The computing dendrite cuntz hermann remme michiel w h torben nielsen benjamin
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Certain functions require 1 that at least one unit in the network have an arbitrarily large receptive field and 2 that the range of synaptic weights be large. Individual approaches are collected to study the aspects of dendrite shape that relate directly to underlying circuit constraints and computation. Remme, Mate Lengyel and Boris S. In der Arbeit von Daniel Lückehe wird ein neues hybrides Verfahren zur Dimensionsreduktion methodisch erarbeitet, analysiert und durch experimentelle assessments mit vorhandenen Methoden verglichen. Nolan Subthreshold resonance contributes to the efficient coding of auditory spatial cues Michiel W. The studies come from many different neural systems and animal species ranging from invertebrates to mammals. Second, we analytically prove that, with a sufficient number of saturating dendritic sub-units, a neuron can compute all functions computable with purely excitatory inputs.

To address these questions we use a binary neuron model and Boolean algebra. Here we review the biophysical determinants of different classes of dendritic operations linear, sublinear and supralinear , how they are measured experimentally, and finally, using a recently published Boolean-based analysis of equivalent dendritic trees Cazé et al. The equivalent structural and mathematical representation of biological neuron is shown in Fig. Because the synaptic activity pattern ultimately determines the neuronal computations, we propose that the elemental computational unit is the neuron rather than the dendrite Cazé et al. The circuits are designed, analyzed and verified on circuit boards.

First, we show that a single non-linear dendritic sub-unit, in addition to the somatic non-linearity, is sufficient to compute linearly non-separable functions. Moreover, analytical methods show under which conditions the expanded computational capacities are generic, i. What position do dendritic morphology and the distributions of synapses and membrane houses over the dendritic tree have in deciding upon the output of a neuron in keeping with its input? Studying the function of dendritic structures has a long tradition in theoretical neuroscience, starting with the pioneering work by Wilfrid Rall in the 1950s. Recent advances in experimental techniques allow us to study dendrites with a new perspective and in greater detail. In the dendritic threshold non-linear neuron model the dendrites of the neuron can be nonlinear. Remme In: Dendritic computation Michiel W. And can saturating dendrites equally expand computa-tional capacity? The present paper tackles these problems which could strengthen or weaken the impact of dendrites on computation.

Remme, Mate Lengyel and Boris S. Musicians searched stimuli containing two lateralized tones for a target timbre and its pitch. Recent advances in experimental techniques allow us to study dendrites with a new perspective and in greater detail. Wadman The effect of dendritic topology on firing patterns in model neurons Arjen van Ooyen, Jacob Duijnhouwer, Michiel W. This well-known tool in engineering measures how well a classification can be approximated, we use exhaustive search to list all possible classifications a neuron, without dendrites, can do and we quantify how close they approximate a linearly non-separable classification. This is a good textbook for modelers, students, fellows, and advanced experts.

Comin, Julian Tejada, Matheus P. We formally prove that implementing the latter architecture is possible with both types of dendritic sub-units whereas the former is only possible with spiking dendrites. Spikelets in pyramidal neurons: action potentials initiated in the axon initial segment that do not activate the soma Martina Michalikova, Michiel W. What role do dendritic morphology andthe distributions of synapses and membrane properties over the dendritic tree have in determining the output of a neuron in response to its input? We then analytically generalize these numerical results to an arbitrary number of non-linear sub-units. The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Fiete The role of ongoing dendritic oscillations in single-neuron dynamics Michiel W.

Nonlinear dendritic integration is thought to increase the computational ability of neurons. This is a good textbook for modelers, students, fellows, and advanced experts. With this broad focus, an overview is given of the diversity of mechanisms that dendrites can employ to shape neural computations. Implications for feature binding models are discussed. Remme, Mate Lengyel and Boris S. The goal of this volume is to provide a rsum of the state-of-the-art in experimental, computational, and mathematical investigations into the functions of dendrites in a variety of neural systems.

This chapter demonstrates that both of these limitations can be overcome in a network of nonlinearly integrating units. The book first looks at morphological properties of dendrites and summarizes the approaches to measure dendrite morphology quantitatively and to actually generate synthetic dendrite morphologies in computer models. Neuronal dendritic trees are complex structures that endow the cell with powerful computing capabilities and allow for high neural interconnectivity. The studies come from many different neural systems and animal species ranging from invertebrates to mammals. Wadman Control of a local neural network by feedforward and feedback inhibition Michiel W.

Point one, we need to maintain multiple inputs as there are multiple dendrites in neuron, second, we need to have threshold units in dendrite regions as well as in soma region, third, algorithm functioning in multiple input and single output environment has to be established. Neuronal dendritic trees are complex structures that endow the cell with powerful computing capabilities and allow for high neural interconnectivity. We also reviewed how such functions can be implemented with either supralinear or sublinear dendrites depending on the spatial mapping of those features within the dendritic tree. While several biologically plausible models based on dendrite thresholds are reported in the previous studies, the hardware implementation of such non-linear neuron models remain as an open problem. It brings together a wide range of studies, with topics ranging from general to system-specific phenomena. Neuronal dendritic bushes are advanced buildings that endow the phone with robust computing features and make allowance for prime neural interconnectivity.