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Does anybody know of an (open source) implementation of Liquid State Machines?
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The LSM is a three-layer neural network which consists of an input layer, a liquid layer, and a readout layer. The recurrent connections in the liquid layer allow it to capture dynamic information, where information fades out over time.
In other words, reservoir computing uses a recurrent neural network, and instead of updating all parameters of the network, it only updates some of the parameters and keeps the other parameters fixed after choosing them randomly. Reservoir computing (RC) is good at processing temporal or sequential kind of data.
When the membrane potential reaches the threshold, the neuron fires, and generates a signal that travels to other neurons which, in turn, increase or decrease their potentials in response to this signal. A neuron model that fires at the moment of threshold crossing is also called a spiking neuron model.
First, the network is treated as a state machine and the relationship between the RNN and ℳ. is established in the context of the algebraic theory of automata. In the second approach, the RNN is viewed as a set of discrete-time dynamical systems associated with input symbols of ℳ.
Here is something for the similar echo state networks: http://mloss.org/software/view/138/
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There is an attempt to code LSM in brian simulator (open source-python)
Maybe you will get some helpful hints or Code from here:
https://groups.google.com/forum/#!msg/briansupport/kh0QkcHhmNM/VTqaOhhGDBUJ
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