Calling all connectomes

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Grant Title: Application of the principle of symmetry to neural circuitry:
From building blocks to neural synchronization in the connectome

By: Hernan Makse and Manuel Zimmer.

1. Our analytical tool:
We have developed a network theoretical toolbox to extract the
symmetries of the connectome. The symmetries are graph automorphisms
or symmetry permutations, i.e. specific similarities in the
connectivity patterns of the connectome, that predict synchronization
of neural populations. The theory successfully predicted functional
building blocks in the C. elegans connectome, like circuits governing
locomotion (see:

2. Questions to answer:
Our central hypothesis to test is if the symmetries in connectivity
underly the synchronization of neuronal population activity, and
therefore can be used to discover functional units within complex
connectome data. Our graph theoretical toolbox will classify the
symmetries of all connectomes thereby identifying neural circuits that
potentially form functional building blocks. Using our symmetry
finder, we aim at predicting which neurons synchronize their activity
and then to further test and investigate these structure-function
relations by (I) measuring neuronal activity and (II) manipulating the
underlying circuits experimentally.

3. What input do you need?
We are calling all connectomes. The archetypical example is the
complete reconstruction of the C. elegans connectome. Partial
reconstructions with similar level of resolution at the neuron-level
and full connectivity are also needed.

4. Specifications:

4.1. Anatomical data: Connectomes could include larval zebrafish,
larval annelid Platynereis, partial reconstructions of the drosophila
adult and larval brain (e.g. visual system or mushroom body) or
partial reconstructions of rodent brains.

4.2. Dynamical data: Alongside these anatomical data, we look for
dynamical single-cell resolution neuronal activity data that can be
acquired from these models, e.g. population wide calcium imaging data
and multi-unit electrophysiological recordings.

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