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Simon Stringer

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Simon Stringer
Alma materBSc University of Kent
PhD University of Reading
Scientific career
FieldsTheoretical Neuroscience
Computational Neuroscience
Artificial Intelligence
InstitutionsUniversity of Oxford
Doctoral advisorNancy K. Nichols
Websitewww.oftnai.org

Simon Stringer is a departmental lecturer,[1] Director of the Oxford Centre for Theoretical Neuroscience and Artificial Intelligence, and Editor-in-Chief of Network: Computation in Neural Systems[2] published by Taylor & Francis.

Research

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Stringer and his research group develop biological computer simulations[3] of the neuronal mechanisms underpinning various areas of brain function, including visual object recognition, spatial processing and navigation, motor function, language and consciousness.

In particular, the study published in Psychological Review[4] and Interface Focus 2018,[5] the Royal Society's cross-disciplinary journal, proposes a novel approach to solve the Binding 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. Binding neurons 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. This finding represents an advancement towards the future development of artificial general intelligence and machine consciousness.[9] According to 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.[10][11]

References

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  1. ^ "Personal Webpage".
  2. ^ "Editorial board". Network: Computation in Neural Systems (journal web site). Taylor & Francis. Retrieved 2024-05-19.
  3. ^ "University of Oxford developing Spiking Neural Networks with Novatech". Novatech. August 2018.
  4. ^ Eguchi, A.; Isbister, J.; Ahmad, N.; Stringer, S. (2018). "The emergence of polychronization and feature binding in a spiking neural network model of the primate ventral visual system" (PDF). Psychological Review. 125 (4): 545–571. doi:10.1037/rev0000103. PMID 29863378. S2CID 44165646.
  5. ^ Isbister, J.; Eguchi, A.; Ahmad, N.; Galeazzi, J.M.; Buckley, M.J.; Stringer, S. (2018). "A new approach to solving the feature-binding problem in primate vision". Interface Focus. 8 (4). The Royal Society: 20180021. doi:10.1098/rsfs.2018.0021. PMC 6015810. PMID 29951198.
  6. ^ "Feature Binding within a Spiking Neural Network Model". University of Bristol. July 2018.
  7. ^ Izhikevich, EM (2006). "Polychronization: computation with spikes". Neural Computation. 18 (2): 245–282. doi:10.1162/089976606775093882. PMID 16378515. S2CID 14253998.
  8. ^ Duncan J.; Humphreys GW. (1989). "Visual Search and Stimulus Similarity" (PDF). Psychological Review. 96 (3): 433–58. doi:10.1037/0033-295x.96.3.433. PMID 2756067. S2CID 8056977.
  9. ^ "Developments in machine learning". SC Magazine UK. January 2018.
  10. ^ The Future of Science Symposium. University of Oxford. 2017.
  11. ^ The weird events that make machines hallucinate. BBC Future. 2019.
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