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?
The following puzzle is going around the Internet right now. It demonstrates—in a simple way—some of the complexity that context brings to the issue of human problem solving.
The solution is obvious, once you've found it. Enjoy solving it.
The McGurk effect is a perception illusion, which shows how our perception of reality can be affected by interactions between multiple senses. The presentation of the McGurk effect demonstrated in the following video also shows, convincingly, that our visual processes can completely override our auditory perceptions of speech — at least in certain circumstances.
In the above video, you will see the speaker's lips form an 'f'-sound. You will “hear” an 'f'-sound even though the actual sound being produced is a 'b'-sound (dubbed in over the video).
In this video, the 'f' perception reported by your eyes completely overrides the 'b' perception reported by your ears. Can we conclude, from this, that visual processing in the brain is given full priority over auditory processing?
The site was switched to a new hosting service at the end of February. The blog and glossary were the pieces I was most anxious about, but they seem to have handled the move just fine.
So far, this host seems to be providing much faster responses. It should also provide better up-time.
Responses have gone from often taking 40-70 seconds, down to less than ten seconds. In fact, I haven't counted a single response greater than 12 yet.
The previous provider would regularly (about once a month) make changes that completely hid most, or all, of the site's content from the search-engines and in-links. Those down-times would typically last from two to six days. Many down-times, including the last one, only ended when I wrote some defensive code to work around their new server-settings.
Hoping this provider will do better in that department as well.
So far, I'm happy with it.
P.S. — BTW, I'm also new to something called TraceWatch, which is a stats package. So far, I'm totally addicted to it. It's like FarmVille for webmasters.
Spent some time today doing minor edits to glossary entries. Of all the small edits, the most significant change made was to add the following section to the entry for weights.
“
. . . . . . .
Netlab's Compatibility Mode
ANN models that use floating point signed-value weights in the conventional fashion are math-centric. That is, they typically are concerned only with the signed numeric weight-value, rather than with the connection-strength represented by its absolute value. In this case, for example, increasing the weight value will make it more positive, regardless of whether it is representing an excitatory or inhibitory connection.
Netlab's default behavior is to operate directly on connection-strength representations, regardless of how they are implemented internally. Netlab neurons facilitate the conventional practice, however, by allowing it to be specified in the learning method for each weight-layer.
The table below shows how Netlab facilitates compatibility with existing practices. The table documents how the translation is carried out between the traditional math-centric convention, and Netlab's connection-strength-centric convention.
Connection-Type->
v--Operation
Excitatory
Inhibitory
Increase
Increase Connection Strength
Decrease Connection Strength
Decrease
Decrease Connection Strength
Increase Connection Strength
Translations performed when conventional adjustment practice is specified for a connection.
”
One possible analogy for the conventional, value-based, adjustment practice is that of adjusting for a specific water temperature from a faucet. If the water is too cold, for example, adjusting the weight value is comparable to simultaneously increasing the hot and reducing the cold (hot being the negative inhibitory weights, and cold being the positive excitatory weights in this analogy). Conversely, if the water is too hot, it is adjusted by simultaneously decreasing the hot, and increasing the cold.
In this way, Netlab is able to fully support the practice of working directly with the numeric value of a signed weight, but it also supports its own alternative strategy of adjusting connection strength representations. This strategy seems to be more representative of what has been learned about the cell, and molecular biology of neurons. The faucet analogy used above to describe the value-based adjustment is not sufficient to describe this strategy[1].
[1] - This is not to say the connection-strength adjustment strategy can't be related with an analogy, just that I have been too lazy, or too unfocused to come up with one that feels satisfyingly apt.
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 may become 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.
Merry Christmas, Happy Hanukkah, and happy new year. May your days be filled with happiness, love, and joy this Christmas season, and may your new year be a blessing to you and others.
“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.
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.
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