Epstein Secrets

3.4.2 Knowledge Representation via Cell Assemblies .................2.05- 257 13.5 Neural Foundations of Learning ..........0 0.000 ee eee 258 13.5.1 Hebbian Learning............ 0.0.00 eee ete eee 258 13.5.2 Virtual Synapses and Hebbian Learning Between Assemblies .......... 258 35:38 Netital DArwitiiSitt +2; case

haphazard or opportunistic combination of algorithms and data structures. At bottom it is motivated by the patternist philosophy of mind laid out in Ben Goertzel’s book The Hidden Pattern [Goe06al, which was in large part a summary and reformulation of ideas presented in a series of books published earlier by th

ake our points about glocal knowledge representation in neural net type systems without discussing some aspects of learning in these systems. 13.5.1 Hebbian Learning The most common and plausible assumption about learning in the brain is that synaptic connec- tions between neurons are adapted via some variant of

e condition may be observing a cat. In this case, the corresponding Concept vertex in the mind of Ben Goertzel is the pattern of activity observed in Ben Goertzel’s brain when his eyes are open and he’s looking in the direction of a cat. The notion of pattern of activity can be made rigorous using mathematical pa

e parameters and relationships guiding what the system pays attention to, at what points in time. This is a term inclusive of Importance Updating and Hebbian Learning. Attentional Currency: Short Term Importance and Long Term Importance values are implemented in terms of two different types of artificial money, ST

ing the activity of the MindAgents, and code for important and exporting data from the Atomspace. e Cognitive Equation: The principle, identified in Ben Goertzel’s 1994 book "Chaotic Logic", that minds are collections of pattern-recognition elements, that work by iteratively recognizing patterns in each other an