Dysfunction of the basal ganglia is implicated in a wide range of neurological and psychiatric disorders. Our understanding of the operation of the basal ganglia is largely derived on data from studies conducted on mice, which are frequently used as model organisms for various clinical conditions. The striatum, the largest compartment of the basal ganglia, consists of 90-95% striatal projection neurons (SPNs). It is therefore crucial to establish if human and mouse SPNs have distinct or similar properties, as this has implications for the relevance of mouse models for understanding the human striatum. To address this, we compared the general organization of the somato-dendritic tree of SPNs, the dimensions of the dendrites, the density and size of spines (spine surface area), and ion channel subtypes in human and mouse SPNs. Our findings reveal that human SPNs are significantly larger, but otherwise the organisation of the dendritic tree (dendrogram) with an average of approximately 5 primary dendrites, is similar in both species. Additionally in both humans and mice, over 90% of the spines are located on the terminal branches of each dendrite. Human spines are somewhat larger (4.3 versus 3.1 μm2) and the terminal dendrites have a uniform diameter in both humans and mice, although somewhat broader in the latter (1.0 versus 0.6 μm). The composition of ion channels is also largely conserved. These data have been used to simulate human SPNs building on our previous detailed simulation of mouse SPNs. We conclude that the human SPNs essentially appear as enlarged versions of the mouse SPNs. This similarity suggests that both species process information in a comparable manner, supporting the relevance of mouse models for studying the human striatum.

This in silico study predicts the impact that the single-cell neuronal morphological alterations will have on the striatal microcircuit connectivity. We find that the richness in the topological striatal motifs is significantly reduced in Parkinson's disease (PD), highlighting that just measuring the pairwise connectivity between neurons gives an incomplete description of network connectivity. Moreover, we predict how the resulting electrophysiological changes of striatal projection neuron excitability together with their reduced number of dendritic branches affect their response to the glutamatergic drive from the cortex and thalamus. We find that the effective glutamatergic drive is likely significantly increased in PD, in accordance with the hyperglutamatergic hypothesis.

Biophysically detailed multi-compartment models are powerful tools to explore computational principles of the brain and also serve as a theoretical framework to generate algorithms for artificial intelligence (AI) systems. However, the expensive computational cost severely limits the applications in both the neuroscience and AI fields. The major bottleneck during simulating detailed compartment models is the ability of a simulator to solve large systems of linear equations. Here, we present a novel Dendritic Hierarchical Scheduling (DHS) method to markedly accelerate such a process. We theoretically prove that the DHS implementation is computationally optimal and accurate. This GPU-based method performs with 2-3 orders of magnitude higher speed than that of the classic serial Hines method in the conventional CPU platform. We build a DeepDendrite framework, which integrates the DHS method and the GPU computing engine of the NEURON simulator and demonstrate applications of DeepDendrite in neuroscience tasks. We investigate how spatial patterns of spine inputs affect neuronal excitability in a detailed human pyramidal neuron model with 25,000 spines. Furthermore, we provide a brief discussion on the potential of DeepDendrite for AI, specifically highlighting its ability to enable the efficient training of biophysically detailed models in typical image classification tasks.

Purpose of the work is to research of the world science publications dynamics on the synchronization. Methods. The research methods are the statistical methods of data processing. Results. The emphasis in the study of synchronization over the past twenty years has shifted from physical and technical sciences to neuroscience with Asian countries domination.

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.

Simulation of large-scale networks of neurons is an important approach to understanding and interpreting experimental data from healthy and diseased brains. Owing to the rapid development of simulation software and the accumulation of quantitative data of different neuronal types, it is possible to predict both computational and dynamical properties of local microcircuits in a ‘bottom-up’ manner. Simulated data from these models can be compared with experiments and ‘top-down’ modelling approaches, successively bridging the scales. Here we describe an open source pipeline, using the software Snudda, for predicting microcircuit connectivity and for setting up simulations using the NEURON simulation environment in a reproducible way. We also illustrate how to further ‘curate’ data on single neuron morphologies acquired from public databases. This model building pipeline was used to set up a first version of a full-scale cellular level model of mouse dorsal striatum. Model components from that work are here used to illustrate the different steps that are needed when modelling subcortical nuclei, such as the basal ganglia.

The neuronal networks that generate locomotion are well understood in swimming animals such as the lamprey, zebrafish and tadpole. The networks controlling locomotion in tetrapods remain, however, still enigmatic with an intricate motor pattern required for the control of the entire limb during the support, lift off, and flexion phase, and most demandingly when the limb makes contact with ground again. It is clear that the inhibition that occurs between bursts in each step cycle is produced by V2b and V1 interneurons, and that a deletion of these interneurons leads to synchronous flexor–extensor bursting. The ability to generate rhythmic bursting is distributed over all segments comprising part of the central pattern generator network (CPG). It is unclear how the rhythmic bursting is generated; however, Shox2, V2a and HB9 interneurons do contribute. To deduce a possible organization of the locomotor CPG, simulations have been elaborated. The motor pattern has been simulated in considerable detail with a network composed of unit burst generators; one for each group of close synergistic muscle groups at each joint. This unit burst generator model can reproduce the complex burst pattern with a constant flexion phase and a shortened extensor phase as the speed increases. Moreover, the unit burst generator model is versatile and can generate both forward and backward locomotion.

The basal ganglia play an important role in decision making and selection of action primarily based on input from cortex, thalamus, and the dopamine system. Their main input structure, striatum, is central to this process. It consists of two types of projection neurons, together representing 95% of the neurons, and 5% of interneurons, among which are the cholinergic, fast-spiking, and low threshold-spiking subtypes. The membrane properties, somadendritic shape, and intrastriatal and extrastriatal synaptic interactions of these neurons are quite well described in the mouse, and therefore they can be simulated in sufficient detail to capture their intrinsic properties, as well as the connectivity. We focus on simulation at the striatal cellular/microcircuit level, in which the molecular/subcellular and systems levels meet. We present a nearly full-scale model of the mouse striatum using available data on synaptic connectivity, cellular morphology, and electrophysiological properties to create a microcircuit mimicking the real network. A striatal volume is populated with reconstructed neuronal morphologies with appropriate cell densities, and then we connect neurons together based on appositions between neurites as possible synapses and constrain them further with available connectivity data. Moreover, we simulate a subset of the striatum involving 10,000 neurons, with input from cortex, thalamus, and the dopamine system, as a proof of principle. Simulation at this biological scale should serve as an invaluable tool to understand the mode of operation of this complex structure. This platform will be updated with new data and expanded to simulate the entire striatum.

The basal ganglia are involved in the motivational and habitual control of motor and cognitive behaviors. Striatum, the largest basal ganglia input stage, integrates cortical and thalamic inputs in functionally segregated cortico-basal ganglia-thalamic loops, and in addition the basal ganglia output nuclei control targets in the brainstem. Striatal function depends on the balance between the direct pathway medium spiny neurons (D1-MSNs) that express D1 dopamine receptors and the indirect pathway MSNs that express D2 dopamine receptors. The striatal microstructure is also divided into striosomes and matrix compartments, based on the differential expression of several proteins. Dopaminergic afferents from the midbrain and local cholinergic interneurons play crucial roles for basal ganglia function, and striatal signaling via the striosomes in turn regulates the midbrain dopaminergic system directly and via the lateral habenula. Consequently, abnormal functions of the basal ganglia neuromodulatory system underlie many neurological and psychiatric disorders. Neuromodulation acts on multiple structural levels, ranging from the subcellular level to behavior, both in health and disease. For example, neuromodulation affects membrane excitability and controls synaptic plasticity and thus learning in the basal ganglia. However, it is not clear on what time scales these different effects are implemented. Phosphorylation of ion channels and the resulting membrane effects are typically studied over minutes while it has been shown that neuromodulation can affect behavior within a few hundred milliseconds. So how do these seemingly contradictory effects fit together? Here we first briefly review neuromodulation of the basal ganglia, with a focus on dopamine. We furthermore use biophysically detailed multi-compartmental models to integrate experimental data regarding dopaminergic effects on individual membrane conductances with the aim to explain the resulting cellular level dopaminergic effects. In particular we predict dopaminergic effects on Kv4.2 in D1-MSNs. Finally, we also explore dynamical aspects of the onset of neuromodulation effects in multi-scale computational models combining biochemical signaling cascades and multi-compartmental neuron models.

The neural control of movements in vertebrates is based on a set of modules, like the central pattern generator networks (CPGs) in the spinal cord coordinating locomotion. Sensory feedback is not required for the CPGs to generate the appropriate motor pattern and neither a detailed control from higher brain centers. Reticulospinal neurons in the brainstem activate the locomotor network, and the same neurons also convey signals from higher brain regions, such as turning/steering commands from the optic tectum (superior colliculus). A tonic increase in the background excitatory drive of the reticulospinal neurons would be sufficient to produce coordinated locomotor activity. However, in both vertebrates and invertebrates, descending systems are in addition phasically modulated because of feedback from the ongoing CPG activity. We use the lamprey as a model for investigating the role of this phasic modulation of the reticulospinal activity, because the brainstem-spinal cord networks are known down to the cellular level in this phylogenetically oldest extant vertebrate. We describe how the phasic modulation of reticulospinal activity from the spinal CPG ensures reliable steering/turning commands without the need for a very precise timing of on-or offset, by using a biophysically detailed large-scale (19,600 model neurons and 646,800 synapses) computational model of the lamprey brainstem-spinal cord network. To verify that the simulated neural network can control body movements, including turning, the spinal activity is fed to a mechanical model of lamprey swimming. The simulations also predict that, in contrast to reticulospinal neurons, tectal steering/turning command neurons should have minimal frequency adaptive properties, which has been confirmed experimentally.