SpikeNET is a program designed for simulating very large networks of asynchronous spiking neurons. Neurons are simulated with a limited number of parameters that includes classic properties like the post-synaptic potential and threshold, but also more novel features like dendritic sensitivity. SpikeNET can be used to simulate networks with millions of neurons and hundreds of millions of synaptic weights. Optimization of computation time and the aim of real time computation has been one of the driving forces behind the development of SpikeNET.

• Perform image processing using biologically plausible network of neurons.
• Simulate millions of integrate-and-fire neurons organized in retinotopical maps.
• Connect these neuronal maps using projection files, and regroup common synaptic weights to save memory in order to be able to declare several hundreds of billions of synaptic connections.
• Convert gray level images into lists of spikes (also SpikeNET can perform a variety of preprocessing on the input images).
• Implement a complex mechanism for projection between neuronal maps of different sizes.
• Implement supervised learning.
• Implement the efficient neuronal Rank-Order-Coding scheme (optional)
  

• SpikeNET does not have yet a comprehensive graphic interface. The neural network topology must be described using several configuration files (documented below).
• SpikeNET has never been used to process more than one spike per neuron (SpikeNET was initially designed to test the biological plausibility of feed-forward processing using at most one spike per neuron). Note that it is still possible to implement lateral inhibition or excitation as well as feedback as long as neurons discharge only once.
• SpikeNET can not implement synaptic connection with various delays. All synaptic connections are instantaneous

When we tested rigorously the performaces of SpikeNET in 1999, SpikeNET could compute roughly 20 million connections per second on a standard deckstop computer (Macintosh Power PC 233 Mhz). As shown in the next figure, this is sufficient to model a network of 400 000 neurons in real time, using a time step of 1 ms (assuming 49 connections per neuron, and an average firing rate of 1 spike per second, a value which is a reasonable estimate for the average firing rate of cortical neurons). Note that time resolution increses has virtually no effect on the computation time, and that adding a decay parameter to neurons adds roughly 30-40% to the computation time. Because of the type of processing performed by SpikeNET, we would expect this figures to scale with the processing power of new deckstop computers: for a Pentium 4 at 2 GHz for instance, the Software may be able to update about 100-200 million connections per second. You may find more detailed specification of this model in the paper section .