On 16/03/2008, Ed Porter <[EMAIL PROTECTED]> wrote:
>
>
>
>
> I am not an expert on neural nets, but from my limited understanding it is far
> from clear exactly what the new insight into neural nets referred to in this 
> article
> is, other than that timing neuron firings is important in the brain, which is
> something multiple people have been saying for years.
>
>
>
> So I fail to understand what is new here, other than a reiteration of the 
> view that
> most of the traditional neural net models used in machine learning are gross
> simplifications of the neural nets in the brain, particularly with regard to 
> their
> simplification of the temporal complexity of brain networks.
>

Disclaimer: I am not a neuro scientist either.

The spiking neuron model, as I understand it, has the neuron when it
is activated give not one signal but a series of signals. This was
called a spike train. It was thought that the exact timing of this
series of signals was unimportant, just the number and so the neurons
just acted as integrators getting an average value.

So if a train was like this

A) _|_|_|

was thought to be the same as this

B) __||_|

Having the same number of spikes on average.

So people simulating real neural networks (not back prop etc) i.e.
computational neuroscientists the sorts of people working on blue
brain*, used this as the model for there systems.

Now it turns out the spike train is more like a message in a packet
rather than a number, so A and B are different signals and may be
treated differently.

It might also have implications for the computational capacity of the
brain. I think the max bandwidth calculations would have gone up.

* I don't know what model of neuron they are using, but it is that
sort of neural network researcher I am talking about.

-------------------------------------------
agi
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