Chris Trengove

contact: chris.trengove (a) ppw.kuleuven (d) be

Functional Description

In the broadest terms, my interest is in the large-scale neural microcircuitry underpinning cognition. Any investigation of this topic faces a major obstacle: the empirical data is grossly insufficient to constrain models. We have much information about single neuron activity and large scale brain activity but little about what is going on in between.

The typical conservative ‘stop-gap’ way of dealing with this obstacle is to model the intervening scale in the most minimal fashion, as unstructured, random connectivity between large spatial groupings of neurons. While it has its uses, this approach forgoes the chance to include non-trivial and functionally important structure and dynamics in the microcircuitry.

A complementary but more difficult approach which I take is to start to explore the vast space of possible large scale network models of cortical microcircuitry, especially those which take advantage of the capability of neurons to respond in a temporally precise and robust fashion to complex patterned input sequences (as noted by advocates of temporal coding over rate coding).

Important for this study of large- scale microcircuitry is the inclusion of non-trivial complex network topology that idealizes the incorporation of cognitive information into structure. The focus is on the relationship between richly organised spatiotemporal activity patterns and the network structure in which they occur. This includes for instance combining (or binding) of activity patterns, regulation of activity, and the role of global activity in pattern selection. The choice of network structure is facilitated by models of network rewiring and plasticity such as those exhibiting small world emergence (Gong and van Leeuwen, 2004; Rubinov et al. 2009).

To date the work has demonstrated the embedding of a large-scale system of structured microcircuitry in the form of synfire chains within a cortical volume on the cubic millimetre scale (Trengove et al, 2012) and the extension of this to a recurrently coupled system of chains that exhibits regulated ongoing activity with rich internal structure that can be understood using the concepts of dynamic effective connectivity and percolation centrality, assisted by quantitative model reduction (Trengove et al, 2013, in preparation). Methods used include large-scale network simulation, model reduction, complex network analysis and pattern classification.




Positions Held

2012-present Researcher, Perceptual Dynamics Laboratory, Leuven, Belgium

2009-2012 Research Scientist, Brain and Neural Systems Team / Laboratory for Statistical Neurophysics, RIKEN, Saitama, Japan

2006-2009 Research Fellow, Bionic Ear Institute, Melbourne Australia


2006 Ph.D. in Computer Science, University of Technology, Sydney Australia

1987 B.Sc (Hons), Macquarie University, Sydney Australia



Trengove, C., van Leeuwen, C., & Diesmann, M. (2012). High-Capacity Embedding of Synfire Chains in a Cortical Network Model. Journal of Computational Neuroscience.

Gerstein, GL, Williams, ER, Diesmann, M, Gruen S, Trengove C. (2012) Detecting synfire chains in parallel spike data. Journal of Neuroscience Methods.

Alexander DM, Trengove C, Sheridan PE, van Leeuwen C. (in press) Generalization of learning by synchronous waves: from perceptual organization to invariant organization. Cognitive Neurodynamics, 5(2) pp. 113-132.

Alexander DM, Trengove C, Wright JJ, Boord PR, Gordon E. (2006) Measurement of phase gradients in the EEG. Journal of Neuroscience Methods, 156(1-2) pp. 111-128.

Alexander DM, Trengove C, Johnston P, Cooper T, August JP, Gordon E. (2005) Separating individual skin conductance responses in a short interstimulus-interval paradigm. [Journal Paper] Journal of Neuroscience Methods, 146(1) pp. 116-123.