> definition of the word neuromorphic

The definition of the term is so vague as to be useless for scientific progress. What exactly is excluded by "brain inspired"? If I paint my computer grey because the brain is grey, it's brain inspired? You know that regular expressions were invented to model neurons [1]? If nothing is excluded the term is useless. But in reality, the term is used explicitly to deceive as already done in the 19th century see [2].

Heraclitus says somewhere something to the effect that "good thinking is dry". Established terminology like

• Asynchronous CPU

• Delay Insensitive Circuit

• Quasi-Delay-Insensitive Circuit

• Clockless CPU

• Asynchronous circuit

• Self-timed circuit

is dry and useful. 'Neuromorphic' suggests an understanding of how the brain works that is just not there in 2025.

> Do we know that neurons operate asynchronously to each other, only activating when their inputs change? Yes.

I strongly disagree.

We do not know how exactly the brain codes information. If you have timed coding, it's not asynchronous. And that doesn't even address the chemical signals that abound in the brain. The Hodgkin-Huxley equations give a good approximation to how the neuron produces a "spike" - the signal that travels down the axon. But it's only an approximation. Every type of ion channel in the cell adds another 10 or so unkown parameters to the equations, which can only be approximately found experimentally. So we have a fairly good picture of how a single neuron behaves in most circumstances, but not complete. Ion channels are are crucial to how the brain works. They are the nonlinearity that makes computation possible, like the transistor is to the computer. There are several hundred types of ion channel that are known about so far and probably thousands more that are not yet discovered.

> That's a silly question

Not in the context I asked: I asked him after he had suggested that 'neuromorphic' would be great to reduce the energy consumption of LLMs. I wanted to know why/how, given that LLMs are trained today with BP. I give him the benefit of the doubt here, since we were in a rush and could not go deeper into the subject.

> mix spiking neural nets and backprop ?!

It's an active area of research to do just this. Google SpikeProp, e.g. [3]. Lot's of papers in this space at the moment. Why? I don't know as I don't follow this space. Potential reasons: (i) BP is natural, (ii) BP works super well for DeepLearning, (iii) the 'neuromorphic' community has failed so spectacular and want to try something that works, (iv) to get funding, (v) we really do not know how the brain works, and in that case, why not try out crazy things?

[1] S. Kleene, Representation of events in nerve nets and finite automata. https://www.dlsi.ua.es/~mlf/nnafmc/papers/kleene56representa....

[2] K. S. Kendler, A history of metaphorical brain talk in psychiatry. https://www.nature.com/articles/s41380-025-03053-6

[3] https://homepages.cwi.nl/~sbohte/publication/esann.pdf

> Not in the context I asked: I asked him after he had suggested that 'neuromorphic' would be great to reduce the energy consumption of LLMs. I wanted to know why/how, given that LLMs are trained today with BP.

I assume this whole Q&A with Furber is a fantasy of yours that never happened, or alternatively you had done zero homework about SpiNNaker and spiking neural networks beforehand, and understood nothing about dataflow as a way to reduce energy consumption (as your last post proved).

You may think that leading off your responses with a bunch of bullet points you just got from Google makes you sound well-informed, but it doesn't - it comes across like a high schooler with nothing to say trying to pad their essay.