Institute for Advanced Simulation (IAS)

Jülich Supercomputing Centre (JSC)

Structural Plasticity

Structural plasticity in a neural network refers to the physical creation and deletion of synapses. This effect is present during brain development [1], learning, and healing after lesions.

A model of structural plasticity has been proposed in [2]. In this model, neurons have contact points called synaptic elements, which can be used to represent axonal boutons and dendritic spines. These elements grow and recede following homeostatic rules based on the mean electrical activity of the neuron.

Intracellular calcium concentration [Ca2+] is used to indirectly measure the mean electrical activity of the neuron, since it increases when neurons spike, and otherwise decreases over time.

When synaptic elements are available, they can randomly create new synapses with other compatible elements. If they recede, the corresponding synapse is removed. This model has been implemented and made available on the NEST simulator by Dr Sandra Diaz [3]. In this implementation, global connectivity is updated in the network on a much slower timescale than changes in electrical activity.

The algorithm consists of three repeating parts:

Calcium concentration in neurons is updated based on
electrical activity;
The number of synaptic elements is updated;
Network connectivity is updated.

Using this algorithm, the network is constantly rewired until a stable configuration of connections leads to the desired average electrical activity. Using simple homeostatic rules, this algorithm can also be used to fill information gaps in networ

Fig.1: Changes in calcium concentration related to the number of synapses created between two populations, one excitatory and one inhibitory.

Our contribution

We have implemented the model of structural plasticity described in [2] as part of the neural network simulatorNEST as detailed in [4]. This framework is included in release version 2.10.0 [5].
We have measured the scalability of the framework. As an example, Fig. 2 shows the execution times of a strong scaling test of a network with 100,000 neurons performed on different numbers of computation nodes. Following the NEST philosophy, simulations using structural plasticity can be performed at a small scale on personal computers and at a large scale on supercom

Fig. 2: Execution times for a network with 100,000 neurons which was simulated on 1 – 16 nodes in the JuRoPA supercomputer from JSC.

We maintain and give support to users of this framework.
We use the model to study the automatic creation of connectivity of large-scale neural networks. We also use it to study the relationships between structural and functional connectivity in the Virtual Connectome (link) project.

Future work

We are now in the process of implementing the homeostatic strurtual plasticity rule in Arbor simulator to enable structural plasticity in network of cable cells with detailed morphology.

[1] Hensch, T. K. (2005).Critical period plasticity in local cortical circuits.Nat. Rev. Neurosci. 6 (11), 877888.

[2] Butz, M., & van Ooyen, A. (2013).A simple rule for dendritic spine and axonal bouton formation can account for cortical reorganization after focal retinal lesions.PLoS Comput. Biol. 9 (10), e1003259.

[3] Diaz Pier S, Naveau M, Butz-Ostendorf M, Morrison A.Automatic generation of connectivity for large-scale neuronal network models through structural plasticity.Frontiers in Neuroanatomy. 2016;10:57.

[4] Butz, M., Steenbuck, I. D., & van Ooyen, A. (2014).Homeostatic structural plasticity increases the eefficiency of small-world networks.Front Synaptic Neurosci 6, 7.

[5] Bos, H., Morrison, A., Peyser, A., Hahne, J., Helias, M., Kunkel, S., Ippen, T., et al. 2015.NEST 2.10.0.Zenodo. doi:10.5281/zenodo.44222.

[6] Manos T, Diaz Pier S, Tass PA. Long-term desynchronization by coordinated reset stimulation in a neural network model with synaptic and structural plasticity

[7] Lu H, Diaz Pier S, Lenz M, Vlachos A. Interplay between homeostatic synaptic scal