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Software

NMSAT

Neural Microcircuit Simulation and Analysis Toolkit (NMSAT)

Tailor-made python package to build, simulate and analyse neuronal microcircuit models with PyNEST.

Image copyright: GPL, Renato Duarte, Barna Zajzon, & Abigail Morrison. (2017). Neural Microcircuit Simulation and Analysis Toolkit (0.1). Zenodo. https://doi.org/10.5281/zenodo.582645

Publication: doi:10.5281/zenodo.582645

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NESTML

NESTML

NESTML is a domain-specific language that supports the specification of neuron models in a precise and concise syntax, based on the syntax of Python. Model equations can either be given as a simple string of mathematical notation or as an algorithm written in the built-in procedural language. The equations are analyzed by the associated toolchain, written in Python, to compute an exact solution if possible or use an appropriate numeric solver otherwise.

Image copyright: CC-BY; Fig 1; Blundell I, Plotnikov D, Eppler JM and Morrison A (2018) Automatically Selecting a Suitable Integration Scheme for Systems of Differential Equations in Neuron Models. Front. Neuroinform. 12:50. doi: 10.3389/fninf.2018.00050

Publications:
doi:10.3389/fninf.2018.00050
doi:10.5281/zenodo.1319653
doi:10.5281/zenodo.1412345

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NEST simulator

NEST simulator

The Neural Simulation Tool NEST is a computer program for simulating large heterogeneous networks of point neurons or neurons with a small number of compartments. NEST is best suited for models that focus on the dynamics, size, and structure of neural systems rather than on the detailed morphological and biophysical properties of individual neurons.

Publications: doi:10.4249/scholarpedia.1430

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Elephant

Elephant (Electrophysiology Analysis Toolkit)

Elephant (Electrophysiology Analysis Toolkit) is an open-source, community centered library for the analysis of electrophysiological data in the Python programming language. The focus of Elephant is on generic analysis functions for parallel spike train data and time series recordings, such as the local field potentials (LFP) or intracellular voltages.

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NetworkUnit

NetworkUnit

NetworkUnit is a library based on SciUnit to perform model validation testing on the level of the statistics exhibited by the population dynamics of parallel spike and LFP data. Based on capabilities of a given model, it computes common statistical measures on the model and experimental data, and evaluates the level of agreement between the two based on comparing the measures.

Image copyright: CC-BY; Gutzen, R., von Papen, M., Trensch, G., Quaglio, P., Grün, S., Denker, M., 2018. Reproducible Neural Network Simulations: Statistical Methods for Model Validation on the Level of Network Activity Data. Frontiers in Neuroinformatics 12, 90. https://doi.org/10.3389/fninf.2018.00090

Publications: doi:10.3389/fninf.2018.00090

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odMLtables

odMLtables

odMLtables is a tool to support working with metadata collections for electrophysiological data. It provides a set of library functions as well as a graphical user interface that offers to swtich between hierarchical and flat (tablular) representations of their metadata collection, and provides corresponding functions that assist in working with odML and spreadsheet files.

Publications: doi:10.3389/fninf.2019.00062

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Meanfield Toolbox

Meanfield Toolbox

Using this package, you can easily calculate quantities like firing rates, power spectra, and many more, which give you a deeper and more intuitive understanding of what your network does. If your network is not behaving the way you want it to, these tools might help you to figure out, or even tell you, what you need to change in order to achieve the desired behaviour. It is easy to store (and in the future, to plot) results and reuse them for further analyses.

Image copyright: Layer, Moritz, Senk, Johanna, Essink, Simon, Korvasová, Karolína, van Meegen, Alexander, Bos, Hannah, … Helias, Moritz. (2020, February 10). LIF Meanfield Tools (Version v0.2). Zenodo. http://doi.org/10.5281/zenodo.3661413; Courtesy- Moritz Helias

Publications:
doi:10.1162/089976602320264015
arXiv:1410.8799
doi:10.1371/journal.pcbi.1005179
doi:10.1103/PhysRevE.92.052119
doi:10.1371/journal.pcbi.1005132

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Data

Massively parallel multi-electrode recordings of macaque motor cortex during an instructed delayed reach-to-grasp task

Massively parallel multi-electrode recordings of macaque motor cortex during an instructed delayed reach-to-grasp task

This publication consists of two electrophysiological datasets recorded via a 10-by-10 multi-electrode array chronically implanted in the motor cortex of two macaque monkeys during an instructed delayed reach-to-grasp task. The datasets contain the continuous measure of extracellular potentials at each electrode sampled at 30 kHz, the local field potentials sampled at 1 kHz and the timing of the online and offline extracted spike times.

Image copyright: CC-BY; Brochier, Thomas, Lyuba Zehl, Yaoyao Hao, Margaux Duret, Julia Sprenger, Michael Denker, Sonja Grün, and Alexa Riehle. 2018. “Massively Parallel Recordings in Macaque Motor Cortex during an Instructed Delayed Reach-to-Grasp Task.” Scientific Data 5: 180055. https://doi.org/10.1038/sdata.2018.55.

Publications: doi:10.1038/sdata.2018.55

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Models

Microcircuit Model

Microcircuit Model

Potjans and Diesmann (2014) describes a microcircuit model of early sensory cortex, displaying asynchronous irregular activity with layer-specific firing rates similar to the activity observed in cortex in the awake spontaneous condition. The inhibitory neurons have higher firing rates than the excitatory neurons, despite being modeled with identical intrinsic properties. Hence, this feature arises due to the connectivity of the network.

Image copyright: CC-BY; Tobias C. Potjans, Markus Diesmann, The Cell-Type Specific Cortical Microcircuit: Relating Structure and Activity in a Full-Scale Spiking Network Model, Cerebral Cortex, Volume 24, Issue 3, March 2014, Pages 785–806, https://doi.org/10.1093/cercor/bhs358

Publications: https://doi.org/10.1093/cercor/bhs358
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Multi Area Model

Multi Area Model

The multi-area model of the vision-related areas of macaque cortex uses the microcircuit model of as a prototype for all 32 areas in the FV91 parcellation and customizes it based on experimental findings on cortical structure. From anatomical studies, it is known that cortical areas in the macaque monkey are heterogeneous in their laminar structure and can be roughly categorized into 8 different architectural types based on cell densities and laminar thicknesses. This distinction was originally developed for prefrontal areas, and then extended to the entire cortex. The visual cortex, and thus the model, comprises areas of categories 2, 4, 5, 6, 7 and 8. Precise layer-specific neuron densities are available for a number of areas, while for other areas, the neuron density is estimated based on their architectural type.

Image copyright: Fig 1; Schmidt, M., Bakker, R., Hilgetag, C.C. et al. Multi-scale account of the network structure of macaque visual cortex. Brain Struct Funct 223, 1409–1435 (2018). https://doi.org/10.1007/s00429-017-1554-4; Courtesy: S. van Albada

Publications:
doi:10.1007/s00429-017-1554-4
doi:10.1371/journal.pcbi.1005179
doi:10.1371/journal.pcbi.1006359

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Reproducible Papers

Reproducing Polychronization

Reproducing Polychronization

We reproduced a seminal study on the concept of polychrony in spiking Neural Networks. In the paper we discuss barriers to repoducibility and common pitfalls in modeling and how to avoid them. The associated github repository presents a fully reproducible workflow implementing the suggested Ideas.

Image copyright: CC-BY; Fig 1; Pauli R, Weidel P, Kunkel S and Morrison A (2018) Reproducing Polychronization: A Guide to Maximizing the Reproducibility of Spiking Network Models. Front. Neuroinform. 12:46. doi: 10.3389/fninf.2018.00046

Publication: doi:10.3389/fninf.2018.00046

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ROS-MUSIC adapters

ROS-MUSIC adapters

Middleware solution that bridges the Robotic Operating System (ROS) to the Multi-Simulator Coordinator (MUSIC) enabling any robotic and neural simulators that implement the corresponding interfaces to be efficiently coupled, allowing real-time performance for a wide range of configurations.

Image copyright: CC-BY; Fig 1; Weidel P, Djurfeldt M, Duarte RC and Morrison A (2016) Closed Loop Interactions between Spiking Neural Network and Robotic Simulators Based on MUSIC and ROS. Front. Neuroinform. 10:31. doi: 10.3389/fninf.2016.00031

Publication: doi:10.3389/fninf.2016.00031

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Benchmarking neural network simulations with OpenAI Gym

Benchmarking neural network simulations with OpenAI Gym

Toolchain connecting benchmark tools from the field of machine learning and state-of-the-art neural network simulators.

Image copyright: CC-BY; Fig1; Jordan J, Weidel P and Morrison A (2019) A Closed-Loop Toolchain for Neural Network Simulations of Learning Autonomous Agents. Front. Comput. Neurosci. 13:46. doi: 10.3389/fncom.2019.00046

Publication: doi:10.3389/fncom.2019.00046

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Striatal D1/D2 in action selection

Striatal D1/D2 in action selection

We developed a striatal model consisting of D1 and D2 medium spiny neurons (MSNs) and interfaced it to a simulated robot moving in an environment.

Image copyright: CC-BY; Fig 1; Bahuguna, J., Weidel, P. and Morrison, A. (2019), Exploring the role of striatal D1 and D2 medium spiny neurons in action selection using a virtual robotic framework. Eur J Neurosci, 49: 737-753. https://doi.org/10.1111/ejn.14021

Publication: doi:10.1111/ejn.14021

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Data-driven Layer 2/3 model

Data-driven Layer 2/3 model

We study the individual and composite functional roles of heterogeneities in neuronal, synaptic and structural properties in a biophysically plausible layer 2/3 microcircuit model, built and constrained by multiple sources of empirical data.

Image copyright: CC-BY; Fig 8; Duarte R, Morrison A (2019) Leveraging heterogeneity for neural computation with fading memory in layer 2/3 cortical microcircuits. PLOS Computational Biology 15(4): e1006781. https://doi.org/10.1371/journal.pcbi.1006781

Publication: doi:10.1371/journal.pcbi.1006781

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Encoding / Decoding

Encoding / Decoding

We compare schemes for encoding symbolic input into spiking neural networks and test the ability of networks to discriminate their input as a function of the number of distinct symbols. We also compare decoding performance using different state variables and learning algorithms. Our results suggest that even this simple mapping task is strongly influenced by design choices on input encoding, state-variables, circuit characteristics and decoding methods, and these factors can interact in complex ways. This work highlights the importance of constraining computational network models of behavior by available neurobiological evidence.

Publication: doi:10.1109/IJCNN.2018.8489114

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Dynamic stability

Dynamic stability

We investigate the actions of dynamic excitatory and inhibitory synapses (STDP) and demonstrate their impact on the robustness and active maintenance of asynchronous irregular activity. Stable and compact stimulus representations are shown to result from the maintenance of this AI-type activity (achieved primarily through the action of iSTDP).

Image copyright: CC-BY; Fig 6 (L, M, N); Duarte RCF and Morrison A (2014) Dynamic stability of sequential stimulus representations in adapting neuronal networks. Front. Comput. Neurosci. 8:124. doi: 10.3389/fncom.2014.00124

Publication: doi:10.3389/fncom.2014.00124

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Reproducing "Spike Synchronization And Rate Modulation Differentially Involved In Motor Cortical Function"

Reproducing "Spike Synchronization And Rate Modulation Differentially Involved In Motor Cortical Function"

In this [Re] Science paper, the work done in Riehle et al., 1997, Science 278 (5345): 1950–53, is replicated and made available in a reproducible manner. The resulting algorithm for the Python implementation of the Unitary Events analysis method is included in the Elephant library.

Image copyright: CC-BY; Rostami, Vahid, Junji Ito, Michael Denker, and Sonja Grün. 2017. “[Re] Spike Synchronization And Rate Modulation Differentially Involved In Motor Cortical Function.” ReScience 3 (1): 3. https://doi.org/10.5281/zenodo.583814.

Publications: https://github.com/ReScience-Archives/Rostami-Ito-Denker-Gruen-2017/blob/master/article/Rostami-Ito-Denker-Gruen-2017.pdf
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Cellular and network mechanisms of slow oscillatory activity (<1 Hz) and wave propagations in  a cortical network model

Cellular and network mechanisms of slow oscillatory activity (<1 Hz) and wave propagations in  a cortical network model

Criteria on Balance, Stability, and Excitability in Cortical Networks for Constraining Computational Models. This repository provides the examination of neural contraints with experimental data.

Image copyright: CC-BY; Fig 4(a, b, c); Maksimov, A., Albada, S.J. van, and Diesmann, M. 2016. [Re] Cellular and network mechanisms of slow oscillatory activity (<1 Hz) and wave propagations in a cortical network model. ReScience 2, 1, #6

Publications: doi:10.3389/fncom.2018.00044

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Scalability of asynchronous networks

Scalability of asynchronous networks

We find that mean activities and correlation structure can be maintained by an appropriate scaling of the synaptic weights, but only over a range of numbers of synapses that is limited by the variance of external inputs to the network. Our results therefore show that the reducibility of asynchronous networks is fundamentally limited.

Image copyright: Fig 3; van Albada SJ, Helias M, Diesmann M (2015) Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and Correlations. PLOS Computational Biology 11(9): e1004490. https://doi.org/10.1371/journal.pcbi.1004490; Courtesy: S. van Albada

Publications: doi:10.1371/journal.pcbi.1004490

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NetworkUnit

Rigorous and Reproducible Neural Network Simulations

In this back-to-back paper, we discuss concepts for verification and validation in computational modeling, and introduce the NetworkUnit software to enable model validation and comparison on the level of population activity.

Image copyright: CC-BY; Gutzen, R., von Papen, M., Trensch, G., Quaglio, P., Grün, S., Denker, M., 2018. Reproducible Neural Network Simulations: Statistical Methods for Model Validation on the Level of Network Activity Data. Frontiers in Neuroinformatics 12, 90. https://doi.org/10.3389/fninf.2018.00090

Publications: https://doi.org/10.3389/fninf.2018.00090


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Statistical field theory for neural networks

Statistical Field Theory for Neural Networks

This book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks. These powerful analytical techniques, which are well established in other fields of physics, are the basis of current developments and offer solutions to pressing open problems in theoretical neuroscience and also machine learning. They enable a systematic and quantitative understanding of the dynamics in recurrent and stochastic neuronal networks.

Image copyright: Fig. 12.1; Moritz Helias, David Dahmen, Statistical Field Theory for Neural Networks, (2020) https://doi.org/10.1007/978-3-030-46444-8

Publications: https://doi.org/10.1007/978-3-030-46444-8


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Identifying Anatomical Origins of Coexisting Oscillations in the Cortical Microcircuit

Identifying Anatomical Origins of Coexisting Oscillations in the Cortical Microcircuit

We introduce a method that determines the mechanisms and sub-circuits generating oscillations in structured spiking networks. The approach exposes the influence of individual connections on frequency and amplitude of these oscillations and therefore reveals locations, where biological mechanisms controlling oscillations and experimental manipulations have the largest impact. The new analytical tool replaces parameter scans in computationally expensive models, guides circuit design, and can be employed to validate connectivity data.

Image copyright: Fig 1; Bos H, Diesmann M, Helias M (2016) Identifying Anatomical Origins of Coexisting Oscillations in the Cortical Microcircuit. PLOS Computational Biology 12(10): e1005132. https://doi.org/10.1371/journal.pcbi.1005132; Courtesy: M. Helias

Publications: https://doi.org/10.1371/journal.pcbi.1005132


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Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome

Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome

We here investigate the critical role of specific structural links between neuronal populations for the global stability of cortex and elucidate the relation between anatomical structure and experimentally observed activity. Our novel framework enables the evaluation of the rapidly growing body of connectivity data on the basis of fundamental constraints on brain activity and the combination of anatomical and physiological data to form a consistent picture of cortical networks.

Image copyright: Fig 4; Schuecker J, Schmidt M, van Albada SJ, Diesmann M, Helias M (2017) Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome. PLOS Computational Biology 13(2): e1005179. https://doi.org/10.1371/journal.pcbi.1005179; Courtesy: M. Helias

Publications: https://doi.org/10.1371/journal.pcbi.1005762


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Bistability, non-ergodicity, and inhibition in pairwise maximum-entropy models

Bistability, non-ergodicity, and inhibition in pairwise maximum-entropy models

We here show that pairwise maximum entropy models tends yield bimodal distributions if average correlations between units is positive. In the application to neuronal activity this is a problem, because the bimodality is an artefact of the statistical model and not observed in real data. This problem could also affect other fields in biology. We here explain under which conditions bimodality arises and present a solution based on introducing a collective negative feedback, corresponding to a modified maximum-entropy model. This result may point to the existence of a homeostatic mechanism active in the system that is not part of our set of observable units.

Image copyright: Fig 5; Rostami V, Porta Mana P, Grün S, Helias M (2017) Bistability, non-ergodicity, and inhibition in pairwise maximum-entropy models. PLOS Computational Biology 13(10): e1005762. https://doi.org/10.1371/journal.pcbi.1005762; Courtesy: M. Helias

Publications: https://doi.org/10.1371/journal.pcbi.1005762


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