The emergence of goals in a self-organizing network: A non-mentalist model of intentional actions

Authors:
Yoram Louzoun;Henri Atlan
Affiliations:
Math Department, Bar Ilan University, Ramat Gan, 52900, Israel;Hadassah University Hospital, Jerusalem, Israel and EHESS, Paris, France
Venue:
Neural Networks
Year:
2007

Citing 10
Cited 1

Neural computing: an introduction

Neural computing: an introduction
Hebbian learning of context in recurrent neural networks

Neural Computation
NLq theory: a neural control framework with global asymptotic stability criteria

Neural Networks
From classical models of morphogenesis to agent-based models of pattern formation

Artificial Life
Complexity: metaphors, models, and reality

Complexity: metaphors, models, and reality
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks

Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Modelling Brain Function: The World of Attractor Neural Networks

Modelling Brain Function: The World of Attractor Neural Networks
MOSAIC Model for Sensorimotor Learning and Control

Neural Computation
Similarity in Perception: A Window to Brain Organization

Journal of Cognitive Neuroscience
Stable encoding of large finite-state automata in recurrent neural networks with sigmoid discriminants

Neural Computation

Concepts in complexity engineering

International Journal of Bio-Inspired Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

A model of intentional actions is presented through the operation of two connected neural networks. A deterministic causal recurrent network relates a random initial state to an ordered final state. A perceptron-like, feed-forward network provides a memory mechanism that links the final states to the original initial states. A non-supervised learning mechanism that selects which final states are defined as goals to be retrieved together with initial states leading to them. Causal sequences of states are transformed into procedures directed towards the achievements of goals. We propose a mechanism through which goals and their achievement in goal-directed actions can be emerging properties of self-organizing networks, not initially endowed with intentionality. This allows for a monist, non-mentalist description which does not need to resort to intentional mental states as causes of intentional actions. Cognitive, neurophysiological and philosophical implications are discussed.