Online research in philosophy

Artificial neural networks have weaknesses as models of cognition. A conventional neural network has limitations of computational power. The localist representation is at least equal to its competition. We contend that locally connected neural networks are perfectly capable of storing and retrieving the individual features, but the process of reconstruction must be otherwise explained. We support the localist position but propose a “hybrid” model that can begin to explain cognition in anatomically plausible terms.

A review of W. Thomas Miller, III, Richard S. Sutton, and Paul J. Werbos (Eds.) Neural Networks for Control. Cambridge, Massachusetts: The MIT Press. 1990. pp. 524. This multi-disciplinary volume concerns the use of artificial neural networks in controlling dynamical processes. As used here 'dynamical' describes processes, such as certain chemical reaction systems, robots, or manufacturing plants, whose operation is governed by known or unknown non-linear models and which, therefore, are subject to certain types of problems related to unpredictability and chaotic performance. Artificial neural networks (ANN) are mathematical models whose components emulate the function of biological nervous systems.

This paper examines the use of connectionism (neural networks) in modelling legal reasoning. I discuss how the implementations of neural networks have failed to account for legal theoretical perspectives on adjudication. I criticise the use of neural networks in law, not because connectionism is inherently unsuitable in law, but rather because it has been done so poorly to date. The paper reviews a number of legal theories which provide a grounding for the use of neural networks in law. It then examines some implementations undertaken in law and criticises their legal theoretical naïvete. It then presents a lessons from the implementations which researchers must bear in mind if they wish to build neural networks which are justified by legal theories.

It is suggested that the anatomical structures whichmediate consciousness evolved as decisiveembellishments to a (non-conscious) design strategypresent even in the simplest monocellular organisms.Consciousness is thus not the pinnacle of ahierarchy whose base is the primitive reflex, becausereflexes require a nervous system, which the monocelldoes not possess. By postulating that consciousness isintimately connected to self-paced probing of theenvironment, also prominent in prokaryotic behavior,one can make mammalian neuroanatomy amenable todramatically simple rationalization.

I address whether neural networks perform computations in the sense of computability theory and computer science. I explicate and defend
the following theses. (1) Many neural networks compute—they perform computations. (2) Some neural networks compute in a classical way.
Ordinary digital computers, which are very large networks of logic gates, belong in this class of neural networks. (3) Other neural networks
compute in a non-classical way. (4) Yet other neural networks do not perform computations. Brains may well fall into this last class.

The first brief description is given of a project aimed at searching for the neural correlates of consciousness through computer simulation. The underlying model is based on the known circuitry of the mammalian nervous system, the neuronal groups of which are approximated as binary composite units. The simulated nervous system includes just two senses - hearing and touch - and it History..