Spiking Neural Networks
Artificial Neural Networks have become a mainstream and efficient tool for many tasks we once thought would be hard or even imposible to achieve, things like image classification, general prediction and natural language processing, if you add big data and purposeful/ready available tools, techniques and frameworks we are truly living in an age of wonders, yet the current tools might not be able to achieve the greater goal of Artificial General Intelligence and recreate human like behavior and cognitive abilities like consciousness which is why we are here.
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This is a brief and casual overview and comparison of a slightly different approach or architecture that might help bridge and solve these problems or inspire new AIs: Spiking Neural Networks.
I’ll point you to more specifics in a second but in general a Spiking Neural Network (SNN for short) introduces time dynamics into the encoding, propagation and learning process (temporal coding) and might also provide new functionality via temporal coupling and cyclical activity, ( these last 2 are more of a system at large rather than intrinsic properties but go well hand in hand ).
To better appreciate the key differences or core concepts of SNNs we need to look at biology as SNNs are considered a more faithful analog to the processing done by neurons in you brain, consider these two examples:
Let's say you need to make a system to do some remote work like turning a water valve on/off, you could achieve this in many ways but the above two methods highlight the conceptual differences:1. You send an ON signal whenever you want to open the valve and an OFF signal when you want to close it.2. You send a continuos…