Stand Out Publishing
Excerpts for book: Netlab Loligo
Text from the back cover graphic
This book describes a programmer's attempt to create a software
abstraction that embodies some of the understanding gained in the fields of
neurology, behavior, cognition, learning, and philosophy of mind. A variety of
novel concepts and methods have emerged from the exploration, which are
Constant Learning — Taken together, the described
innovations overcome the obstacles normally encountered in the design of
systems capable of continuously learning and adapting within a complex
milieu. This is a primary driving goal in the design of Netlab.
No Restrictions On Feedback — A new learning method,
called Influence Learning, is presented that is based on a
given neuron using its more influential post-synaptic neurons as role models.
Existing neural network models often impose restrictions in the amount,
or type, of feedback used, due to requirements imposed by their learning
methods. Such limitations are completely overcome in Netlab.
Giving Adaptive Systems a “Present” — A new
theory of learning is introduced in the book, which embodies and predicts the
neurobiological perception of a “present moment”. It is
characterized by each connection-point having more than one distinct
connection-strength. Patented methods included in the described simulation
approach fully model the theory.
Poly-Temporal Synapses — A new structure, and
accompanying learning method called Weight-To-Weight Learning, is introduced
and described. It is based on the above theory of learning. These new
mechanisms implement and model the biological experience of a “present
moment.” The advantage is that neural networks are free to quickly and
sharply adapt to their present situation, while preserving and enhancing
long-term response connections.
Device-Based Neural Network Design and Construction —
Netlab's approach to design is modular, resembling the process by which
complex electronic devices are constructed from simpler assemblies. Each
assembly, once designed, can be used as a component within the design of
other, more complex assemblies.
Introducing Noodletm, a Neural Network Description
Language — A chapter introduces, and briefly describes Netlab's
new hardware-description programming language. It allows designers to
divide-and-conquer complexity, via component libraries and the device-based
approach alluded to above.
(c) Dominic John Repici, 2009-2012