(but sometimes the results look like one) [epistemic status: I work in a lab dedicated to biologically plausible neural circuits, so I’m informed on the problem, but probably still biased. There’s probably going be a follow-up post to this once I get a bunch of rebuttals from people smarter than me.] Update: As expected, I got a bunch of rebuttals and have adjusted my position accordingly I keep seeing and comparing Deep (DL) and the brain. This offends my sensibilities a bit, because although there are from DL that resembles certain areas of the brain, DL is not a good overall description of the brain. DL explicitly passes the buck on biological plausibility (like almost every other cognitive modelling approach) and implies that it’s “neurons” can be implemented biologically, it’s just that no one has bothered yet. I think the problem goes much deeper and that DL is missing a lot of the key features of the brain, which makes it a poor analogical target. academic non-academic articles Learning many results The brain is low power DL is power hungry. Alpha GO consumed the power of , not to train, but . The is an attempt to satiate this hunger, but it’s still not even close to the . IBM’s TrueNorth chip is another example of trying to bring low-power computation, but . Specifically, True North only implements feed-forward networks and has no on-chip learning. 1202 CPUs and 176 GPUs just to run TenserFlow Processing Unit brain’s power consumption of 20W it’s capabilities are quite limited when compared to other Neuromorphic hardware The brain can’t do back-prop Back-propagation is the foundation of all DL. Although there is evidence that errors being propagated through multiple layers is happening in the brain, no one has come up with a method for back-propagation (back-prop)that doesn’t rely on information propagating backwards through unidirectional synapses. I personally think it’s only a matter of time before a biologically plausible method is discovered, but until then it is unwise to ignore the implementation details and the restrictions it might place on what can be learned. The brain uses spikes to communicate Although it is possible to , these spikes are not leveraged for specific computational advantages. As far as I know (and I still have some reading to do), spiking computation has yet to be used anywhere for the learning in DL. convert DL networks into spiking neurons for use on neuromorphic hardware Neurotransmitters aren’t just spike transporters and neurons aren’t just spike-machines DL completely ignores the role of neurotransmitters. However, neurotransmitters have been shown to be computationally significant in adapting the receptive features on the fly, something which DL has really hard time doing. networks The brain is noisy Given the choice between neuron redundancy and neuron performance, evolution chose to make the brain redundant. , which isn’t surprising when you consider the warm, biologically variable environment they’re in. Although certain DL networks can cope with loss of their nodes, DL isn’t known for it’s robustness to noisy input or noisy training data. Neurons are noisy In conclusion, there are a lot of features of the brain that DL is omitting, thus using DL as an analogy for neural circuitry isn’t ideal. The alternative to comparing with DL is using a modelling paradigm that takes these challenges into account. At the time of writing, the only approach I know of is the Neural Engineering Framework (NEF) from the laboratory I belong to, but I’m sure as research marches forward other frameworks will emerge. “But Sean,” you cry with a gleam of mischief in your eye, “don’t most NEF models from the lab you belong to suffer from the same problems as DL? The models usually stop at LIF neurons, which aren’t realistic neurons at all! Why don’t you use more complex neuron models, i.e. things like , multi-compartmental neurons, and neurogenesis?” dendritic computation glial cells That’s a work in progress. There are 2 people ( and ) of the 16-strong (CNRG) that I belong to who are working on the problem of more complex neuron models. is working on biologically plausible back-prop and the moment he has a break-through you can be sure I’ll be shoving it in everyone’s face. Aaron Voelker Peter Duggins Computation Neuroscience Research Group Eric Hunsberger As for neurogenesis, There’s no good computational model of what neurogenesis does and the CNRG lacks the resources to do that sort of basic research. Dendritic and glial cell computation has no one working on it, because we’re only 16 people. If that bothers you, maybe you want to join us? is how hackers start their afternoons. We’re a part of the family. We are now and happy to opportunities. Hacker Noon @AMI accepting submissions discuss advertising &sponsorship To learn more, , , or simply, read our about page like/message us on Facebook tweet/DM @HackerNoon. If you enjoyed this story, we recommend reading our and . Until next time, don’t take the realities of the world for granted! latest tech stories trending tech stories

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