Simon Stringer

Simon Stringer
Alma mater BSc University of Kent
PhD University of Reading
Scientific career
Fields Theoretical Neuroscience
Artificial Intelligence
Institutions University of Oxford
Website www.oftnai.org

Simon Stringer is a British Mathematician, Professor of Computational Neuroscience at the University of Oxford, Director of the Oxford Centre for Theoretical Neuroscience and Artificial Intelligence, and Editor-in-Chief of Network: Computation in Neural Systems [1] published by Taylor & Francis.

Career

Stringer has worked across a range of different areas of applied mathematics such as control systems, computational aerodynamics and epidemiology. He joined the Department of Experimental Psychology at the University of Oxford in 1993, and he became director of the Oxford Centre for Theoretical Neuroscience and Artificial Intelligence in 2006.

Research

Stringer and his research group develops biologically inspired computer simulations[2] of the neuronal mechanisms underpinning various areas of brain function, including visual object recognition, spatial processing and navigation, motor function, language and consciousness.

Recently, the team made a breakthrough in unlocking one of the unresolved mysteries of psychology and neuroscience, the classic feature-binding problem[3], which refers to the ability of the brain to represent the hierarchical binding relationships between all of the features within a visual scene. The study published in Psychological Review [4] and Interface Focus 2018 [5], the Royal Society's cross-disciplinary journal, proposes a novel approach to this timeless problem. Spiking Neural Network simulations[6] of the primate ventral visual system have shown the gradual emergence of a subpopulation of neurons, called polychronous neuronal groups (PNGs), that exhibits regularly repeating spatiotemporal patterns of spikes. The underlying phenomenon of these characteristic patterns of neural activity is known as polychronization [7]. The main point is that within these PNGs exist neurons, called binding neurons, that learn to represent the hierarchical binding relationships between lower and higher level visual features in the hierarchy of visual primitives, at every spatial scale and across the entire visual field. This observation is consistent with the hierarchical nature of primate vision proposed by the two neuroscientists John Duncan and Glyn W. Humphreys almost thirty years ago.[8] Furthermore this proposed mechanism for solving the binding problem suggests that information about visual features at every spatial scale, including the binding relations between these features, would be projected upwards to the higher layers of the network, where spatial information would be available for readout by later brain systems to guide behavior. This mechanism has been called the holographic principle.

These feature binding representations are at the core of the capacity of the visual brain to perceive and make sense of its visuospatial world, and of the consciousness itself. Therefore, this finding represents a fundamental advancement towards the future development of artificial general intelligence and machine consciousness [9] [10]. Indeed, as highlighted by Stringer:

"Today’s machines are unable to perceive and comprehend their working environment in the same rich semantic way as the human brain. By incorporating these biological details into our models [...] will allow computers to begin to make sense of their visuospatial world in the same way as the human brain."[11]

Publications

Podcasts and videos

See also

References

  1. ↑ "Network: Computation in Neural Systems – New Editor-in-Chief Announcement". Retrieved 26 January 2018.
  2. ↑ "University of Oxford developing Spiking Neural Networks with Novatech". Novatech. August 2018.
  3. ↑ von der Malsburg, Christoph, (1999). The What and Why of Binding. Cell Press.
  4. ↑ Eguchi A.; et al., (2018). The emergence of polychronization and feature binding in a spiking neural network model of the primate ventral visual system. Psychological Review.
  5. ↑ Isbister, J.; et al. (2018). "A new approach to solving the feature-binding problem in primate vision". The Royal Society.
  6. ↑ "Feature Binding within a Spiking Neural Network Model". University of Bristol. July 2018.
  7. ↑ Izhikevich, EM, (2006). Polychronization: computation with spikes. Neural Computation.
  8. ↑ Duncan J.; Humphreys GW. (1989). Visual Search and Stimulus Similarity (PDF). Psychological Review.
  9. ↑ "Intelligent machines are starting to catch up". The Times. August 2018.
  10. ↑ "Developments in machine learning". SC Magazine UK. January 2018.
  11. ↑ The Future of Science Symposium. University of Oxford. 2017.


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