Chaos/reservoir computing and sequential cognitive models like HTM

[robf]

@complyue

@robf

For some things text just doesn’t suffice!

Maybe it is ironic (pity me at least), my verbal (both hearing and uttering) English is terribly poor , seems we’ll have to testify the limit of text based communication, anyway we are finding ways to tell a machine to do such things effectively, doing it ourselves (i.e. dogfooding ) may help somehow.

Well, we could try in my Chinese. Though I would be very surprised if that provided a clearer channel!

To attempt in text… Your first question:

@complyue

@robf

The “identity” of word is an element which has a minimal/maximal energy relationship with its surroundings.

From the computer’s (or programmer’s) perspective, all I get is neurons, synapses/connections, and spikes, I need an algorithm to identify such “elements” for “words”, out of simulated data and phenomena. That’s how the “naming” is done, quite seems.

I’m hypothesizing “words” will be collections of letter spikes which tend to synchronize in a raster plot. As sketched in this post:

@robf

If we imagine the letter raster plot in time, then we might imagine coding the input of a sentence (a prompt) like “Place wood blocks” with node spikes for the constituent letters as (letter vs. time of spike):

a       x
b                             x
c          x
d                          x
e             x
f
g
h
i
j
k                                      x
l    x                           x
m
n
o                   x   x           x
p x
q
r
s                                         x
t
u
v
w                x
x
y
z

Where I have token time gaps of three spaces between each letter as it occurs in the phrase in time.

Those are the input, or “prompt” spikes. The actual network would also be coded like this. So you can think of these spikes as having edges between them in sequence. And for the whole network, representing many phrases, each node would have many, many, such edges.

As input, the spikes for the letters making up the words are equally spaced. A bit like this:

P   l   a   c   e   w   o   o   d   b   l   o   c   k   s

The hypothesis is that sub-sequences of letters like “place”, “wood”, and “blocks”, would tend to have more edges at their beginning and end, and thus under oscillation would tend to synchronize their constituent letter spikes, so the spike times might be pushed further together, like this:

 P  l  a  c  e      w  o  o  d       b  l  o  c  k  s

You can imagine the "x"s in the sketched raster plot above being pushed together, synchronized, in the same way.

So, to answer your question, I’m saying that might be how we could ‘identify such “elements” for “words”, out of simulated data and phenomena.’

Why would they synchronize? You made the good point that with a single node representation the letter network would immediately become completely saturated with links. It’s brought me back to the idea that we will need a distributed representation, an SDR, even for letters. I think an SDR representation for letters can mean that an entire path leading to a letter can be coded as a subset of the whole letter SDR. The same path can even be encoded in multiple such subsets, effectively coding for repetition. And the same variety of subsets can differentiate letter connections generally, so our network is not immediately saturated, and we can differentially find resonances among more tightly connected clusters associated with words.

In this I’m reminded of earlier work coding longer sequences in an SDR explored together with @floybix back in 2014. I’ve posted that in a separate thread. Perhaps Felix’s code can be a basis for our letter SDR representation now:

The coding of longer sequences in HTM SDRs

The issue of coding longer sequences in an SDR has come up in my Chaos and sequential AI models thread with @complyue . That we need to represent letters with SDR, so we can capture code for paths back along a sequence: I recall some work done by Felix Andrews @floybix on this, and discussed on the predecessor to this forum, back, initially together with me, starting in 2014. As I recall, basically, making sequence predicting links from only a random selection of nodes in an SDR for a state, …

Your second question:

@complyue

@robf

In this case found by seeing which sub-sequences synchronize their oscillations in relative isolation (maximal relative self connection) to their surroundings.

I don’t think you mean enumerate all possible sub-sequences for it, but we should generate them, right? I need the generative algorithm then, and have no clue how such a generation process can obtain the “maximal” property.

To find which sub-sequences synchronize their oscillations we don’t need to enumerate them all. We just need to set the network oscillating, perhaps with a specific driving “prompt” activation, and see which elements synchronize. The network performs the search, in parallel, for us, as it is seeking its own minimum energy configuration.