Neurocomputing: foundations of research
Neurocomputing: foundations of research
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Hybrid architectures for intelligent systems
A model of cortical associative memory based on Hebbian cell assemblies
selected papers from the Swedish conference on Connectionism in a broad perspective
Knowledge-based artificial neural networks
Artificial Intelligence
Extraction of rules from discrete-time recurrent neural networks
Neural Networks
Hybrid neural plausibility networks for news agents
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Neural codes and distributed representations: foundations of neural computation
Neural codes and distributed representations: foundations of neural computation
Knowledge Extraction from Transducer Neural Networks
Applied Intelligence
Emergent neural computational architectures based on neuroscience: towards neuroscience-inspired computing
Biological Grounding of Recruitment Learning and Vicinal Algorithms in Long-Term Potentiation
Emergent Neural Computational Architectures Based on Neuroscience - Towards Neuroscience-Inspired Computing
An Investigation into the Role of Cortical Synaptic Depression in Auditory Processing
Emergent Neural Computational Architectures Based on Neuroscience - Towards Neuroscience-Inspired Computing
Images of the Mind: Brain Images and Neural Networks
Emergent Neural Computational Architectures Based on Neuroscience - Towards Neuroscience-Inspired Computing
Journal of Artificial Intelligence Research
Syntax, preference, and right attachment
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 2
Preference Moore machines for neural fuzzy integration
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Spike-Timing Dependent Competitive Learning of Integrate-and-Fire Neurons with Active Dendrites
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Processing of information in synchroneously firing chains in networks of neurons
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Compilation of symbolic knowledge and integration with numeric knowledge using hybrid systems
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
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In the past, a variety of computational problems have been tackled with different connectionist network approaches. However, very little research has been done on a framework which connects neuroscience-inspired models with connectionist models and higher level symbolic processing. In this paper, we outline a preference machine framework which focuses on a hybrid integration of various neural and symbolic techniques in order to address how we may process higher level concepts based on concepts from neuroscience. It is a first hybrid framework which allows a link between spiking neural networks, connectionist preference machines and symbolic finite state machines. Furthermore, we present an example experiment on interpreting a neuroscience-inspired network by using preferences which may be connected to connectionist or symbolic interpretations.