Living neurons as liquid in LSM, why it makes sense

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LCN: living cortical network
LSM: liquid state machine

Advantage of using LCN for LSM

In the original LSM framework, any dynamical system that satisfies the separation property can be used as the liquid. However, how to choose a proper liquid for a specific problem is not yet well established. Although the details are unknown, the LCN has the capability to adapt to the signals that it is exposed, and self-organize itself. Therefore, the information processing through LCN could be interesting. In fact, well known phenomenological synaptic plasticity rules including spike-timing dependent plasticity turned out to have the power of self-organization and mutual information maximization. Being a biological system that is far from being fully understood, the LCN system has the power equivalent to the brain—neurons grow axon and dendrites, self-regulate ion channels, synapses grow, split and disappear, and more. The totality of LCN cannot be simulated in a computer as a traditional LSM would work with. Even if it is possible to simulate the system, it is always computationally cheaper to use the actual physical system rather than the complicated simulation.