Loligo Blog - Neural Networks

AI - Time for a New Name?

nospam@example.com (John R) — Thu, 05 Jan 2012 08:33:00 -0800

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.

Things may be changing now. . .

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Multitemporal Synapses and Our Perception of a Present Moment

nospam@example.com (John R) — Sat, 19 Nov 2011 19:44:00 -0800

Overview

“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]

. . . . . . .
Contents

^[top]

An Interesting Visualization

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.

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Learning is Adaptation is Learning - Using Batesian Mimicry As an Explanatory Device

nospam@example.com (John R) — Thu, 03 Nov 2011 13:11:00 -0700

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

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.

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Neural Networks Backgrounder: Ce n'est pas une l'histoire

nospam@example.com (John R) — Mon, 03 Oct 2011 08:24:00 -0700

Overview

This article provides a layman's-level discussion of neural network technology within the framework of a sketchy historical sequence. Neural networks are described while presenting an overview of just one of the many routs taken by the field in the last half-century or so.

It is not for those interested in a full history of neural networks (i.e., connectionism). It is just a quick backgrounder, which should suffice to give readers a little bit of perspective into how we got from "there" to "here." The actual history of this field is storied, and sometimes even checkered and controversial. I highly recommend to anybody who is interested, that you get a good book or two on the subject.

This entry will also serve as a place to accumulate links to resources and information on the subject of neural networks and their history at this layman's level.

. . . . . . .
Contents

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UC Berkeley - Scientists use brain imaging to reveal the movies in our mind

nospam@example.com (John R) — Sat, 24 Sep 2011 12:41:21 -0700

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)]

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Multitemporal Synapses Awarded a Patent

nospam@example.com (John R) — Thu, 10 Mar 2011 10:15:28 -0800

YEAH baby!

A neural network innovation described in the book: Netlab Loligo has been awarded a patent (#7,904,398). — Of the innovations described in the book, it is the second to receive letters patent (so far ). The patent is titled:

“Artificial Synapse Component Using Multiple Distinct Learning Means With Distinct Predetermined Learning Acquisition Times”
Patent titles serve mainly as an aid for future patent searchers. The patented innovation, along with the underlying concepts and principles that led to it are described and discussed in the book, where they are simply referred to as “Multitemporal Synapses.”

The primary advantage imparted by the innovation is that it gives adaptive systems a present moment in time. This allows them to quickly and intricately adapt to the detailed response needs of their present situation, without cluttering up long term memories with the minute details of those responses.

Sources & Resources

Multitemporal Synapses and Our Perception of a Present Moment
Stated simply, the theory behind multitemporal synapses is that we maintain the blunt essence of past lessons in long-term connections. Everything else is RE-learned in the moment.

Multitemporal Synapses
This is a blog entry here that tries to describe Multitemporal Synapses. When time permits, I will try to provide a new blog entry with a clearer explanation using book excerpts (P.S. see above entry). It will be specifically geared to laymen. If you are interested, please subscribe to the feed.

Influence Learning Gets A Patent
Influence Based Learning was the first of Netlab's innovations to be granted a patent. This latest patent makes two (and counting, stay tuned).

[pdf] Patent Title Page

Official Gazette Entry at the Patent Office [TEMPORARY LINK]

Biological Underpinnings of Influence Learning

nospam@example.com (John R) — Thu, 03 Mar 2011 16:28:00 -0800

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.

Support In Biological Observations

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.

Continue reading "Biological Underpinnings of Influence Learning"

Introducing: Multitemporal Synapses

nospam@example.com (John R) — Tue, 25 Jan 2011 19:21:00 -0800

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.

Continue reading "Introducing: Multitemporal Synapses"

See Every Synapse and Its Type - Stanford's New Imaging Technique

nospam@example.com (John R) — Wed, 17 Nov 2010 15:10:00 -0800

3D + Context

Stanford University School of Medicine has developed a relatively simple new imaging technique that provides a very exact way to capture the synapses of a connectome with pinpoint 3D positional accuracy, and considerable contextual resolution.

Stanford has performed a study (see below), which was admittedly done primarily just to showcase the new technique. That said, the study managed to produce a very impressive new find.

“ In the course of the study, whose primary purpose was to showcase the new technique’s application to neuroscience, Smith and his colleagues discovered some novel, fine distinctions within a class of synapses previously assumed to be identical. ”

Continue reading "See Every Synapse and Its Type - Stanford's New Imaging Technique"

Influence Learning Gets A Patent

nospam@example.com (John R) — Sun, 17 Oct 2010 09:31:00 -0700

Woo HOOO!

Influence Based Learning, one of two new learning methods described in the book Netlab Loligo, has just been awarded a United States Patent. The official title of the patent is:

“Feedback-Tolerant Method And Device Producing Weight-Adjustment Factors For Pre-Synaptic Neurons In Artificial Neural Networks”
The title is a mouthful, primarily designed to help future patent searchers determine if their great idea has already been discovered and patented. It is fully described and discussed in the book, where it is simply referred to as Influence Learning.

As the patent-title expresses, one of the benefits it imparts over existing learning algorithms, is that it is feedback-tolerant. It will work fine with the current-day feed-forward networks configured as "slabs", but it also allows connecting neurons to pre-synaptic neurons as well. That is, it allows feedback, which means you don't have to configure your network with "hidden layers" anymore if you don't want to. You are free to use any connectome you'd like.

[pdf] Patent Title Page
Neural Networks Backgrounder: Ce n'est pas une l'histoire
A quick backgrounder on neural networks presented in a sketchy semi-historical format.

Introducing: Influence Learning

nospam@example.com (John R) — Tue, 07 Sep 2010 12:14:00 -0700

Influence learning is one of two new learning algorithms that have emerged (so far) from the Netlab development effort. This blog entry contains a brief overview describing how it works, and some of the advantages it brings to the task of neural network weight-adjustment.

How It Works

This learning method is based on the notion that—like their collective counterparts—neurons may be attracted to, and occasionally repulsed by, the exercise of influence by others. In the case of neurons, the "others" would be other neurons. As simple as that notion sounds, it produces a learning method with a number of interesting benefits and advantages over the current crop of learning algorithms.

A neuron using influence learning is not nosy, and does not concern itself with how its post-synaptic (forward) neurons are learning. It simply trusts that their job is to learn, and that they are doing their job. In other words, a given neuron fully expects, and assumes that other neurons within the system are learning. Each one treats post-synaptic neurons that are exercising the most influence as role models for adjusting connection-strengths. The norm is for neurons to see influential forward neurons as positive role models, but neurons may also see influential forward neurons as negative role models.

It Is Simple

As you might guess, the first benefit is simplicity. The method does not try to hide a lack of new ideas behind a wall of new computational complexity. It is a simple, new, method based on a simple, almost axiomatic, observation, and it can be implemented with relatively little computational power.

It Imposes No Restrictions On Feedback

Influence Learning is completely free of feedback restrictions. That is, network connection-structures may be designed with any type, or amount of feedback looping. The learning mechanism will continue to be able to properly adapt connection-strengths regardless of how complex the feedback scheme is. The types of feedback designers are free to employ include servo feedback, which places the outside world (or some network structure that is closer to the outside world) directly in the signaling feedback path.

This type of "servo-feedback" is shown graphically in figure 6-5 of the book, which has been re-produced here.

Continue reading "Introducing: Influence Learning"

Evidence for Myosin II Mediation of Short- to Long-Term Memory Formation

nospam@example.com (John R) — Tue, 31 Aug 2010 12:28:00 -0700

One of Netlab's synapse mechanisms and structures is based loosely on a silent-synapse hypothesis of long- vs short-term memory, in which short and long both occur at the same connection-point (synapse). Netlab includes a learning method based on this as well, called weight-to-weight learning. The silent synapse phenomenon has been observed for quite some time in biological studies, and there has been very good evidence to explain some of the underlying mechanisms responsible for the observation. Still there have been many missing pieces to the puzzle.

An Interesting Study

Recently there has been a development that seems to give evidence and details to a related theory/hypothesis of how synapse strength may be mediated through a molecular motor called Myosin II on the post-synaptic side. So suggests one study out of The Scripps Research Institute.

It has been thought for some time now (see background information below) that molecular motors resembling those used to produce movement in muscle tissue, may be a major player in the processes mediating the transfer of memory-connections from short-, to long-term on the post-synaptic side. We now seem to be getting to more detailed understanding of the mechanisms underlying these phenomena. Like so many brain constructs, there does seem be a great deal of variety.

The vernacular that seems to be emerging is that these mechanisms "stabilize" the connection strengths. This might still be jumping the gun on the conclusions, but it is not a bad way to think about it for now.

Sources & Resources

Scripps Research Scientists Uncover New Mechanism of Memory Formation.
This is a news story about the study.

Abstract and link to article (full=$)
This gets you to the article from the journal Neuron (full text is behind a pay wall)
Gavin Rumbaugh
Are you ready to Rumbaugh? i'm sure he's never heard that joke before

Related/Background:

Remodeling the Plasticity Debate: The Presynaptic Locus Revisited
A really interesting paper from 2006 published at the journal Physiology. From its description: "The cellular mechanisms contributing to long-term potentiation and activity-induced formation of glutamatergic synapses have been intensely debated. Recent studies have sparked renewed interest in the role of presynaptic components in these processes. Based on the present evidence, it appears likely that long-term plasticity utilizes both pre- and postsynaptic expression mechanisms."

Silent Synapses in Neural Plasticity: Current Evidence
Great article from 1999 gets you a nice sense of the time-line to that point.

CSHL show in unprecedented detail how cortical nerve cells form synapses with neighbors
Related.

Actuators (scroll to bottom)
A blog-post here about actuators. Mostly robotics, but a section at the bottom has a couple of nice videos describing the function and structure of animal muscles.

The Neuron: Cell and Molecular Biology (Amazon.com)
A primary reference work, which is listed as the primary reference in Netlab. You will find information on the silent synapse hypothesis here.

Memory's Master Switch: Molecular Power Behind Memory Discovered
Related/Background

Scientists discover major clue in long-term memory making
Related/Background

Diffuse-to-Focal vs Local-to-Distributed: Not an Either-Or Choice

nospam@example.com (John R) — Mon, 23 Aug 2010 11:51:00 -0700

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.

In his post titled: Neural Mechanisms Giving Rise to Diffuse-to-Focal and Local-to-Distributed Developmental Shifts he has provided a run-down of some of the best observational support showing evidence for the diffuse to focal shift. He then explores and teaches some evidence for the local-to-distributed shift, which, it turns out, is just as convincing.

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.

Experimenter: Bring Your Own Razor - (book excerpt)

nospam@example.com (John R) — Tue, 17 Aug 2010 14:58:00 -0700

The following excerpts from Chapter 4 of the book ("Our Metaphysical Tool Shed") may help to clarify the point of this post.

Anti-Razor¹

It would probably be sufficient to simply express the weighted result as a ratio, but for now that's just one option. As our understanding grows, we may find that our maintaining two separate sums and many of the other values, is the metaphorical equivalent of gluing feathers to the wings of a flying machine. . .

. . .

1. This sub-heading is a reference to Ockham's razor, which is almost always mis-characterized in common usage. William of Ockham's original advice is based on sound, logical, reasoning, while the common mis-characterization is essentially a fashion statement. Here, however, I argue that programmers must give experimenters the ability to define their own (real) razors, and so should not mandate them in the modeling tools we provide. That is, we should give experimenters more, and let them decide for themselves how to divide and conquer those “more” into “fewer”.

Many of the ANN modeling tools available today are merely paint-by-number kits, which allow experimenters to try out solutions that other people have worked out and documented in formulaic recipes. Netlab is decidedly NOT among these offerings.

The known neural network formulas, for example, each represent somebody else's abstraction, and reduction of the observation data. The function produced is essentially a workable, defined, recipe for creating an effect that is similar to observed behaviors. In short, the person who came up with the formula in the first place was the one who did all the heavy lifting.

Experimenters should be given tools that let them find and test their own theories, and their own ways to pair down and represent what they think is most essential about what's going on in the problem space.

Netlab

To state it simply, Netlab is built on the proposition that experimenters should be able to try out their own ideas, and not merely find new ways to use other people's ideas.

One very good way for a programmer to achieve such a goal in his design, is to reverse engineer the existing formulas. That is, for each existing formula, produce a simulation environment that would allow somebody to create the formula for the first time, were it not already known. This is one of the design philosophies underlying Netlab's software specification.

Some Practical Examples

To this point it has been a rather abstract post. What follows is mostly a list of links into the glossary (I think). Now that the I.P. protection is starting to come through, I'll be able to describe this stuff more openly, and more deeply. As new documentation becomes available I'll either try to update this section with links, or copy this section into a more complete discussion of the practical mechanisms provided for achieving the simple goal described above.

Chemicals
One of the abstractions provided by Netlab is the notion of chemicals. Neurons, beside producing output values on their axons based on a variety conditions (direct and indirect), are also capable of producing chemicals. The chemicals, much like the axon-level, can be specified to be produced in various concentration-levels based on a variety of environmental factors. The factors that lead to the production of a chemical are specified by the designer and can include stimulus on the neuron's synapses by other axons, other chemical influences in the vicinity of the neuron, or globally present chemicals (among many other factors). To specify a new chemical the designer simply comes up with a name for it.

No characteristics are explicitly specified for a given designer-named chemical. The properties and characteristics of any given named chemical are purely a biproduct of how other objects in the environment (usually other neurons) have been specified to respond/react to them. Responses to any given chemical can be different for different individual instances of a neuron, or for different classes of a neuron (this is simplified, "neuron" is really "object" and can include other super-types besides neurons),
Spacial and Temporal Distances
Netlab's description language provides a way to modularize the design and construction of neural networks. It allows experimenters to produce components, called "units", which contain other components, such as neurons and previously designed units.

The modular construct used to overcome complexity at design time, is preserved in the Netlab run-time, giving Netlab's networks an abstraction of volumetric space, which can be used as a framework when representing both spacial and temporal phenomena.
Pathfinding
Once you have a viable abstraction for representing temporal and volumetric chemical gradients, you can then begin to define all kinds of useful mechanisms based on the influence (e.g., repulsive or attractive) your chemical concentrations have on them. For example, like all neural network packages, Netlab includes the traditional Latent Connections. These are the static connections you determine at design time. Whether or not they actually develop is based on changes to their strengths, but the connection will always be between the same two neurons, which were specified at design time.

Netlab is able to go farther, allowing designers to specify Receptor Pads which are areas on a neuron's synapse-space that can make connections with any other neuron in the network while it is running. Other neurons put out something called a growth cone. Among other things, dynamic connections are facilitated at run time based on affinities or aversions to named chemicals specified for both the growth-cones, and the receptor pads. This allows, for example, a given class of growth cone to be defined to "desire", "seek-out", and "find" its perfect receptor pad, dynamically, as the network runs. It is good to mention again here, that chemicals influencing these movement decisions by growth-cones are also being produced dynamically, and their concentrations will be based on factors that are products of the running network within its dynamic environment.
No Limits On Feedback
A new patented learning method has been developed that completely eliminates past restrictions placed on types or amounts of feedback employed in the structure of the network. Beside just being great for general feedback that may want to span multiple local loops at times, this is also very nice for servo-structures, which put the outside world directly in the feedback path. This allows for representing the outside world as a the correction-factors that must be adapted, in order to correct for outside forces. In essence, the world's complex and chaotic stimuli become a transfer function that sits directly in the network's feedback loop. The network, then performs the function of correcting for inconsistencies in the feedback function, which it can then learn.

Brain Wiring Structure? How About a Donut?

nospam@example.com (John R) — Thu, 12 Aug 2010 12:29:00 -0700

A recent USC study applies a new technique that allows researches to more closely map the brain's wiring. One goal of the study is to better clarify our current understanding of the connection-structure of brains. Also, to try and settle the raging "It's an internetwork" / "It's a hierarchical pyramid" debate.

The Netlab abstraction is designed to facilitate a similar, but slightly different concept of brain wiring-structure, which is visually depicted in the cover art of the book:

Any Connectome You'd Like N^o 1

Netlab's idealized wiring-structure requirement (simplified)

The above diagram should be seen as a cross-section through a sphere, so the word "donut" in this entry-title takes some license. The interior/exterior connection-model, as depicted, does seem to find—at least passing— observational support in the USC study, e.g.,:

"The circuits showed up as patterns of circular loops, suggesting that at least in this part of the rat brain, the wiring diagram looks like a distributed network."

-djr