Marcus Benna


We investigate open problems in computational neuroscience — especially concerning synaptic plasticity, memory consolidation, continual learning, representational geometry and efficient coding — using tools from theoretical physics, applied mathematics, machine learning and data science. We examine recordings of neural activity, develop targeted data analysis methods, and build theoretical frameworks to shed light on the mechanisms of biological computation.

The main goal of our research is to understand learning and memory under resource constraints in the biological brain, which performs highly non-trivial functions using noisy and severely limited components such as neurons and synapses. By constructing normative mathematical models of brain circuits, which can be tested on experimental data, we attempt to clarify how these limitations are overcome and uncover the underlying computational principles.

Select Publications

  • “Computational principles of synaptic memory consolidation”, M. K. Benna and S. Fusi, Nature Neuroscience 19, 1697–1706 (2016), doi:10.1038/nn.4401, PMID: 27694992 [arXiv:1507.07580 [q-bio] “Computational principles of biological memory”]
  • “Efficient online learning with low-precision synaptic variables”, M. K. Benna and S. Fusi, 51st Asilomar Conference on Signals, Systems & Computers (2017); doi: 10.1109/ACSSC.2017.8335630
  • “The geometry of abstraction in hippocampus and pre-frontal cortex’’, Silvia Bernardi, Marcus K. Benna, Mattia Rigotti, Jérôme Munuera, Stefano Fusi and C. Daniel Salzman, bioRxiv 408633 (2018); doi:
  • “Are place cells just memory cells? Memory compression leads to spatial tuning and history dependence”, Marcus K. Benna and Stefano Fusi, bioRxiv 624239 (2019); doi:


Marcus Benna studied Natural Sciences at the University of Cambridge, where he received his BA and MSci. He obtained his PhD in Physics from Princeton University, and went on to pursue postdoctoral research at the Simons Center for Geometry and Physics (Stony Brook University) and the Center for Theoretical Neuroscience (Columbia University).