A programmer who is obsessed with giving experimenters
a better environment for developing biologically-guided
neural network designs. Author of
an introductory book on the subject titled:
"Netlab Loligo: New Approaches to Neural Network
Simulation". BOOK REVIEWERS ARE NEEDED!
Can you help?
Linguists have recently discovered [1] that almost all words are metaphorical at their base, and some people (e.g., me) posit that they all are. Though speculative, it is at least conceivable that even the sub-language signaling in the brain, which eventually leads to language, is also metaphorical. Consider that the bell becomes a metaphor for food in the mind of Pavlov's dog.
Language is also able to relate ambiguity about the concepts it conveys. The word “life,” for example, can mean life-biology, or life-consciousness. Up until now, it has been perfectly acceptable to use these two meanings interchangeably. There simply has never been an instance of consciousness that existed outside of a biological body — at least none that we could directly experience with our physical senses.
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)]
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.
A New Learning Theory That Predicts A “Present Moment”
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.
How It's Done
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.
As a programmer I find it very satisfying when a phony false choice is taken down. Chris Chatham, who maintains Developing Intelligence blog looks like he's hot on the trail of one.
Here's a cool visualization from the article used to clarify the local-to-distributed data:**
He provides a very good explanation for the apparent disagreement in the experimental data. His conclusion? The two aren't mutually exclusive. (thank you Mr. Chatham)
So, how does this work? Is the brain just big enough to accommodate two different mechanisms? Possibly, but Chatham also explores a distinct possibility that the same underlying mechanisms may be responsible for both types of development. It turns out there is a bit of good reason to think it is the latter.
= = = = = = = = = = =
Notes:
** Okay, I don't know how much clarity it brings, but it is a slick visualization, therefor, it makes it into the post.
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:
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.
IMO
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.
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.
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.
We seem to be advancing rapidly on what can best be described (metaphorically), as the brain-function correlates of metaphor and analogy. Synaesthesia—mistaking sound for color, or perceiving numbers as having colors, etc.—may, in fact, be a characteristic of basic, inherent functions and structures, which are common in all brains. The determining factor may be only a question of degrees.
Take A One-Question Survey
One of the following alien-alphabet symbols is named “Woobul,” and the other one is named “kitkit.” Take your best guess to answer the question: which one is kitkit, and which one is woobul?
A
B
If you're like over 90% of respondents, you have guessed that 'A' is woobul and 'B' is KitKit. Why aren't the responses to this question around 50/50 for either symbol? According to Vilayanur Ramachandran, who used the above demonstration in his talk, what you are seeing in these responses is, in fact, the same mechanism that leads to synesthesia[note 1]. It is, obviously, a bit less pronounced in most people compared to those whom we would classify as having synesthesia.
Stand Out Publishing
