Absi ◽  
Fiorenza Stagni ◽  
Marco Emili ◽  
Andrea Giacomini ◽  
...  
Keyword(s):  
Down Syndrome ◽  
Granule Cells ◽  
Dendritic Tree ◽  
Early Appearance

COMPLEXITY MAPS REVEAL CLUSTERS IN NEURONAL ARBORIZATIONS

Fractals ◽  
1994 ◽  
Vol 02 (02) ◽  
pp. 297-301
Author(s):  
B. DUBUC ◽  
S. W. ZUCKER ◽  
M. P. STRYKER
Keyword(s):  
Computer Vision ◽  
Fractal Dimension ◽  
Tree Growth ◽  
Local Structure ◽  
Dendritic Tree ◽  
Local Information ◽  
Growth Patterns ◽  
Rate Of Growth ◽  
Local Complexity ◽  
Formal Definitions

A central issue in characterizing neuronal growth patterns is whether their arbors form clusters. Formal definitions of clusters have been elusive, although intuitively they appear to be related to the complexity of branching. Standard notions of complexity have been developed for point sets, but neurons are specialized "curve-like" objects. Thus we consider the problem of characterizing the local complexity of a "curve-like" measurable set. We propose an index of complexity suitable for defining clusters in such objects, together with an algorithm that produces a complexity map which gives, at each point on the set, precisely this index of complexity. Our index is closely related to the classical notions of fractal dimension, since it consists in determining the rate of growth of the area of a dilated set at a given scale, but it differs in two significant ways. First, the dilation is done normal to the local structure of the set, instead of being done isotropically. Second, the rate of growth of the area of this new set, which we named "normal complexity", is taken at a fixed (given) scale instead instead of around zero. The results will be key in choosing the appropriate representation when integrating local information in low level computer vision. As an application, they lead to the quantification of axonal and dendritic tree growth in neurons.

scholarly journals Simulation of Dendritic CaV1.3 Channels in Cat Lumbar Motoneurons: Spatial Distribution

2005 ◽  
Vol 94 (6) ◽  
pp. 3961-3974 ◽  
Author(s):  
Sherif M. ElBasiouny ◽  
David J. Bennett ◽  
Vivian K. Mushahwar
Keyword(s):  
Spatial Distribution ◽  
Low Voltage ◽  
Dendritic Tree ◽  
Triceps Surae ◽  
Spinal Motoneurons ◽  
Functional Interaction ◽  
Intermediate Band ◽  
Synaptic Inputs ◽  
Persistent Inward Current ◽  
Adult Cat

We used computer simulations to study the dendritic spatial distribution of low voltage-activated L-type calcium (CaV1.3 type) channels, which mediate hysteretic persistent inward current (PIC) in spinal motoneurons. This study was prompted by the growing experimental evidence of the functional interactions between synaptic inputs and active conductances over the motoneuron dendritic tree. A compartmental cable model of an adult cat α-motoneuron was developed in NEURON simulation environment constituting the detailed morphology of type-identified triceps surae α-motoneuron and realistic distribution of group Ia afferent-to-motoneuron contacts. Simulations of different distributions of CaV1.3 channels were conducted and the resultant behavior was compared to experimental data. Our results suggest that CaV1.3 channels do not uniformly cover the whole motoneuron dendritic tree. Instead, their distribution is similar to that of synaptic contacts. We found that CaV1.3 channels are primarily localized to a wide intermediate band overlapping with the dendritic Ia-synaptic territory at dendritic distances of 300 to 850 μm (0.62 ± 0.21λ) from the soma in triceps surae α-motoneurons. These findings explain the functional interaction between synaptic inputs and the CaV1.3 channels over the motoneuron dendritic tree.

How Synaptic Release Probability Shapes Neuronal Transmission: Information-Theoretic Analysis in a Cerebellar Granule Cell

2010 ◽  
Vol 22 (8) ◽  
pp. 2031-2058 ◽  
Author(s):  
Angelo Arleo ◽  
Thierry Nieus ◽  
Michele Bezzi ◽  
Anna D'Errico ◽  
Egidio D'Angelo ◽  
...  
Keyword(s):  
Information Transmission ◽  
Numerical Simulations ◽  
Granule Cell ◽  
Mossy Fiber ◽  
Dendritic Tree ◽  
Long Term Potentiation ◽  
Cerebellar Granule Cell ◽  
Information Theoretic ◽  
Cerebellar Granule ◽  
Release Probability

A nerve cell receives multiple inputs from upstream neurons by way of its synapses. Neuron processing functions are thus influenced by changes in the biophysical properties of the synapse, such as long-term potentiation (LTP) or depression (LTD). This observation has opened new perspectives on the biophysical basis of learning and memory, but its quantitative impact on the information transmission of a neuron remains partially elucidated. One major obstacle is the high dimensionality of the neuronal input-output space, which makes it unfeasible to perform a thorough computational analysis of a neuron with multiple synaptic inputs. In this work, information theory was employed to characterize the information transmission of a cerebellar granule cell over a region of its excitatory input space following synaptic changes. Granule cells have a small dendritic tree (on average, they receive only four mossy fiber afferents), which greatly bounds the input combinatorial space, reducing the complexity of information-theoretic calculations. Numerical simulations and LTP experiments quantified how changes in neurotransmitter release probability (p) modulated information transmission of a cerebellar granule cell. Numerical simulations showed that p shaped the neurotransmission landscape in unexpected ways. As p increased, the optimality of the information transmission of most stimuli did not increase strictly monotonically; instead it reached a plateau at intermediate p levels. Furthermore, our results showed that the spatiotemporal characteristics of the inputs determine the effect of p on neurotransmission, thus permitting the selection of distinctive preferred stimuli for different p values. These selective mechanisms may have important consequences on the encoding of cerebellar mossy fiber inputs and the plasticity and computation at the next circuit stage, including the parallel fiber–Purkinje cell synapses.

Dendritic integration in ganglion cells of the mudpuppy retina

1995 ◽  
Vol 12 (1) ◽  
pp. 165-175 ◽  
Author(s):  
T.J. Velte ◽  
R.F. Miller
Keyword(s):  
Functional Organization ◽  
Simulation Analysis ◽  
Ganglion Cells ◽  
Impedance Matching ◽  
Dendritic Tree ◽  
Membrane Resistance ◽  
Transient Responses ◽  
Dendritic Integration ◽  
Necturus Maculosus ◽  
Integration Efficiency

AbstractComputer simulations were carried out to evaluate the influence of varying the membrane resistance (Rm) on the dendritic integration capacity of three classes of ganglion cells in the mudpuppy (Necturus maculosus) retina. Three broadly different morphological classes of ganglion cells were selected for this study and represent the range of dendritic tree sizes found in the ganglion cell population of this species. Simulations were conducted on anatomical data obtained from cells stained with horseradish peroxidase; each cell was traced, using a computer as an entry device and later converted to a compartmental (electrical) representation of the cell. Computer-simulation analysis used a time-variant conductance change which was similar in waveform to light-activated bipolar cell input. The simulated membrane resistance for each cell varied between 5000 and 100,000 Ω cm2, and conductance changes were introduced into different regions of the soma-dendritic tree to evaluate dendritic integration efficiency. When higher values of Rm are used, even the largest cells become electrotonically compact and attenuation of voltage responses is minimized from distal to soma regions. Responses were less attenuated from proximal to distal regions of the cell because of the favorable impedance matching, and because less current is required to polarize small “sealed” dendritic terminations. Steady-state responses integrate more effectively than transient responses, particularly when Rm is high, since transient responses were more attenuated by the membrane capacitance. The possibility that Rm is a dynamic property of retinal ganglion cells is discussed in view of the functional organization of dendritic integration efficiency as Rm fluctuates from low to high values.

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