The speed of thinking process gives us approximate range of time scale that we do real-time computation. And our very long-lasting memory tells us the time scale of the corresponding memory processes. The former computation is most likely performed by fast dynamical systems, and the latter is hypothesized to be based on long-term synaptic plasticity. The important question is "How are these time scales connected"?
Among the underlying biophysical systems, the spike-timing dependent plasticity (STDP) is one of the possible mechanisms. If learning and memory is trying to store and recall the fast dynamics that it is performing now, it is necessary to be able to do prediction of its own dynamics. Hence, mechanisms like STDP that enhances the causal connections would be a very good start.
Showing posts with label computation. Show all posts
Showing posts with label computation. Show all posts
Monday, September 01, 2008
Monday, August 04, 2008
Living neurons as liquid in LSM, why it makes sense
8am
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.
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.
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