• L. Andrew Coward. The Recommendation Functional Architecture as the Basis for a Neurophysiological Understanding of Cognition. New Trends in Cognitive Science, pages 91–105, 1995.
• L. Andrew Coward. A physiologically based approach to consciousness. New Ideas in Psychology, 17(3):271–290, 1999. (doi:10.1016/S0732-118X(99)00028-8)
• L.Andrew Coward. A Physiologically Based System Theory of Consciousness. In S. Jordan, editor, Modeling Consciousness Across the Disciplines, pages 113–178. UPA, Maryland, 1999.
• Tamas D. Gedeon, L. Andrew Coward, and Bailing Zhang. Results of Simulations of a System with the Recommendation Architecture. In Proceedings of the 6th International Conference on Neural Information Processing, pages 78–84, 1999.
• L. Andrew Coward. A Cognitive Architecture based on the Recommendation Architecture. pages 1–31, 2000.
• L. Andrew Coward. A Functional Architecture Approach to Neural Systems. International Journal of Systems Research and Information Systems, 9(4):69–120, 2000.
• L. Andrew Coward. Modeling Cognitive Processes with the Recommendation Architecture. In Proceedings of the 18th Twente Workshop on Language Theory, pages 47–67, 2000.
• L. Andrew Coward and Tamas Gedeon. Optimization of Architectural Parameters in a Simulated Recommendation Architecture. Journal of Advanced Computational Intelligence, 2000.
• L. Andrew Coward. The Recommendation Architecture: lessons from large-scale electronic systems applied to cognition. Cognitive Systems Research, 2(2):111–156, 2001. (doi:10.1016/S1389-0417(01)00024-9)
• L. Andrew Coward, Tamas D. Gedeon, and William D. Kenworthy. Application of the Recommendation Architecture To Telecommunications Network Management. International Journal of Neural Systems, 11(4):323–327, 2001. (doi:10.1142/S0129065701000783)
• L. Andrew Coward. A Basis for a Rigorous Cognitive Science: Maintaining Context for Information Exchange between Modules in a Functional Hierarchy. In Proceedings of the Twenty-Fourth Annual Conference of the Cognitive Science Society, volume 24, 2002.
• Uditha Ratizayake and Tamas D. Gedeon. Application of the Recommendation Architecture for Discovering Associtive Similarities in Text. In Proceedings of the 9th International Conference on Neural Information Processing (ICONIP'02), volume 4, 2002.
• L. Andrew Coward. Architectural Constraints Imposed by Functional Complexity: Implications for the structure and operation of the brain. 2003.
• L. Andrew Coward. General Constraints on the Architectures of Functionally Complex Learning Systems: Implications for Understanding Human Memory. In B. Kokinov and W. Hirst, editors, Constructive Memory, NBU Series in Cognitive Science. 2003.
• Uditha Ratnayake. Application of the Recommendation Architecture Model for Text Mining. PhD Thesis, 2003.
• L. Andrew Coward. Managing Interference Between Prior and Later Learning. International Conference on Neural Information Processing, 2004.
• L. Andrew Coward. Modeling Cognitive Development in the Human Brain. In International Conference on Development and Learning, 2004.
• L. Andrew Coward. Simulation of a Proposed Binding Model. In Brain Inspired Cognitive Systems, 2004.
• L. Andrew Coward. The Recommendation Architecture Model for Human Cognition. In Proceedings of the Conference on Brain Inspired Cognitive Systems, volume 7, pages 1–32, 2004.
• L. Andrew Coward and Nikos A. Salingaros. The Information Architecture of Cities. Journal of Information Science, 30(2):107–118, 2004. (doi:10.1177/0165551504041682)
• L. Andrew Coward and Ron Sun. Criteria for an effective theory of consciousness and some preliminary attempts. Consciousness and Cognition, 13(2):268–301, 2004. (doi:10.1016/j.concog.2003.09.002)
• L. Andrew Coward, Tamas D. Gedeon, and Uditha Ratnayake. Learning complex combinations of operations in a hybrid architecture. In Proceedings of the IEEE International Conference on Fuzzy Systems, pages 923–928, 2004. (doi:10.1109/FUZZY.2004.1375531)
• L. Andrew Coward. Accounting for episodic, semantic and procedural memory in the Recommendation Architecture cognitive model. Progress in Neural Processing, 16, 2005.
• L. Andrew Coward. Practical Architectural Limits on Complex Learning Systems. 2005.
• L. Andrew Coward and Tamas D. Gedeon. A model for representation of concepts in the brain. In Proceedings of the International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning, pages 154–160, 2005.
• L. Andrew Coward and Tamás D. Gedeon. Physiological Representation of Concepts in the Brain. International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning, 2005.
• L. A. Coward. Constraints on the Design Process for Systems with Human Level Intelligence. In The 2006 IEEE International Joint Conference on Neural Network Proceedings, pages 427–434, 2006. (doi:10.1109/IJCNN.2006.1716124)
• L.A. Coward. Constructing a Physiologically Realistic Machine Model of Consciousness. Brain Inspired Cognitive Systems, 2006.
• L. Andrew Coward and Ron Sun. Hierarchical approaches to understanding consciousness. Neural Networks, 20(9):947–954, 2007. (doi:10.1016/j.neunet.2007.09.009)
• L. Andrew Coward. The Hippocampal System as the Cortical Resource Manager: AModel Connecting Psychology, Anatomy and Physiology. Brain Inspired Cognitive Systems, pages 315–364, 2009. (doi:10.1007/978-0-387-79100-5)
• L. Andrew Coward. The Hippocampal System As the Manager of Neocortical Declarative Memory Resources. In Brain Inspired Cognitive Systems, pages 315–364. 2009. (doi:10.1142/9789812834232_0006)
• L. Andrew Coward and Tamas O. Gedeon. Implications of resource limitations for a conscious machine. Neurocomputing, 72(4-6):767–788, 2009. (doi:10.1016/j.neucom.2008.06.015)
• L. A. Coward. Brain anatomy and artificial intelligence. In International Conference on Artificial General Intelligence, 2011. (doi:10.1007/978-3-642-22887-2_27)
• L. Andrew Coward. ModellingMemory and Learning Consistently from Psychology to Physiology. In Vassilis Cutsuridis, Amir Hussain, and John G. Taylor, editors, Perception-Action Cycle, pages 63–133. 2011. (doi:10.1007/978-1-4419-1452-1)
• L. Andrew Coward. Towards a theoretical neuroscience. In Towards a Theoretical Neuroscience: from Cell Chemistry to Cognition, pages 389–393. 2013. (doi:10.1007/978-94-007-7107-9)
• L. Andrew Coward. Brain computational primitives. Procedia Computer Science, 41:164–175, 2014. (doi:10.1016/j.procs.2014.11.100)
• L. Andrew Coward and Tamas D. Gedeon. Using the Change Manager Model for the Hippocampal System to Predict Connectivity and Neurophysiological Parameters in the Perirhinal Cortex. Computational Intelligence and Neuroscience, 2016.