Benutzer:Adrian Lange/Pulsierendes neuronales Netz

Dieser Artikel ist im Entstehen und noch nicht Bestandteil der freien Enzyklopädie Wikipedia.

Solltest du über eine Suchmaschine darauf gestoßen sein, bedenke, dass der Text noch unvollständig sein und Fehler oder ungeprüfte Aussagen enthalten kann. Wenn du Fragen zum Thema hast, nimm Kontakt mit dem Autor Adrian Lange auf.

Spiking neural networks (SNNs) fall into the third generation of neural network models, increasing the level of realism in a neural simulation. In addition to neuronal and synaptic state, SNNs also incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not fire at each propagation cycle (as it happens with typical multi-layer perceptron networks), but rather fire only when a membrane potential - an intrinsic quality of the neuron related to its membrane electrical charge - reaches a specific value. When a neuron fires, it generates a signal which travels to other neurons which, in turn, increase or decrease their potentials in accordance with this signal.

In the context of spiking neural networks, the current activation level (modeled as some differential equation) is normally considered to be the neuron's state, with incoming spikes pushing this value higher, and then either firing or decaying over time. Various coding methods exist for interpreting the outgoing spike train as a real-value number, either relying on the frequency of spikes, or the timing between spikes, to encode information.

The first scientific model of a spiking neuron was proposed by Alan Lloyd Hodgkin and Andrew Huxley in 1952. This model describes how action potentials are initiated and propagated. Spikes, however are not generally transmitted directly between neurons, communication requires the exchange of chemical substances in the synaptic gap, called neurotransmitters. The complexity and variability of biological models have resulted in various neuron models, such as the integrate-and-fire (1907), FitzHugh-Nagumo (1961-1962) and Hindmarsh-Rose model (1984).

From the information theory point of view, the problem is to propose a model that explains how information is encoded and decoded by a series of trains of pulses, i.e., action potentials. Thus, one of the fundamental questions of neuroscience is to determine if neurons communicated by a rate code or by a pulse code.^[1]

Early results with spiking neural models suggested that by using temporal coding, networks of spiking neurons may gain more computational power than traditional neural networks. It was also suggested that, under certain conditions, any multilayered perceptron can be simulated closely by a network consisting of spiking neurons.

This kind of neural network can in principle be used for information processing applications the same way as traditional artificial neural networks. However due to their more realistic properties, they can also be used to study the operation of biological neural circuits. Starting with a hypothesis about the topology of a biological neuronal circuit and its function, the electrophysiological recordings of this circuit can be compared to the output of the corresponding spiking artificial neural network simulated on computer, determining the plausibility of the starting hypothesis.

In practice, there is a major difference between the theoretical power of spiking neural networks and what has been demonstrated. They have proved useful in neuroscience, but (not yet) in engineering. To date, there have been no large scale spiking neural networks that solve computational tasks of the order and complexity of rate coded (second generation) neural networks. It is relatively easy to construct a spiking neural network model and observe its dynamics. It is much harder to develop a model with stable behavior that computes a specific function.

There is diverse range of application software to simulate spiking neural networks. This software can be classified according to the use of the simulation:

• Software used primarily to simulate spiking neural networks which are present in the biology to study their operation and characteristics. In this group we can find simulators such as GENESIS (the GEneral NEural SImulation System) developed in James Bower's laboratory at Caltech; NEURON, mainly developed by Michael Hines, John W. Moore and Ted Carnevale in Yale University and Duke University; and Brian, developed by Romain Brette and Dan Goodman at the École Normale Supérieure. This type of application software usually supports the simulation of complex neural models with a high level of detail and accuracy. However large networks usually require very time-consuming simulations.
• Software which addresses information processing tasks to solve problems. Computer programs such as SpikeNET are in this group, which has been developed by Delorme and Thorpe in collaboration between Centre de Recherche Cerveau et Cognition and SpikeNet Technology. This type of application software usually runs very fast simulations but does not allow the simulation of very complex or biologically-realistic neural models.
• Software which provides capabilities to support the simulation of relatively complex neural models efficiently so that it can also be convenient for information processing tasks. This software can exploit biological neuron characteristics to perform computation functions and at the same time allows the study of the functionality of these neural characteristics. In this software group we can find EDLUT which has been developed in the University of Granada. This application software must be efficient enough to run fast simulations, sometimes even in real time, and at the same time it must support neural models which are detailed and biologically plausible.

Neurogrid, built at Stanford University, is a board that can simulate spiking neural networks directly in hardware.

1. ↑ Wulfram Gerstner: Pulsed Neural Networks. Hrsg.: Wolfgang Maass and Christopher M. Bishop. MIT Press, 2001, ISBN 0-262-63221-7, Spiking Neurons (google.com). 

• Full text of the book Spiking Neuron Models. Single Neurons, Populations, Plasticity by Wulfram Gerstner and Werner M. Kistler (ISBN 0521890799)

[[Category:Machine learning [[Category:Computational statistics [[Category:Neural networks