Predicting Synaptic Connectivity for Large-Scale Microcircuit Simulations Using Snudda

Hjorth, Johannes; Kotaleski, Jeanette Hellgren; Kozlov, Alexander

doi:10.1007/s12021-021-09531-w

Cited by 5 publications

(18 citation statements)

References 58 publications

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“…The Snudda detection algorithms employ touch detection on the axons and dendrites within the network and via several pruning rules ( Materials and Methods and refs. 1 and 17 ) synaptic contacts are established according to the experimental data in Fig. 1 A .…”

Section: Resultsmentioning

confidence: 99%

“…We utilize detailed simulations of the striatal network with multicompartmental model neurons of SPNs and most of the interneuron subtypes, with validated intrinsic properties and models of GABAergic and glutamatergic synapses with validated synaptic properties. We make use of the detailed striatal simulation platform (Snudda) ( 1 , 17 – 19 ). Snudda was developed to facilitate investigations into the function and intrinsic complexity of the striatal microcircuit with its neuronal subtypes and their connection probability, morphology, and intrinsic properties as well as the extrastriatal inputs.…”

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confidence: 99%

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The roles of surround inhibition for the intrinsic function of the striatum, analyzed in silico

Frost Nylén,

Hjorth,

Kozlov

et al. 2023

Proc. Natl. Acad. Sci. U.S.A.

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The basal ganglia are important for action initiation, selection, and motor learning. The input level, the striatum, receives input preferentially from the cortex and thalamus and is to 95% composed of striatal projection neurons (SPNs) with sparse GABAergic collaterals targeting distal dendrites of neighboring SPNs, in a distance-dependent manner. The remaining 5% are GABAergic and cholinergic interneurons. Our aim here is to investigate the role of surround inhibition for the intrinsic function of the striatum. Large-scale striatal networks of 20 to 40 thousand neurons were simulated with detailed multicompartmental models of different cell types, corresponding to the size of a module of the dorsolateral striatum, like the forelimb area (mouse). The effect of surround inhibition on dendritic computation and network activity was investigated, while groups of SPNs were activated. The SPN-induced surround inhibition in distal dendrites shunted effectively the corticostriatal EPSPs. The size of dendritic plateau-like potentials within the specific dendritic segment was both reduced and enhanced by inhibition, due to the hyperpolarized membrane potential of SPNs and the reversal-potential of GABA. On a population level, the competition between two subpopulations of SPNs was found to depend on the distance between the two units, the size of each unit, the activity level in each subgroup and the dopaminergic modulation of the dSPNs and iSPNs. The SPNs provided the dominating source of inhibition within the striatum, while the fast-spiking interneuron mainly had an initial effect due to short-term synaptic plasticity as shown in with ablation of the synaptic interaction.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

The roles of surround inhibition for the intrinsic function of the striatum, analyzed in silico

Frost Nylén,

Hjorth,

Kozlov

et al. 2023

Proc. Natl. Acad. Sci. U.S.A.

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“…This process resulted in systematic decrease of the total dendritic length while not much affecting the maximum radius of dendritic area and the number of primary dendrites similar to the data in Fieblinger et al (2014). Morphological reconstructions were manipulated using Python module treem (Kozlov, A. K., 2021, Hjorth et al (2021)). The initial WT morphologies were labelled PD0.…”

Section: Methodsmentioning

confidence: 90%

The impact of Parkinson’s disease on striatal network connectivity and cortico-striatal drive: an in-silico study

Carannante,

Scolamiero,

Hjorth

et al. 2023

Preprint

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Striatum, the input stage of the basal ganglia, is important for sensory-motor integration, initiation and selection of behaviour, as well as reward learning. Striatum receives glutamatergic inputs from mainly cortex and thalamus. In rodents, the striatal projection neurons (SPNs), giving rise to the direct and the indirect pathway (dSPNs and iSPNs, respectively), account for 95% of the neurons and the remaining 5% are GABAergic and cholinergic interneurons. Interneuron axon terminals as well as local dSPN and iSPN axon collaterals form an intricate striatal network. Following chronic dopamine depletion as in Parkinson's disease (PD), both morphological and electrophysiological striatal neuronal features are altered. Our goal with this in-silico study is twofold: a) to predict and quantify how the intrastriatal network connectivity structure becomes altered as a consequence of the morphological changes reported at the single neuron level, and b) to investigate how the effective glutamate drive to the SPNs would need to be altered to account for the activity level seen in SPNs during PD. In summary we find that the richness of the connectivity motifs is significantly decreased during PD, while at the same time a substantial enhancement of the effective glutamatergic drive to striatum is present.

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“…The software is compatible with Linux operating system and supercomputer clusters (Cray XC40 system). The simulations of large networks use Snudda (Hjorth et al, 2020(Hjorth et al, , 2021, a Python package for creating data-driven networks of neurons available from its Github site 3 . To simulate neuromodulation, within the Snudda framework, additional simulation classes and associated neuromodulation subpackages were created, snudda.neuromodulation.…”

Section: Software Setupmentioning

confidence: 99%

“…Previously, Snudda , a Python package for creating data-driven networks of neurons, placing synapses using touch detection between axons and dendrites and setting up large scale simulations was developed by Hjorth et al (2021) . The software included fast synaptic transmission but neuromodulation was limited to dopamine.…”

Section: Introductionmentioning

confidence: 99%

Dopaminergic and Cholinergic Modulation of Large Scale Networks in silico Using Snudda

Nylén

Hjorth²,

Grillner³

et al. 2021

Front. Neural Circuits

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Neuromodulation is present throughout the nervous system and serves a critical role for circuit function and dynamics. The computational investigations of neuromodulation in large scale networks require supportive software platforms. Snudda is a software for the creation and simulation of large scale networks of detailed microcircuits consisting of multicompartmental neuron models. We have developed an extension to Snudda to incorporate neuromodulation in large scale simulations. The extended Snudda framework implements neuromodulation at the level of single cells incorporated into large-scale microcircuits. We also developed Neuromodcell, a software for optimizing neuromodulation in detailed multicompartmental neuron models. The software adds parameters within the models modulating the conductances of ion channels and ionotropic receptors. Bath application of neuromodulators is simulated and models which reproduce the experimentally measured effects are selected. In Snudda, we developed an extension to accommodate large scale simulations of neuromodulation. The simulator has two modes of simulation – denoted replay and adaptive. In the replay mode, transient levels of neuromodulators can be defined as a time-varying function which modulates the receptors and ion channels within the network in a cell-type specific manner. In the adaptive mode, spiking neuromodulatory neurons are connected via integrative modulating mechanisms to ion channels and receptors. Both modes of simulating neuromodulation allow for simultaneous modulation by several neuromodulators that can interact dynamically with each other. Here, we used the Neuromodcell software to simulate dopaminergic and muscarinic modulation of neurons from the striatum. We also demonstrate how to simulate different neuromodulatory states with dopamine and acetylcholine using Snudda. All software is freely available on Github, including tutorials on Neuromodcell and Snudda-neuromodulation.

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Predicting Synaptic Connectivity for Large-Scale Microcircuit Simulations Using Snudda

Cited by 5 publications

References 58 publications

The roles of surround inhibition for the intrinsic function of the striatum, analyzed in silico

The roles of surround inhibition for the intrinsic function of the striatum, analyzed in silico

The impact of Parkinson’s disease on striatal network connectivity and cortico-striatal drive: an in-silico study

Dopaminergic and Cholinergic Modulation of Large Scale Networks in silico Using Snudda

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