Introducing: Influence Learning

It Does Not Restrain Parallelism

A single network can be spread out over any number of processors running asynchronously and in parallel. Influence learning mechanisms are only concerned with factors within neurons that are nearby from a connection standpoint. Because of this, a designer is free to produce a very large neural network, in which smaller parts of the network are running on many different processors, each with their own storage, and even their own clock-speeds.

It Supports Continuous Learning

Learning based on influence over outputs is inherently self-limiting and does not over-train weights. Neural networks employing influence based learning can be "always on", and continuously adapt to their environment. This is sometimes called "online learning", though, in common usage that term makes no distinction between continual, and continuous learning.

Online vs. Continuous - As stated, the term online learning is often used to convey the concept of learning methods which are merely continual. Conventionally neural network systems are trained on a particular representative training set (sometimes called "exemplars"). The learning function in a traditional neural network is turned off once it has been "fully" trained on a set of exemplars. At the time it is placed into service it has no ability to learn how to respond to any new situations it may encounter. That said, in some cases, systems have been developed that allow the learning to be turned on and off while they are in service, and this has been termed online learning.

The difference between continuous and continual becomes an important distinction, however, when discussing influence learning. Influence learning does not rely on an externally derived indication of whether to turn on connection-weight adjustments. Weight-adjustments are tacitly limited based on need. This allows adaptation to be always on, and continuously making adjustments as needed for the present situation. In other words, a system can be designed to continuously adapt to the environment with which it interacts. This is especially true when influence learning is combined with multitemporal sysnapses and weight-to-weight learning.

It Can Learn From Neurons Taught By Other Methods

As stated, the learning method recognizes no justifiable reason to know anything about the inner-workings used by its role-models (post-synaptic neurons) to achieve learning. It simply assumes that whatever method they are using is working, and then uses their activity as a template to drive its own learning process.

It Imposes No Limit On Number Of Layers

Because the learning algorithm permits feedback, there is no longer any requirement that neural networks be structured in the traditional layered configuration. That said, a designer may freely choose to limit his or her neural network to be a pure feed-forward structure, with its characteristic layers. It is also the case that networks designed with feedback will often form their own layers as they learn.

In these cases, restrictions on the number of layers that can be employed in such networks no longer exist. Influence learning is only concerned with activity that occurs during signal propagation. There is no reverse propagation of ever-diminishing error values back to pre-synaptic (formerly "hidden") layers of neurons.

To Learn More

This post should give you a fairly good idea of the inner-workings and underlying concept of this new learning method. If you get it, and you're interested, you will find a much more detailed description and discussion in the book: "Netlab Loligo: New Approaches to Neural Network Simulation". Which is available here, or from Amazon.com

It should also be noted that this learning method is now patented, and descriptions do not constitute grant of license.