Powered by Indico
v3.2.2
Computational neuroscience is entering a new era. This originates from the convergence of
two developments: First, biological knowledge has expanded, enabling the construction of
anatomically detailed models of one or multiple brain areas. The models are formulated at the
resolution of individual nerve cells (neurons), represent the respective part of the brain with its
natural number of neurons, and are multi-scale. Next to the spiking activity of neurons, also
mesoscopic signals like the local field potential (LFP) and fMRI signals can be generated (e.g.
[1]). Second, with the completion of the European Human Brain Project (HBP), simulation has
firmly established itself in neuroscience as a third pillar alongside experiment and theory. A
conceptual separation has been achieved between concrete network models and generic
simulation engines [2,3]. Many different models can be simulated with the same engine, such
that these codes can continuously be optimized [4] and operated as an infrastructure. Network
models with millions of neurons can routinely be investigated (e.g. [4]).
Neuroscientists can now work with digital twins of certain brain structures to test their ideas
on brain functions and probe the validity of approximations required for analytical approaches.
However, the efficient use of this capability also requires a change in mindset. Computational
neuroscience seems stuck at a certain level of model complexity for the last decade not only
because anatomical data were missing or because of a lack of simulation technology. The
fascination of the field with minimal models leads to explanations for individual mechanisms,
but the reduction to the bare equations required provides researchers with few contact points
to build on these works and construct larger systems with a wider explanatory scope. In
addition, creating large-scale models goes beyond the period of an individual PhD project, but
an exclusive focus on hypothesis-driven research may prevent such sustained constructive
work. Possibly, researchers may also just be missing the digital workflows to reuse large-scale
models and extend them reproducibly. The change of perspective required is to view digital
twins as research platforms and scientific software as infrastructure.
As a concrete example, the presentation discusses how the universality of mammalian brain
structures motivates the construction of large-scale models and demonstrates how digital
workflows help to reproduce results and increase the confidence in such models.
A digital twin promotes neuroscientific investigations, but can also serve as a benchmark for
technology. The energy consumption of present AI systems is unsustainable and
undemocratic. Understanding the energy efficiency of the brain may uncover pathways out of
the dilemma. The talk shows how a model of the cortical microcircuit has become a de facto
standard for neuromorphic computing [5] and has sparked a constructive race in the
community for ever larger computation speed and lower energy consumption.
[1] Senk J, Hagen E, van Albada SJ, Diesmann M (2018) Reconciliation of weak pairwise
spike-train correlations and highly coherent local field potentials across space.
arXiv:1805.10235 [q-bio.NC]
[2] Einevoll GT, Destexhe A, Diesmann M, Grün S, Jirsa V, de Kamps M, Migliore M, Ness
TV, Plesser HE, Schürmann F (2019) The Scientific Case for Brain Simulations. Neuron
102:735-744
[3] Senk J, Kriener B, Djurfeldt M, Voges N, Jiang HJ, Schüttler L, Gramelsberger G,
Diesmann M, Plesser HE, van Albada SJ (2022) Connectivity concepts in neuronal
network modeling. PLOS Comput Biol 18(9):e1010086
[4] Tiddia G, Golosio B, Albers J, Senk J, Simula F, Pronold J, Fanti V, Pastorelli E, Paolucci
PS, van Albada SJ (2022) Fast Simulation of a Multi-Area Spiking Network Model of
Macaque Cortex on an MPI-GPU Cluster. Front Neuroinform 16:883333
[5] Kurth AC, Senk J, Terhorst D, Finnerty J, Diesmann M (2022) Sub-realtime simulation of
a neuronal network of natural density. Neuromorphic Computing and Engineering
2:021001
keywords: simulation as third pillar, software as infrastructure, universality of cortex, cellular-
resolution cortical microcircuit, multi-area model, neuromorphic computing
Zoom Meeting details:
https://zoom.us/j/91561735697?pwd=Umw1S2ozMGFmTXlWNW1oY3BML29rUT09
Meeting ID: 915 6173 5697
Passcode: 228621