Neurophysics: Theoretical and Computational Neuroscience

Neurophysics: theoretical and computational neuroscience consists of many research strands including: (i) How does the brain compute: Distributed dynamic computation (DDC) in neural circuits; (ii) Spatiotemporal patterns in the brain. See below for more details on these research strands. This research area is led by Dr Pulin Gong, and consists of the following people:

PEOPLE

How does the brain compute: Distributed dynamic computation (DDC) in neural circuits

Dr. Gong has proposed a novel theory, the distributed dynamic computation (DDC) theory of neural circuits, to understand how the brain processes information in such an amazingly efficient and flexible way. The conventional theories such as the attractor theory, which are based on the stable states of neural systems, are unable to explain complex spatiotemporal patterns such as propagating wave patterns as widely observed in the brain. In our theory, however, these patterns are essential neural substrates for the brain to carry out distributed computation: information is encoded in spiking wave patterns, information is communicated based on the propagation of these patterns, and information is processed when they interact with each other.

Distributed computing schematic image

The space-time behaviour of propagating spiking waves and distributed computational operations carried out by the interactions of these propagating waves.

Spatiotemporal patterns in the brain

Recently, Dr. Pulin Gong’s group has successfully developed a new method that is very effective in detecting and characterizing coherent spatiotemporal patterns from large-scale data such as MEA recorded spikes and local field potentials. Based on this method, we have found a richer than expected repertoire of coherent structures; beside the plane waves that have been a subject of recent interests, we have found standing waves that are complemented by waves that radiate out or converge to phase singularities (sink patterns), or spiral around them (spiral waves). Importantly, we can figure out the evolution dynamics of these patterns. Currently, we are investigating the functional nature of these patterns by analysing how they are modulated by external stimuli in the visual cortex of marmosets.

Spatiotemporal patterns in cortex.

Representations of different types of spatiotemporal pattern from the visual cortex of marmosets.

Additional areas

Cover of neural computing

Additional research areas include: Collective dynamics of spatially extended, spiking neural circuits and Models of working memory and associative memory. One of our papers (Palmer and Gong, 2013) is a cover article of Neural Computation (see the figure to the right). Please email Pulin Gong for more information.

SELECTED PUBLICATIONS

  1. Yang Qi, Michael Breakspear, Pulin Gong, Subdiffusive Dynamics of Bump Attractors: Mechanisms and Functional Roles. Neural Computation. Accepted, September, 2014.

    Adam Keane and Pulin Gong, Propagating waves can explain irregular neural dynamics, Journal of Neuroscience. Accepted, November, 2014.
  2. P. Gong, Steel, H., Robinson, P., Q, Yang, Dynamic patterns and their interactions in networks of excitable elements. Physical Review E, 88(4), 1-10, 2013.
  3. J. Palmer and P. Gong. Formation and Regulation of Dynamic Patterns in Two- imensional Spiking Neural Circuits with Spike-Timing-Dependent Plasticity. Neural Computation, 25(11), 2833-2857, 2013.
  4. P. Gong, S.T.C. Loi, P.A. Robinson and C.Y.J. Yang, Spatiotemporal pattern formation in two-dimensional neural circuits: role of refractoriness and noise. Biological Cybernetics, 107:1-13, 2013.
  5. Y. Qi, J. Palmer, P. Gong. Discrete breathers in integrate-and-fire oscillator networks. Europhysics Letters, 102(3), 1-6, 2013.
  6. P. Gong and P.A. Robinson, Dynamic pattern formation and collisions in networks of excitable elements. Physical Review E (Rapid Communications), 85, 055101(R), 2012.
  7. H. Stewart, P. Gong, and M. Breakspear, A computational role for bistability and traveling waves in motor cortex. Frontiers in Computational Neuroscience, 6, 67 (2012) DOI: 10.3389/fncom.2012.00067.
  8. T.M. Patten, C.J. Rennie, P.A. Robinson and P. Gong, Human cortical traveling waves: Dynamical properties and correlations with responses. PLoS One, 7: e38392, 2012.
  9. D. van den Berg, P. Gong, M. Breakspear, and C. Van Leeuwen, Fragmentation: Loss of global coherence or breakdown of modularity in functional brain architecture? Frontiers in systems neuroscience, 6: 20 (2012).
  10. A. R. Nikolaev, S. Gepshtein, P. Gong and C. van Leeuwen, Duration of coherence intervals in electrical activity in perceptual organization. Cerebral Cortex. 20, 365-382, 2010.
  11. P. Gong, C. van Leeuwen, Distributed dynamical computation in neural circuits with propagating coherence activity patterns. PLoS Computational Biology, 5(12): e1000611, 2009.
  12. P. Gong and C. van Leeuwen, Dynamically maintained spike timing sequences in networks of pulse-coupled oscillators with delays. Physical Review letters, 98, art. no.048104, 2008.
  13. P. Gong, A.R. Nikolaev, and C. van Leeuwen, Intermittent dynamics of collective phase synchronization in the brain electrical activity, Physical Review E, 76, art. no.011904, 2007.