Science & Tech - 2

In the book, Netlab Loligo, repeated calls are made for true random number generators (TRNGs) to be included in all CPUs, or at least in those that are intended for use in neural network applications. Naturally, I was very excited to see a headline about Intel having developed one with general purpose use in mind.

Intel's Low-Power “True” Random Number Generator

IEEE has an article about a new “true” random number generator from Intel that has been 10 years in development. Its primary advantage is that, while it is a true RNG, it operates entirely in digital mode using digital devices to obtain randomness from hardware. The slow, energy hogging, analog technology normally needed to glean randomness from Quantum phenomena has been eliminated. It has a few quirks, such as the need to force the outputs of its two mutex inverters high, and the seemingly unavoidable need to compensate using averaging techniques. I expand just a little on these quirks below.

In the spirit of not critiquing something without also offering, at least, a sincere attempt at a solution, I've forwarded a quick (if dirty) attempt at an “all logic gates” DTRNG (Digital True Random Number Generator) below. Only the equations were scratched out at the IEEE blog, I've since produced a circuit diagram graphic, which is included here as well.

George Charles Devol
Inventor of Robot Arm

      

20 February 1912 — 11 August 2011

The self taught inventor of the robotic arm, which has become an icon of factory automation from Detroit to Asia, has died.


Story: Pioneer of the long arm of factory floor technology

From the press release: “Once infected by spores, the worker ants ... leave the nest, find a small shrub and start climbing. The fungi directs all ants to the same kind of leaf: about 25 centimeters [(9.8 inches)] above the ground and at a precise angle to the sun (though the favored angle varies between fungi). How the fungi do this is a mystery.”

[. . .article]



The paper:

Hidden Diversity Behind the Zombie-Ant Fungus Ophiocordyceps unilateralis: Four New Species Described from Carpenter Ants in Minas Gerais, Brazil





A few interesting links to related stuff. Includes some strange and interesting background about ants.

• Youtube short video about Cordyceps fungi
• Ant Colony Optimization Site - Behavior of real ants
• AntWeb
• AntBase (database of ants)
• Bug Guide
  
  
• Ant Death Spiral
  • Ant Spiral Video
  
  
  



• Killer wasps turn spiders into zombies, make them build super web for larvae (VIDEO)
  
  

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

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 algorithm and 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.

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.

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.