Brain Inspired Algorithms

Google Scholar

You can also find out about my published research on Google Scholar, which, as well as providing links to the papers and their abstracts, additionally provides the latest citation counts and index values.


After working for nearly a decade in sub-areas of artificial intelligence (robotics, temporal reasoning, constraint satisfaction and satisfiability), by 2006 I started to question the underlying idea that general artificial intelligence could arise by integrated all the sub-areas of AI into a unified Heath Robinson-type hybrid system. For the more neuroscience discovered about the one structure that we know exhibits general intelligence (i.e. the human neocortex) the less it looked like it implemented a hybrid set of independent specialised systems. Instead the neocortex showed itself to possess a remarkably uniform structure that seems to be performing the same basic function across its entire surface. And what has emerged from twenty-first century neuroscience is the idea that the neocortex is primarily engaged in making predictions about its own inputs and outputs – which has come to be known as the theory of predictive processing.

Taking heed of these developments, I formed the Cognitive Computing Unit at Griffith University as a centre of research where we could explore the computational implementation of predictive processsing and the following papers present the outcomes of this research. The primary focus has been the investigation and development of a particular implementation of predictive processing known as Hierarchical Temporal Memory, first proposed by Jeff Hawkins in the mid-2000s. This led to my own proposal of Hierarchical Temporal Intentionality (see ASSC 2014 below) which brings together the idea of predictive processing with the discoveries and insights of twentieth century phenomenology – connecting the brain science with our immediate experience of being conscious. This synthesis of science and consciousness is also a central theme in my new book The Questioning of Intelligence (see the Books page for more details, and The Being of Form below).

As with my work in constraint satisfaction, much of the actual coding and experimental evaluation reported in the papers below was performed by the PhD and Honours students I was supervising. And, as is usual, you can tell who had the greatest input into each paper by the order in which the author’s names are listed. Of these authors, I have been the primary PhD supervisor for Benjamin Cowley, Adam Kneller and Linda Main, and primary Honours supervisor for Andrew Srbic (for more detail on the pre-2010 papers co-authored with Jolon Faichney see the Handwriting Recognition page).

Brain-inspired algorithms


Thornton, J. (2021). The Being of Form. In: The Questoning of Intelligence: A phenomenological exploration of what it means to be intelligent. FUBText: Brighton, pp. 320-350, ISBN 978-1-8384787-0-4


Cowley, B., Thornton, J. R., Main, L. & Sattar, A. (2018). Precision without Precisions: Handling uncertainty with a single predictive model. In: ALIFE 2018, Proceedings of the 2018 Conference on Artificial Life, Tokyo, Japan, MIT Press, pp. 129-136.


Cowley, B., Thornton, J. R., Main, L. & Sattar, A. (2017). Dynamic Thresholds for Self-Organizing Predictive Cells. In: ECAL 2017, Proceedings of the 14th European Conference on Artificial Life, Lyon, France, MIT Press, pp. 114-121.

Cowley, B. & Thornton, J. R. (2017). Feedback Modulated Attention within a Predictive Framework. ACALCI 2017, Artificial Life and Computational Intelligence, 3rd Australasian Conference. Lecture Notes in Computer Science, 10142, 61-73. Springer, ISSN 0302-9743.

Main, L. & Thornton, J. R. (2017). Stable Sparse Encoding for Predictive Processing. In: SSCI 2017, Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, Hawaii, USA, IEEE, pp. 1-8.


Kneller, A. & Thornton, J. R. (2015). Distal Dendrite Feedback in Hierarchical Temporal Memory. In: IJCNN 2015, Proceedings of the 2015 International Joint Conference on Neural Networks, Killarney, Ireland, IEEE, pp. 385-392.

Main, L. & Thornton, J. R. (2015). A Cortically Inspired Model for Bioacoustics Recognition. ICONIP 2015: 22nd International Conference on Neural Information Processing, Istanbul, Turkey, Lecture Notes in Computer Science, 9492, 348-355, Springer, ISSN 0302-9743.

Thornton, J. R. (2015). The Transcendence of Computational Intelligence. PhD Confirmation Document, 141 pp. Completed under the supervision of John Mandalios, School of Humanities, Griffith University.


Cowley, B., Kneller, A. & Thornton, J. R. (2014). Cortically-Inspired Overcomplete Feature Learning for Colour Images. PRICAI 2014: Trends in Artificial Intelligence, 13th Pacific Rim International Conference on Artificial Intelligence. Lecture Notes in Computer Science, 8862, 720-732, Springer, ISSN 0302-9743.

Thornton, J. R. (2014). Hierarchical Temporal Intentionality. Abstract in: ASSC 18: Handbook of the 18th Conference of the Association for the Scientific Study of Consciousness, Brisbane, Australia, University of Queensland, pp. 41-42.


Thornton, J. R. & Srbic, A. (2013). Spatial Pooling for Greyscale Images. International Journal of Machine Learning and Cybernetics. 4(3), 207-216, Springer, ISSN 1868-8071.


Thornton, J. R., Main L. & Srbic, A. (2012). Fixed Frame Temporal Pooling. AI 2012: 25th Australasian Joint Conference on Artificial Intelligence, Lecture Notes in Computer Science, 7691, 707-718, Springer, ISSN 0302-9743.


Thornton, J. R., Srbic, A., Main, L. & Chitsaz, M. (2011). Augmented Spatial Pooling. AI 2011: 24th Australasian Joint Conference on Artificial Intelligence, Lecture Notes in Computer Science. 7106, 261-270, Springer, ISSN 0302-9743.


Thornton, J. R., Faichney, J., Blumenstein, M., Nguyen, V. & Hine, T. (2009). Offline Cursive Character Recognition: A state-of-the-art comparison. In: IGS 2009: Proceedings of the 14th Conference of the International Graphonomics Society, Dijon, France, pp. 148-151.


Thornton, J. R., Faichney, J., Blumenstein, M. & Hine, T. (2008). Character Recognition using Hierarchical Vector Quantization and Temporal Pooling. AI 2008: 21st Australasian Joint Conference on Artificial Intelligence. Lecture Notes in Computer Science, 5360, 562-572, Springer, ISSN 0302-9743.


Thornton, J. R., Gustafsson, T., Blumenstein, M. & Hine, T. (2006). Robust Character Recognition using a Hierarchical Bayesian Network. AI 2006: 19th Australian Joint Conference on Artificial Intelligence. Lecture Notes in Computer Science, 4304, 1259-1264, Springer, ISSN 0302-9743.