Spiking vs Linear Neuron Models - Pros and Cons
Neural models have trade offs with speed, efficiency of computation, and ease of use. There are two major domains for neural models: Pattern recognition and computation, and robot control. Spiking neurons have the following advantages: Spikes that only occur once over many time ticks do not require getting memory into cache for the target neurons, and thus are computationally efficient. Spiking neurons can do bursts. Spiking neurons make triggering behavior in a robot simple to implement. Spiking Neural models are inherently forward additive, imply Neural Hiding, and subsumptive architectures. Subtle timing of spikes can result in information encoding by spike arrival times. Drawbacks to Spiking Neurons: Integration over several time ticks are required to get intermediate levels of activation. The math is more complex; Charge, Activation, Burst and other variables must be tracked. Linear Neuron Advantages: (Neurons that transfer activation levels multiplied by weight...