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I don't get the appeal of spiking networks. They struggle to solve problems that have already readily been solved by ANNs and they don't offer much in terms of
by cblconfederate 5y ago
I don't get the appeal of spiking networks. They struggle to solve problems that have already readily been solved by ANNs and they don't offer much in terms of biological realism - they don't account for neuronal geometry and dendritic nonlinear phenomena, nor do they explain the protein dependence of LTP.
- periheli0n 5y agoThese are two disparate applications of spiking networks. 1. Machine learning—yes, many in the field seem to be trying to reinvent ANNs with spikes. Not very useful in my opinion. 2. Modeling biological processes. A lot of progress has been made in neuroscience research thanks to spiking network models, coupled with dendritic computation and all other sorts of biological detail. But one would not normally use neuromorphic hardware if the end goal is biological realism. Only if biological realism is required in real time and on a constrained power budget, such as on a robot, is Neuromorphic hardware the weapon of choice.
- glial 5y agoI can't speak for the ML applications. But to neuroscientists, I imagine the appeal is: 1. the brain uses spiking neurons 2. the brain does sophisticated computations, predictive coding, etc ...how? Like with other basic research, I think it's a safe to bet that there will be applications for working SNNs that you or I have not imagined yet.