Data-driven large-scale simulations of fine-scalecortical network dynamics
1,2Tobias C Potjans, Tomoki Fukai

1,3, Markus Diesmann

1,3

1 Brain and Neural Systems Team, RIKEN Computational Science Research Program, Wako
City, Saitama, Japan
2 Institute of Neurosciences and Medicine, Research Center Juelich, Juelich, Germany
3 RIKEN Brain Science Institute, Wako City, Saitama, Japan
E-mail: tobias_potjans@brain.riken.jp
Abstract： We present a hierarchical simulation framework that is capable of capturingmultiple levels of the fine-scale circuitry of cortical networks. We apply this framework
to large-scale simulations of a layered cortical network model. In so doing we assess therelationship of the connectivity structure of the cortex to its activity.

1 Introduction

2 Hierarchical simulation framework

The local cortical network consists ofspecificallyinterconnected
neuronal
populations (see [1] for review). This
microcircuitry determines the possible
interactions between neurons and thus may
play a crucial role in shaping neuronalactivity. We investigate the dynamical
implicationsofthespecificityofconnections in the local network by meansof large-scale simulations [2] of a spiking
layered network model. To this end, wequantify the specificity of connections
measuredbydiverse
experimental
techniques and develop the simulation
framework for fine-scale cortical networks.

The simulation framework is based on ahierarchy of specificity of cortical microcircuits (see figure): neuronal connectivity
depends on the cortical layer (see fig. A)
and the type of the target neuron (see fig.
B) [3]. Further subdivision is based on thespecific projection patterns of subpopulations (see fig. C, [4]) and fine-scaleconnectivity [5]. The basic unit in our
simulation framework is the neuronal
population. Multiple populations instantiate
a cortical layer and multiple layers in turnconstitute a local cortical network. We
employ a data-driven approach to addressin particular the explosion of the number ofparameters when increasing the level of detail ofthe fine-scale circuitry: On
the one hand, the level ofdescription in the network
simulation is determined by
the available experimental
data (e.g. [5]). On the otherhand,thehierarchicalframework allows us tomodel highly specific circuits despite the limitations
and incompleteness of thedata by demanding consistency of connectivity on all hierarchical levels. This
framework has been developed in the highlevel scripting language SLI of the NEST
simulator [2] and is instantiated into
distributed network models by high-level
connection routines.

3

Application to layered
network models

cortical

We apply the hierarchical simulation
framework to layer-specific and fine-scalecortical network models. Thereby, we canexploit the framework by easily integrating
additional connectivity data into established data sets [3]. We investigate theimpact of the detailed statistics ofconnectivity on the spike train statistics byparametrizing our layered cortical networkmodel with increasing details in specificity.
The model consists of 80,000 integrateand-fire neurons and explains about 90% of
the synapses constituting the local cortical
microcircuit. We instantiate the networkmodel using the parallel simulation tool
NEST [2]. The application scales excellently
to thousands of processors on Blue Gene
supercomputers. This enables us to carryout systematic studies including synaptic
plasticity which requires simulation times
on the scale of minutes of biological time.
The network may serve as a building blockfor brain-scale simulations. As we focus on
the relationship between connectivity andnetwork activity, we use identical dynamics
and parameters for all neuron types in thenetwork. Despite this homogeneity, weobserve that the layer-specific connections
alone cause layer- and type-specific firing
rates. These rates are in excellent
agreement with experimental observations,exhibiting lowest firing in superficial layers
and highest rates in layer 5. Furthermorewe find that a small subset of targetspecific connections [3] strongly influences
the stability of network activity. Finally, theincorporation
ofprojection-specificity
enables us to investigate the influence offine-scale connectivity on global activity

patterns. We conclude that specificconnections represent a structural correlateof the experimentally observed networkdynamics.

Acknowledgements
Partially funded by Next-Generation
Supercomputer Project of MEXT, Japan, EU
Grant 15879 (FACETS), BMBF Grant
01GQ0420 to BCCN Freiburg, and theHelmholtz Alliance on Systems Biology.
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