Simulation of networks of spiking neurons: A review of tools and strategies (2024)

Simulation of networks of spiking neurons: A review of tools and strategies (1) https://doi.org/10.1007/s10827-007-0038-6 · Simulation of networks of spiking neurons: A review of tools and strategies (2) Full text

Journal: Journal of Computational Neuroscience, 2007, №3, p.349-398

Publisher: Springer Science and Business Media LLC

Authors:

  1. Romain Brette
  2. Michelle Rudolph
  3. Ted Carnevale
  4. Michael Hines
  5. David Beeman
  6. James M. Bower
  7. Markus Diesmann
  8. Abigail Morrison
  9. Philip H. Goodman
  10. Frederick C. Harris
  11. Milind Zirpe
  12. Thomas Natschläger
  13. Dejan Pecevski
  14. Bard Ermentrout
  15. Mikael Djurfeldt
  16. Anders Lansner
  17. Olivier Rochel
  18. Thierry Vieville
  19. Eilif Muller
  20. Andrew P. Davison
  21. Sami El Boustani
  22. Alain Destexhe

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Simulation of networks of spiking neurons: A review of tools and strategies (2024)

FAQs

What is spiking neural networks theory? ›

The idea is that neurons in the SNN do not transmit information at each propagation cycle (as it happens with typical multi-layer perceptron networks), but rather transmit information only when a membrane potential—an intrinsic quality of the neuron related to its membrane electrical charge—reaches a specific value, ...

What is neuron simulation? ›

The NEURON Simulation Environment (aka NEURON --see http://www.neuron.yale.edu/) is designed for modeling individual neurons and networks of neurons, and is widely used by experimental and theoretical neuroscientists.

What are the applications of SNNs? ›

Key Applications

Time-Series Prediction: SNNs are highly effective in predicting future events by recognizing temporal patterns in data. Industries like finance, meteorology, and healthcare are utilizing this capability. Robotics: SNNs can imitate the human brain's function of processing information over time.

Are spiking neural networks the future? ›

Robotics: Spiking Neural Networks (SNNs) offer exciting possibilities for the future of robotics due to their unique characteristics like low power consumption, real-time processing, and bio-inspired learning. Here are some specific use cases where SNNs are making significant progress: 1.

How to train a SNN? ›

Training the SNN

Below is a function that takes a batch of data, counts up all the spikes from each neuron (i.e., a rate code over the simulation time), and compares the index of the highest count with the actual target. If they match, then the network correctly predicted the target.

How to simulate neurons? ›

Simulations utilize mathematical models of biological neurons, such as the hodgkin-huxley model, to simulate the behavior of neurons, or other cells within the brain. Various simulations from around the world have been fully or partially released as open source software, such as C.

What does stimulating neurons do? ›

Neural stimulation modulates the depolarization of neurons, thereby triggering activity-associated mechanisms of neuronal plasticity. Activity-associated mechanisms in turn play a major role in post-mitotic structure and function of adult neurons.

What are the applications of caprolactone? ›

Among these materials, poly(ε-caprolactone) (PCL) stands out as a prominent biocompatible and biodegradable polyester. This polymer has become extremely attractive in various fields due to its versatile properties and potential application in regenerative medicine, tissue engineering, and drug delivery.

What are the applications of YSZ? ›

Applications of Yttria Stabilized Zirconia

YSZ is resistant to heat decomposition. Because it is resistant to high temperatures, yttria-stabilized zirconia is often used as a refractory material. Refractory materials can be used to line furnaces, hold hot objects, and provide thermal insulation.

What are the applications of Chromenes? ›

Chromene derivatives are an important class of heterocycles being the chief components of many naturally occurring products. Generally, chromenes are used as cosmetic agents, food additives, and potential biodegradable agrochemicals. Coumarin is a chemical compound found in many plants.

What is the point of spiking neural networks? ›

Spiking Neural Networks (SNNs) were developed in computational neuroscience to replicate the behaviour of organic neurons. As a result, the Leaky-Integrate-and-Fire (LIF) model was developed, which characterizes neuronal activity as integrating incoming spikes and poor dispersion (leakage) to the environment.

What is the largest neural network today? ›

To the best of my knowledge, the largest public (claimed) neural network so far is 160B parameters, where a parameter roughly corresponds to a synapse in the human brain. Given the human brain is estimated to have about 100T synapses, that neural network could be said to be about 0.16% of the human brain.

Can the universe be a neural network? ›

In the 19th century, when thermodynamics was emerging, the Universe was compared to an engine. When the computer became popular, scientists started comparing the Universe to a computer or a simulation. Now, in the age of artificial intelligence and machine learning, we are saying it is a giant neural network.

What is the difference between spiking and non spiking neural networks? ›

Non-spiking neurons have graded potentials, making them more susceptible to disruptions. In other words, non-spiking neurons can react to anything. Spiking neurons, in contrast, function according to the 'all or none' principle; they show action potentials.

What is a boosted neural network? ›

Boosting is the process of building a large additive neural network model by fitting a sequence of smaller models. Each of the smaller models is fit on the scaled residuals of the previous model. The models are combined to form the larger final model.

Why are spiking neural networks more efficient? ›

Technical studies estimate that SNNs can achieve up to two orders of magnitude in energy savings compared to traditional neural networks. This efficiency stems from their ability to exploit temporal sparsity — only computing or updating states when necessary, based on the incoming spike patterns.

What is a network spike? ›

Spike in Network Trafficedit

Such a burst of traffic, if not caused by a surge in business activity, can be due to suspicious or malicious activity. Large-scale data exfiltration may produce a burst of network traffic; this could also be due to unusually large amounts of reconnaissance or enumeration traffic.

References

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