[HN Gopher] Neuromorphic learning, working memory, and metaplast... ___________________________________________________________________ Neuromorphic learning, working memory, and metaplasticity in nanowire networks Author : taubek Score : 62 points Date : 2023-04-24 17:51 UTC (5 hours ago) (HTM) web link (www.science.org) (TXT) w3m dump (www.science.org) | SeriousGamesKit wrote: | Really excited to see this after first learning about NWNs two | years back. Great to see progress on these new 'hardware' | techniques for AI. Well done to Alon & the team! | dpflan wrote: | FTA: "A quintessential cognitive task used to measure human | working memory is the n-back task. In this study, task variations | inspired by the n-back task are implemented in a NWN device, and | external feedback is applied to emulate brain-like supervised and | reinforcement learning. NWNs are found to retain information in | working memory to at least n = 7 steps back, remarkably similar | to the originally proposed "seven plus or minus two" rule for | human subjects" | | Hm, so is the physical design of the device, having been modeled | after human, imply the design of synapse networks is going to be | limited as much as the human "device"? Are there other species | with better n-back performance? | NeuroCoder wrote: | We use this in my lab and I think you this is a lot more | complex than better or worse on the task as a whole. Certain | kind sof stimuli will interact with subject memory in different | ways. So even if there's research saying another species is | better or worse it probably depends on what is being recalled. | dpflan wrote: | I think I was more thinking about the possible direct mapping | of the physical device to the computational device implying | that it may not be possible to make make a more intelligent | device from a device base. | | What is your lab doing? Are you mapping physical brains? | NeuroCoder wrote: | We don't really do brain mapping in the sense that would | apply to nanotechnology. The actual mechanism of working | memory is pretty hard to establish in humans at this level. | sva_ wrote: | Pretty sure I read before that chimpanzee have higher 'n-back' | capacity. | dr_kiszonka wrote: | There are neuromorphic deep learning algorithms. From what I | read, one promise of these spiking neural networks is higher | efficiency than that of typical neural nets, which would enable | learning from much fewer data samples. | | If anybody here works with SNNs, can you share if you think this | claim is true? Also, are there any good entry points for people | interested in learning more about SNNs? | jegp wrote: | I'm a PhD student working with neuromorphic computing. I like | to think about SNNs as RNNs with discretized outputs. The | neurons themselves may have some complicated nonlinear dynamic | (currents integrating into the membrane voltage somehow etc.) | but they are essentially just stateful transfer functions. The | notion of spikes is a crippling simplification, but it's power | efficient and you can argue for numerical stability in the | limit. So I tend to consider spikes as an annoying engineering | constraint in some neuromorphic systems. Brains function | perfectly well without them, although in smaller scales (C. | elegans). | | The true genius of neuromorphics in my view, is that you can | build analog components that performs neutron integration for | free. Imagine a small circuit that "acts" like the stateful | transfer function, with physical counterparts to the state | variables (membrane voltage, synaptic current, etc.). In such a | circuit you don't need transistors to inefficiently approximate | your function. Physics is doing the computation for you! This | gives you a ludicrous advantage over current neural net | accelerators. Specifically 3-5 _orders of magnitude_ in energy | _and_ time, as demonstrated in the BranScaleS system | https://www.humanbrainproject.eu/en/science-development/focu... | | Unfortunately, that doesn't solve the problem of learning. Just | because you can build efficient neuromorphic systems doesn't | mean that we know how to train them. Briefly put, the problem | is that a physical system has physical constraints. You can't | just read the global state in NWN and use gradient descent as | we would in deep learning. Rather, we have to somehow use local | signals to approximate local behaviour that's helpful on a | global scale. That's why they use Hebbian learning in the paper | (what fires together, wires together), but it's tricky to get | right and I haven't personally seen examples that scale to | systems/problems of "interesting" sizes. This is basically the | frontier of the field: we need local, but generalizable, | learning rules that are stable across time and compose freely | into higher-order systems. | | Regarding educational material, I'm afraid I haven't seen great | entries for learning about SNNs in full generality. I co-author | a simulator (https://github.com/norse/norse/) based on PyTorch | with a few notebook tutorials | (https://github.com/norse/notebooks) that may be helpful. | | I'm actually working on some open resources/course material for | neuromorphic computing. So if you have any wishes/ideas, please | do reach out. Like, what would a newcomer be looking for | specifically? ___________________________________________________________________ (page generated 2023-04-24 23:00 UTC)