Loligo Blog

“Certainly, one of the most relevant and obvious characteristics of a present moment is that it goes away, and that characteristic must be represented internally.”

Stated plainly[1], the principle behind multitemporal synapses is that we maintain the blunt “residue” of past lessons in long-term connections, while everything else is learned in the instant. In other words, we re-learn the detailed parts of our responses as we are confronted with each new current situation.[2]






An earlier blog entry makes various attempts—using statically presented explanations—to have readers visualize the concept. For the most part, those attempts seem to miss the mark.

The following video, however, was produced by people who probably have never heard of multitemporal synapses. Their amazing experiment inadvertently does a much better job of relating the concept of multitemporal learning than I ever could with static presentations.

http://www.youtube.com/watch?v=wM6lGNhPujE Long face?: What you are viewing in this video may be your immediate responses—driven by long-term connections—before your short-term connection-components have had a chance to form/learn finer “present moment” responses.

The book on the Netlab project often returns to the notion that learning is merely a form of adaptation and that, conversely, adaptation is merely a form of long-term learning. This, in turn, all fits under the umbrella notion that memory is behavior.

The idea that learning is adaptation is learning is forwarded as a possibility, mainly as a better means of discussing the concepts. This (in my opinion) provides a clearer and more converged understanding of how memory works in biological organisms. This could be very wrong, of course, so it's important to describe it properly. That way it, and not a straw man, can be critiqued. This article represents one such attempt to properly describe it. . .





Batesian mimicry is when a non-noxious/poisonous plant or animal projects the appearance of a poisonous plant or animal, allowing it to avoid being eaten by predators.

Those predators, goes the logic, which have partaken of the poisonous organism and survived, would have become very sick, and would have learned to avoid ingesting anything that appears to be that organism in the future. This will include those organisms who are not poisonous, but merely look, or act, like the poisonous organism.

Scientists at UC Berkeley have taken brain scans of subjects in an fMRI machine while they watched a movie clip. They then reconstructed the movie the subjects were watching using only the brain scan data, and a database of 18 million seconds of random video gleaned from the web.

First, they used fMRI imaging to measure brain activity in visual cortex as a person looked at several hours of movies. They then used those data to develop computational models that could predict the pattern of brain activity that would be elicited by any arbitrary movies (i.e., movies that were not in the initial set). Next, they used fMRI to measure brain activity elicited by a second set of movies that were also distinct from the first set. Finally, they used the computational models to process the elicited brain activity, and reconstruct the movies in the second set.

The amount of new understanding this could allow us to gather about mind-brain correlates and first person knowledge should be considerable. If this lives up to the hype, a lot of new research ideas should come out of it. Keeping fingers crossed here.




In the above clip - the movie that each subject viewed while in the fMRI is shown in the upper left position. Reconstructions for three subjects are shown in the three rows at bottom. All these reconstructions were obtained using only each subject's brain activity and a library of 18 million seconds of random YouTube video that did not include the movies used as stimuli. The reconstruction at far left is the Average High Posterior (AHP). The reconstruction in the second column is the Maximum a Posteriori (MAP). The other columns represent less likely reconstructions. The AHP is obtained by simply averaging over the 100 most likely movies in the reconstruction library. These reconstructions show that the process is very consistent, though the quality of the reconstructions does depend somewhat on the quality of brain activity data recorded from each subject. [source: Gallant Lab (see resources below)]

The Netlab development effort has led to a new method and device that produces learning factors for pre-synaptic neurons. The need to provide learning factors for pre-synaptic neurons was first addressed by backpropagation (Werbos, 1974). The new method differs from backpropagation in that its use is not restricted to feed-forward only networks. This new learning method, called Influence Learning, is described here and in other entries in this blog (see Resources section below) .

Influence Learning is based on a simple conjecture. It assumes that those forward neurons that are exercising the most influence over responses to the immediate situation will be more attractive to pre-synaptic neurons. That is, for the purpose of forming or strengthening connections, active pre-synaptic neurons will be most attracted to forward neurons that are exercising the most influence.

Perhaps the most relevant thing to understand about this process is that these determinations are based entirely on activities taking place while signals (stimuli) are propagating through the network. Unlike backpropagation, there is no need for an externally generated error signal to be pushed through the network, in backwards order, and in ever diminishing magnitudes.





While influence learning in artificial neural network simulations is new, it is based on biological observations and underpinnings from discoveries made over twenty years ago. One of the biological observations that led to the above speculation about attraction to the exercise of influence was discussed briefly in the book The Neuron: Cell and Molecular Biology.

An experiment described in that book shows what happens when you cut (or pharmacologically block) the axon of a target neuron. In that experiment the pre-synaptic connections to the target neuron began to retract after its axon was cut. That is, the axons making presynaptic connections to the modified neuron went away when it no longer made synaptic connections to its own post-synaptic neurons.





The book also described how, when the target neuron’s axon was unblocked (or grew back), the axons from presynaptic neurons immediately began to reform and re-establish connections with the target. Based on these observations, the following possibility was asserted.

"...Maintenance of presynaptic inputs may depend on a post-synaptic factor that is transported from the terminal back toward the soma."

The following diagram depicts these observations schematically.

A set of constructs and methods introduced and described in the book: Netlab Loligo will improve the ability of systems constructed with them to adapt to current short-term situations, and learn from those short-term experiences over the long term.






How do we, as biological organisms, manage to keep so much finely detailed information in our brains about how to respond to any given situation? That is, how do we manage to keep countless tiny intricacies stored away in our “subconscious” ready to be called upon at just the right time, right when we need them in the present moment?

According to this theory of learning, the answer to that question is: We don't.

Instead, our long term connections—those that immediately drive our responses at all times—are only concerned with getting us started in any given “present.” Responses stored in long-term connections start us along a trajectory that makes it easier for us to learn whatever short-term, detailed responses are needed for any given detailed situation.

Connections that drive short-term responses, on the other hand, form spontaneously in-the-moment, and quickly adapt to whatever present situation we currently find ourselves in. Just as significantly, connections driving short-term responses tend to dissipate as quickly as they form. This theory essentially says that each connection in the brain that drives responses (physical or internal) includes multiple distinct connection strengths, which each increase and decrease at different rates of speed.






Multi-temporality is achieved in Netlab's simulation environment by providing multiple weights per a connection point (i.e., synapse), which are referred to as Multitemporal^{[Note 1]} synapses. Multitemporal synapses employ multiple weights. Each of the multiple weights associated with a given synapse represents a connection strength, and can be set to acquire and retain its strength at a different rate from the others. The methods also specify Weight-To-Weight Learning, which is a means of teaching a given weight in the set of multiple weights, using the value of other weights from the same connection. Together these constructs provide all the functionality required to model the theory of learning discussed above.

Following is a graphic excerpted from the book: Netlab Loligo, which shows a neuron containing three different weights for each connection point. Each weight is given its own learning algorithms, with its own learning-rate, and forget-rate.

There's an interesting article at the Talking Brains blog that gets our bearings and discusses our current understanding of the relationship between Broca's area and language. If you are still of the old notion, that it is the area singularly responsible for syntactical construction of sentence grammar (as I was), this will be a worthwhile read:

20100527: Syntax found in the brain -- not in Broca's area

BA15? No, not BA15. [note 1]

The above blog post gives a good overview with a nice sequence-line of how we got from the old understanding to the new (it has been a progression). It then puts a nice bow on the whole thing by describing a new study that provides strong observational evidence for the notion that the anterior temporal lobe has—at least—something to say about processing language and grammatical structure.





This is just my take (which could be very wrong), but I think what has been going on here is that understanding of the brain has been refining our understanding of linguistics, which has in turn, been refining our understanding of the brain. . . But that situation may be changing.

Grammatical rules, while being a nice way to talk about sentences, may not be something for which there is a directly correlated, and nameable brain structure. Again, it's just my hunch, but the connections between verbal communication and things like metaphor, and even dance (with the help of the brain constructs now believed to effect metaphor) seem to be part of what is becoming a much more complete understanding of the processes underlying speech. Our facilities for making metaphorical-conceptual connections between verbal communications, other forms (modes) of stimulus, and other forms of physical expression, seem to be emerging as the underlying causes of grammatical structure.

As our knowledge of the underlying brain mechanisms grows, sentence-structure is looking more like the old clockwork models of the solar system and universe. It is an abstract system of notation which was designed and developed to let us represent and model observable characteristics of the language itself. The language being modeled, however, was merely the end result of underlying processes that were (like the laws of gravity and motion in days past) totally hidden from us.

Understanding of language structure will be updated by our—now exploding—understanding of its causes. Of that I have no doubt. Right now, however, there seems to be a non-linear jump in what we're learning about the brain processes that lead to grammatical sentence structure.

-djr




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NOTE 1. - Perhaps BA15

And so too, it would seem, are our thoughts...

• Metaphoric intelligence and foreign language learning - Jeannette Littlemore "Metaphor is so pervasive in language that it would be impossible for a person to speak without using metaphor at some point, whether knowingly or not."



• The Contemporary Theory of Metaphor - Lakoff, G.
  "...a great many common concepts like causation and purpose are metaphorical..."



• Cognitive linguistics - Terri Eynon
  "Until relatively recently it was assumed that it must be possible to provide an accurate, objective (i.e. literal) description of reality for the purpose of scientific advancement. For the modernist, metaphors characterized rhetoric, not scientific discourse. "



• Cognitive Linguistics - George Lakoff
  "It was discovered in the late 1970's that the mind contains an enormous system of general conceptual metaphors -- ways of understanding relatively abstract concepts in terms of those that are more concrete."



• Metaphor - a Working Concept - Olle Torgny
  "On the other hand, abstract products and services, computer software, medication, electronics and similar phenomena has an ”inner structure” that is dependent on specific domain knowledge or that even is incomprehensible for experts. In this case the properties of the product (object, service, concept) has to be conveyed by something else. The more complicated and abstract this message is, the better suited is the use of metaphor."



• Oral Metaphor Construct (OMC) - Asa M. Stepak !
  This guy has a very interesting theory. It is also relatable at a lower abstract level than most. That is, it may provide implementable understanding about neural networks at the signaling level, where neural-network constructs live. For now this is just an interesting aside I found while researching. It (for me at least) merits a closer look.



• Standford U. Lecture: Analogy as the Core of Cognition - Douglas Hofstadter
  A fairly decent talk on a very important subject.




• Related Blog Entries:


• Synaesthesia: Not a mental anomaly, a mental characteristic
  A blog entry here about syneasthesis specifically and Dr. Vilayanur Ramachandran's theory that the underlying causes of syneasthesia are the same mechanisms responsible for our ability to think in metaphor and analogy.
  
  
• Simile, Metaphor, Analogy: Differences and Similarities
  A blog entry here that is mostly about unambiguously defining these terms for use in writing. It should help to in this area to clarify the definitions of these terms, which are often misunderstood, or used ambiguously.