4 ms·

Neuromorphic learning, working memory, and metaplasticity in nanowire networks

83 points by taubek 4y ago

dpflan 4y ago
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?
- sva_ 4y ago
  Pretty sure I read before that chimpanzee have higher 'n-back' capacity.
- NeuroCoder 4y ago
  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 4y ago
    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 4y ago
      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.
SeriousGamesKit 4y ago
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!
dr_kiszonka 4y ago
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 4y ago
  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/focus-areas/neuromorphic-computing/ 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/ https://github.com/norse/norse/) based on PyTorch with a few notebook tutorials (https://github.com/norse/notebooks 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?
ffss99 4y ago
One of the authors (Kuncic) has another recent paper where the potentiation and depression in conductance has an analytical form. https://arxiv.org/abs/2301.10145 https://arxiv.org/abs/2301.10145 I think that what they are doing is a generalized version of that, with multi-probes.

83 points by taubek 4y ago

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?

sva_ 4y ago
Pretty sure I read before that chimpanzee have higher 'n-back' capacity.
NeuroCoder 4y ago
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 4y ago
  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 4y ago
    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.

SeriousGamesKit 4y ago

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!

dr_kiszonka 4y ago

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 4y ago
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/focus-areas/neuromorphic-computing/ 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/ https://github.com/norse/norse/) based on PyTorch with a few notebook tutorials (https://github.com/norse/notebooks 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?

ffss99 4y ago

One of the authors (Kuncic) has another recent paper where the potentiation and depression in conductance has an analytical form. https://arxiv.org/abs/2301.10145 https://arxiv.org/abs/2301.10145 I think that what they are doing is a generalized version of that, with multi-probes.