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Publications of Theoretical Neuroanatomy

Selected Publications of Theoretical Neuroanatomy

A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas

A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas

Cortical activity has distinct features across scales, from the spiking statistics of individual cells to global resting-state networks. We here describe the first full-density multi-area spiking network model of cortex, using macaque visual cortex as a test system. The model represents each area by a microcircuit with area-specific architecture and features layer- and population-resolved connectivity between areas. Simulations reveal a structured asynchronous irregular ground state. In a metastable regime, the network reproduces spiking statistics from electrophysiological recordings and cortico-cortical interaction patterns in fMRI functional connectivity under resting-state conditions. Stable inter-area propagation is supported by cortico-cortical synapses that are moderately strong onto excitatory neurons and stronger onto inhibitory neurons. Schmidt M., Bakker R., Shen K., Bezgin G., Diesmann M., van Albada SJ. (2018) PLOS Comp Biol 14(10):e1006359: A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas …

Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model

Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model

The digital neuromorphic hardware SpiNNaker has been developed with the aim of enabling large-scale neural network simulations in real time and with low power consumption. Real-time performance is achieved with 1 ms integration time steps, and thus applies to neural networks for which faster time scales of the dynamics can be neglected. By slowing down the simulation, shorter integration time steps and hence faster time scales, which are often biologically relevant, can be incorporated. We here describe the first full-scale simulations of a cortical microcircuit with biological time scales on SpiNNaker. Since about half the synapses onto the neurons arise within the microcircuit, larger cortical circuits have only moderately more synapses per neuron. van Albada SJ., Rowley AG., Senk J., Hopkins M., Schmidt M., Stokes AB., Lester DR., Diesmann M., Furber SB. Frontiers in Neuroscience 12:291. (2018): Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model …

Multi-scale account of the network structure of macaque visual cortex

Multi-scale account of the network structure of macaque visual cortex

Cortical network structure has been extensively characterized at the level of local circuits and in terms of long-range connectivity, but seldom in a manner that integrates both of these scales. Furthermore, while the connectivity of cortex is known to be related to its architecture, this knowledge has not been used to derive a comprehensive cortical connectivity map. Schmidt M., Bakker R., Hilgetag CC., Diesmann M., van Albada SJ. Brain Structure and Function, 223:1-27. (2018): Multi-scale account of the network structure of macaque visual cortex …

Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome

Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome

The continuous integration of experimental data into coherent models of the brain is an increasing challenge of modern neuroscience. Such models provide a bridge between structure and activity, and identify the mechanisms giving rise to experimental observations. Nevertheless, structurally realistic network models of spiking neurons are necessarily underconstrained even if experimental data on brain connectivity are incorporated to the best of our knowledge. Guided by physiological observations, any model must therefore explore the parameter ranges within the uncertainty of the data. Schuecker J., Schmidt M., van Albada SJ., Diesmann M., Helias M. PLoS Comput Biol 13(2): e1005179 (2017): Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome …

Paper RB 20170131

The missing link: Predicting connectomes from noisy and partially observed tract tracing data

Our understanding of the wiring map of the brain, known as the connectome, has increased greatly in the last decade, mostly due to technological advancements in neuroimaging techniques and improvements in computational tools to interpret the vast amount of available data. Despite this, with the exception of the C. elegans roundworm, no definitive connectome has been established for any species. Hinne M., Meijers A., Bakker R., Tiesinga PHE., Morup M., van Gerven MAJ. PLoS Comput Biol 13(4): e1005478 (2017): The missing link: Predicting connectomes from noisy and partially observed tract tracing data …

Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and Correlations

Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and Correlations

Network models are routinely downscaled compared to nature in terms of numbers of nodes or edges because of a lack of computational resources, often without explicit mention of the limitations this entails. While reliable methods have long existed to adjust parameters such that the first-order statistics of network dynamics are conserved, here we show that limitations already arise if also second-order statistics are to be maintained. The temporal structure of pairwise averaged correlations in the activity of recurrent networks is determined by the effective population-level connectivity. van Albada SJ., Helias M., Diesmann M. PLoS Comput Biol 11:e1004490 (2015): Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and Correlations …

Paper RB 20150217

The Scalable Brain Atlas: Instant Web-Based Access to Public Brain Atlases and Related Content

The Scalable Brain Atlas (SBA) is a collection of web services that provide unified access to a large collection of brain atlas templates for different species. Its main component is an atlas viewer that displays brain atlas data as a stack of slices in which stereotaxic coordinates and brain regions can be selected. These are subsequently used to launch web queries to resources that require coordinates or region names as input. It supports plugins which run inside the viewer and respond when a new slice, coordinate or region is selected. Bakker R., Tiesinga P., Kötter R. Neuroinformatics 13(3):353-366. (2015): The Scalable Brain Atlas: Instant Web-Based Access to Public Brain Atlases and Related Content …


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