Dr Samuel Johnson BSc MSc PhD

Dr Samuel Johnson

School of Mathematics
Associate Professor in Applied Mathematics

Contact details

Address
School of Mathematics
Watson Building
University 麻豆精选
Edgbaston
Birmingham
B15 2TT
UK

Dr Johnson's research focuses on complex systems, and in particular on the relationship between structure and dynamics. Many of the questions his work addresses relate to ecology, neuroscience or society.

Qualifications

  • PhD in Physics (Interplay between Network Topology and Dynamics in Neural Systems), University of Granada, 2011
  • Certificate of Teaching Aptitude in Mathematics, University of Granada, 2008.
  • MSc in Physics and Mathematics (specialising in mathematical biology), University of Granada, 2008
  • BSc in Physics, University of Granada, 2006

Biography

Sam Johnson studied Physics and Mathematics at the University of Granada, Spain, where he did a PhD entitled 'Interplay between Network Topology and Dynamics in Neural Systems', under the supervision of Joaquin Torres and Joaquin Marro. He was a postdoc at the University of Oxford working with Nick Jones before taking up a Marie Curie research fellowship at Imperial College London. After a brief period in a joint position between DNV GL and the University of Bath, he became a Warwick Zeeman Lecturer at the University of Warwick in 2014. In 2017 he took up his current position at the University 麻豆精选 as Lecturer in Applied Mathematics.

Teaching

Semester 2

LM Advanced Mathematical Biology

Research

Research activity

Sam has many research interests, most of which can be placed under the umbrella of 'complex systems'. Some of the topics he is currently working on are as follows.

Trophic structure of directed networks: Together with colleagues in Granada, Sam recently studied a network property called 'trophic coherence', (Johnson et al., PNAS, 2014). This and subsequent work (with colleagues Miguel Ángel Muñoz, Virginia Dominguez, Janis Klaise and Nick Jones) has shown that trophic coherence is related to many aspects of complex systems, including the prevalence of cycles and feedback loops, ecological stability, motif distributions and spreading processes such as epidemics and neuronal avalanches. It has also opened several questions, such as: Does trophic coherence account for the existence of large, complex ecosystems? Can trophic structure be used to identify node function in systems like gene regulatory networks? Is there a general theory relating feedback and stability in complex, dynamical systems.

Human ecology: Work with Weisi Guo, Xueke Lu and Guillem Mosquera has show that the prevalence of violence around the world is related to the spatial distribution of cities according to a remarkably simple and robust law. We are currently looking into the causes of this effect though a combination of data analysis and agent based modelling and looking for ways in which our results can be applied.

Brain development: The neural network underlying all mental activity comes into being in a curious way: at birth it has a great many synapses (connections between neurons), about half of which are 'pruned' throughout infancy. Recent work with Ana Paula Millán, Joaquin Torres and Joaquin Marro describes a mechanism which could explain this phenomenon with a simple neural network model. We are now studying this process with more realistic neural modelling, looking into its effects in other complex systems. 

Other activities

Adviser to Polymaths R&D Ltd

Member of the Conflict Research Society

Publications

Recent publications

Article

Drysdale, C & Johnson, S 2025, '', Frontiers in Applied Mathematics and Statistics, vol. 10, 1512865.

Deco, G, Sanz Perl, Y, Johnson, S, Bourke, N, Carhart-Harris, RL & Kringelbach, ML 2024, '', Nature Mental Health.

Rodgers, N, Ti艌o, P & Johnson, S 2024, '', Journal of Physics: Complexity, vol. 5, no. 3, 035013.

Junges, L, Galvis, D, Winsor, A, Treadwell, G, Richards, C, Seri, S, Johnson, S, Terry, JR & Bagshaw, AP 2024, '', PLOS One, vol. 19, no. 8, e0309243.

Rodgers, N, Ti艌o, P & Johnson, S 2023, '', Royal Society Open Science, vol. 10, no. 8, 221380.

Rodgers, N, Tino, P & Johnson, S 2023, '', Proceedings of the National Academy of Sciences, vol. 120, no. 12, e2215752120.

Rodgers, N, Tino, P & Johnson, S 2022, '', Physical Review E, vol. 105, no. 6, 064304 , pp. 64304.

Budd, C, Calvert, K, Johnson, S & Tickle, SO 2021, '', Royal Society Open Science, vol. 8, no. 5, 210344.

Mill谩n, AP, Torres, JJ, Johnson, S & Marro, J 2021, '', Neural Networks, vol. 142, pp. 44-56.

Johnson, S 2020, '', Journal of Physics: Complexity, vol. 1, no. 1, 015003.

Mackay, RS, Johnson, S & Sansom, B 2020, '', Royal Society Open Science, vol. 7, no. 9, 201138.

Pilgrim, C, Guo, W & Johnson, S 2020, '', Scientific Reports, vol. 10, no. 1, 4388.

Botero, JD, Guo, W, Mosquera, G, Wilson, A, Johnson, S, Aguirre-Garcia, GA & Pachon, LA 2019, '', PLoS ONE, vol. 14, no. 12, e0225689.

Letter

Johnson, S 2024, '', Journal of Physics: Complexity, vol. 5, no. 1, 01LT01.

Review article

Eastwood, N, Stubbings, WA, Abdallah, MA-E, Durance, I, Paavola, J, Dallimer, M, Pantel, JH, Johnson, S, Zhou, J, Hosking, JS, Brown, J, Ullah, S, Krause, S, Hannah, D, Crawford, S, Widmann, M & Orsini, L 2022, '', Trends in Ecology & Evolution, vol. 37, no. 2, pp. 138-146.