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Browsing by Author "Bolouri, H."
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Artificial evolution: modelling the development of the retina
Rust, A.G.; Adams, R.G.; George, S.; Bolouri, H. (University of Hertfordshire, 1996)The evolution of neural systems relies on the repeated modification of developmental programmes contained within genes. This paper proposes that to efficiently investigate artificial evolution, developmental processes must ... -
Developmental artificial neural networks for shape recognition: a model of the retina
Rust, A.G.; George, S.; Bolouri, H.; Adams, R.G. (University of Hertfordshire, 1996)There has been recent interest in mimicking the self-organising processes of biological development to design artificial neural networks. An a priori decision must however be made as to the degree of biological detail ... -
Developmental evolution of an edge detecting retina
Rust, A.G.; Adams, R.G.; George, S.; Bolouri, H. (Springer Nature, 1998) -
Developmental neural networks for shape recognition: motivation and review
Rust, A.G.; Bolouri, H. (University of Hertfordshire, 1996)Artificial Neural Networks (ANNs) are widely exploited in Artificial Intelligence applications. However, at present, there is a wide gap in functionality between artificial and biological neural systems. Appropriate neuron ... -
The ERATO Systems Biology Workbench : enabling interaction and exchange between software tools for computational biology
Hucka, M.; Finney, A.; Sauro, H.; Bolouri, H.; Doyle, J.; Kitano, H. (2002)Researchers in computational biology today make use of a large number of different software packages for modeling, analysis, and data manipulation and visualization. In this paper, we describe the ERATO Systems Biology ... -
Evolution of developmental ontogeny for robustly reproducible phenotypes
Rust, A.G.; Adams, R.G.; George, S.; Bolouri, H. (University of Hertfordshire, 1998)Development has been used by a number of researchers as an efficient means of nonlinearly decoding genetic information is evolutionary systems. We show that developmental routines which do not utilise cell-cell interactions ... -
Evolutionary neural topiary: growing and sculpting artifical neurons to order
Rust, A.G.; Adams, R.G.; Bolouri, H. (2000) -
Evolving computational neural systems using synthetic developmental mechanisms
Adams, R.G.; Rust, A.G.; Schilstra, M.; Bolouri, H. (Elsevier, 2003)Biological development is highly complex, beginning with an egg and resulting in a complete living organism (Purves and Lichtman, 1985). Development is essentially sequential, establishing a gross structure which becomes ... -
A Finite State Automation Model for Multi-Neuron Simulations
Schilstra, M.; Rust, A.G.; Adams, R.G.; Bolouri, H. (2002) -
A Genomic Regulatory Network for Development
Davidson, E.H.; Rast, J.P.; Oliveri, P.; Ransick, A.; Calestani, C.; Yuh, C.H.; Minokawa, T.; Amore, G.; Hinman, V.; Arenas-Mena, C.; Otim, A.; Brown, C.T.; Livi, C.B.; Lee, P.Y.; Revilla, R.; Rust, A.G.; Pan, Z.; Schilstra, M.; Clarke, P.J.C.; Arnone, M.I.; Rowen, L.; Cameron, R.A.; McClay, D.R.; Hood, L.; Bolouri, H. (2002)Development of the body plan is controlled by large networks of regulatory genes. A gene regulatory network that controls the specification of endoderm and mesoderm in the sea urchin embryo is summarized here. The network ... -
Image redundancy reduction for neural network classification using discrete cosine transforms
Pan, Z.; Rust, A.G.; Bolouri, H. (2000)High information redundancy and strong correlations in face images result in inefficiencies when such images are used directly in recognition tasks. In this paper, discrete cosine transforms (DCT) are used to reduce image ... -
Image redundancy reduction for neural network classification using discrete cosine transforms
Pan, Z.; Rust, A.G.; Bolouri, H. (Institute of Electrical and Electronics Engineers (IEEE), 2000)High information redundancy and strong correlations in face images result in inefficiencies when such images are used directly in recognition tasks. In this paper, discrete cosine transforms (DCT) are used to reduce image ... -
Molecular self-organisation in a developmental model for the evolution of large-scale artificial neural networks
Bolouri, H.; Adams, R.G.; George, S.; Rust, A.G. (1998)We argue that molecular self-organisation during embryonic development allows evolution to perform highly nonlinear combinatorial optimisation. A structured approach to architectural optimisation of large-scale Artificial ... -
New Computational Approaches for Analysis of cis-Regulatory Networks
Brown, C.T.; Rust, A.G.; Clarke, P.J.C.; Pan, Z.; Schilstra, M.; De Buysscher, T.; Griffin, G.; Wold, B.J.; Cameron, R.A.; Davidson, E.H.; Bolouri, H. (2002) -
A provisonal regulatory gene network for specification of endomesoderm in the sea urchin embryo
Davidson, E.H.; Rast, J.P.; Oliveri, P.; Ransick, A.; Calestani, C.; Yuh, C.H.; Minokawa, T.; Amore, G.; Hinman, V.; Arenas-Mena, C.; Otim, O.; Brown, C.T.; Livi, C.B.; Lee, P.Y.; Revilla, R.; Schilstra, M.; Clarke, P.J.C.; Rust, A.G.; Pan, Z.; Arnone, M.I.; Rowen, L.; Cameron, R.A.; McClay, D.R.; Hood, L.; Bolouri, H. (2002)We present the current form of a provisional DNA sequence-based regulatory gene network that explains in outline how endomesodermal specification in the sea urchin embryo is controlled. The model of the network is in a ... -
Staged training of Neocognitron by evolutionary algorithms
Pan, Z.; Sabisch, T.; Adams, R.G.; Bolouri, H. (Institute of Electrical and Electronics Engineers (IEEE), 1999)The Neocognitron, inspired by the mammalian visual system, is a complex neural network with numerous parameters and weights which should be trained in order to utilise it for pattern recognition. However, it is not easy ... -
Towards Computational Neural Systems through Developmental Evolution
Rust, A.G.; Adams, R.G.; George, S.; Bolouri, H. (2001)The capability of creating artificial neural networks with biologically-plausible characteristics, is becoming ever more attainable through the greater understanding of biological neural systems and the constant increases ...