We propose that the isomorphism generally observed between the representations
composing our momentary phenomenal experience and the structure of the world is the end …

Using neural networks to represent value functions in reinforcement learning algorithms
often involves a lot of work in hand-crafting the network structure, and tuning the learning …

An algorithm for performing simultaneous growing and pruning of cascade-correlation (CC)
neural networks is introduced and tested. The algorithm adds hidden units as in standard …

The new field of developmental robotics faces the formidable challenge of implementing
effective learning mechanisms in complex, dynamic environments. We make a case that …

This chapter presents and discusses several well-known constructive neural network
algorithms suitable for constructing feedforward architectures aiming at classification tasks …

I challenge here the concept of SOC in regard to the question of the consciousness or
unconsciousness of logical errors. My commentary offers support for the demonstration of …

This paper presents a novel approach for estimating the rotor position of a Switched
Reluctance Motor (SRM) drive system using the Cascade Correlation Artificial Neural …

We consider Perruchet & Vinter's (P&V's) central claim that all mental representations are
conscious. P&V require some way of fixing their meaning of representation to avoid the …

A novel adaptive noise cancellation algorithm using cascaded correlation neural networks is
described. In the proposed algorithm the objective is to filter out an interference component …

A new type of neural network is introduced, where symbolic rules are combined using a
constructive algorithm. Initially, symbolic rules are converted into networks. Rule-based …