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New Insights into Computational Neuroscience
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New Insights into Computational Neuroscience

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Neuroscience and Neural Engineering".

Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 13123

Special Issue Editor

Dr. Giulia De Bonis

E-Mail Website
Guest Editor
Istituto Nazionale di Fisica Nucleare (INFN), 00185 Rome, Italy
Interests: neuroscience; brain dynamics; brain states; data analysis; brain models; computing; simulations; algorythms

Special Issue Information

Dear Colleagues,

This Special Issue is devoted to the illustration of new research in the field of Computational Neuroscience, focusing on its multidisciplinary aspects that gather expertise in physics and mathematics as well as biology, chemistry, engineering, and computer science.

We believe that leveraging the combination of different approaches for the investigation of the brain, its structure, and its cognitive functions represents a richness that Computational Neuroscience should exploit. Therefore, we anticipate contributions that aim at the understanding of brain physiology and functionalities through the development of analysis methods, algorithms, and mathematical models, whilst linking different scales and building a theoretical framework that takes into account the latest experimental findings, fostering interactions between theoretical and experimental neuroscience.

A related workshop, BASSES (Brain Activity across Scales and Species: analysis of Experiments and Simulations) will be held in Rome, Italy, on 13th-15th June 2022, in a hybrid format, with lectures, hands-on sessions and call for contributions. Further details can be accessed on the workshop web page, https://www.humanbrainproject.eu/en/education/ebrains-workshops/basses/.

Dr. Giulia De Bonis
Guest Editor

Manuscript Submission Information

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Published Papers (6 papers)

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21 pages, 2002 KiB  
Article
Vast Parameter Space Exploration of the Virtual Brain: A Modular Framework for Accelerating the Multi-Scale Simulation of Human Brain Dynamics
by Michiel van der Vlag, Lionel Kusch, Alain Destexhe, Viktor Jirsa, Sandra Diaz-Pier and Jennifer S. Goldman
Appl. Sci. 2024, 14(5), 2211; https://doi.org/10.3390/app14052211 - 6 Mar 2024
Viewed by 1387
Abstract
Global neural dynamics emerge from multi-scale brain structures, with nodes dynamically communicating to form transient ensembles that may represent neural information. Neural activity can be measured empirically at scales spanning proteins and subcellular domains to neuronal assemblies or whole-brain networks connected through tracts, [...] Read more.
Global neural dynamics emerge from multi-scale brain structures, with nodes dynamically communicating to form transient ensembles that may represent neural information. Neural activity can be measured empirically at scales spanning proteins and subcellular domains to neuronal assemblies or whole-brain networks connected through tracts, but it has remained challenging to bridge knowledge between empirically tractable scales. Multi-scale models of brain function have begun to directly link the emergence of global brain dynamics in conscious and unconscious brain states with microscopic changes at the level of cells. In particular, adaptive exponential integrate-and-fire (AdEx) mean-field models representing statistical properties of local populations of neurons have been connected following human tractography data to represent multi-scale neural phenomena in simulations using The Virtual Brain (TVB). While mean-field models can be run on personal computers for short simulations, or in parallel on high-performance computing (HPC) architectures for longer simulations and parameter scans, the computational burden remains red heavy and vast areas of the parameter space remain unexplored. In this work, we report that our HPC framework, a modular set of methods used here to implement the TVB-AdEx model for the graphics processing unit (GPU) and analyze emergent dynamics, notably accelerates simulations and substantially reduces computational resource requirements. The framework preserves the stability and robustness of the TVB-AdEx model, thus facilitating a finer-resolution exploration of vast parameter spaces as well as longer simulations that were previously near impossible to perform. Comparing our GPU implementations of the TVB-AdEx framework with previous implementations using central processing units (CPUs), we first show correspondence of the resulting simulated time-series data from GPU and CPU instantiations. Next, the similarity of parameter combinations, giving rise to patterns of functional connectivity, between brain regions is demonstrated. By varying global coupling together with spike-frequency adaptation, we next replicate previous results indicating inter-dependence of these parameters in inducing transitions between dynamics associated with conscious and unconscious brain states. Upon further exploring parameter space, we report a nonlinear interplay between the spike-frequency adaptation and subthreshold adaptation, as well as previously unappreciated interactions between the global coupling, adaptation, and propagation velocity of action potentials along the human connectome. Given that simulation and analysis toolkits are made public as open-source packages, this framework serves as a template onto which other models can be easily scripted. Further, personalized data-sets can be used for for the creation of red virtual brain twins toward facilitating more precise approaches to the study of epilepsy, sleep, anesthesia, and disorders of consciousness. These results thus represent potentially impactful, publicly available methods for simulating and analyzing human brain states. Full article
(This article belongs to the Special Issue New Insights into Computational Neuroscience)
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13 pages, 3669 KiB  
Article
Asynchronous and Slow-Wave Oscillatory States in Connectome-Based Models of Mouse, Monkey and Human Cerebral Cortex
by Maria Sacha, Jennifer S. Goldman, Lionel Kusch and Alain Destexhe
Appl. Sci. 2024, 14(3), 1063; https://doi.org/10.3390/app14031063 - 26 Jan 2024
Cited by 5 | Viewed by 1216
Abstract
Thanks to the availability of connectome data that map connectivity between multiple brain areas, it is now possible to build models of whole-brain activity. At the same time, advances in mean-field techniques have led to biologically based population models that integrate biophysical features [...] Read more.
Thanks to the availability of connectome data that map connectivity between multiple brain areas, it is now possible to build models of whole-brain activity. At the same time, advances in mean-field techniques have led to biologically based population models that integrate biophysical features such as membrane conductances or synaptic conductances. In this paper, we show that this approach can be used in brain-wide models of mice, macaques, and humans.We illustrate this approach by showing the transition from wakefulness to sleep, simulated using multi-scale models, in the three species. We compare the level of synchrony between the three species and find that the mouse brain displays a higher overall synchrony of slow waves compared to monkey and human brains. We show that these differences are due to the different delays in axonal signal propagation between regions associated with brain size differences between the species. We also make the program code—which provides a set of open-source tools for simulating large-scale activity in the cerebral cortex of mice, monkeys, and humans—publicly available. Full article
(This article belongs to the Special Issue New Insights into Computational Neuroscience)
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23 pages, 3678 KiB  
Article
Investigating the Impact of Local Manipulations on Spontaneous and Evoked Brain Complexity Indices: A Large-Scale Computational Model
by Gianluca Gaglioti, Thierry Ralph Nieus, Marcello Massimini and Simone Sarasso
Appl. Sci. 2024, 14(2), 890; https://doi.org/10.3390/app14020890 - 20 Jan 2024
Cited by 1 | Viewed by 1675
Abstract
Brain complexity relies on the integrity of structural and functional brain networks, where specialized areas synergistically cooperate on a large scale. Local alterations within these areas can lead to widespread consequences, leading to a reduction in overall network complexity. Investigating the mechanisms governing [...] Read more.
Brain complexity relies on the integrity of structural and functional brain networks, where specialized areas synergistically cooperate on a large scale. Local alterations within these areas can lead to widespread consequences, leading to a reduction in overall network complexity. Investigating the mechanisms governing this occurrence and exploring potential compensatory interventions is a pressing research focus. In this study, we employed a whole-brain in silico model to simulate the large-scale impact of local node alterations. These were assessed by network complexity metrics derived from both the model’s spontaneous activity (i.e., Lempel–Ziv complexity (LZc)) and its responses to simulated local perturbations (i.e., the Perturbational Complexity Index (PCI)). Compared to LZc, local node silencing of distinct brain regions induced large-scale alterations that were paralleled by a systematic drop of PCI. Specifically, while the intact model engaged in complex interactions closely resembling those obtained in empirical studies, it displayed reduced PCI values across all local manipulations. This approach also revealed the heterogeneous impact of different local manipulations on network alterations, emphasizing the importance of posterior hubs in sustaining brain complexity. This work marks an initial stride toward a comprehensive exploration of the mechanisms underlying the loss and recovery of brain complexity across different conditions. Full article
(This article belongs to the Special Issue New Insights into Computational Neuroscience)
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11 pages, 1478 KiB  
Article
Cortical Neurons Adjust the Action Potential Onset Features as a Function of Stimulus Type
by Ahmed A. Aldohbeyb and Ahmad O. Alokaily
Appl. Sci. 2023, 13(18), 10158; https://doi.org/10.3390/app131810158 - 9 Sep 2023
Viewed by 1476
Abstract
Pyramidal neurons and interneurons play critical roles in regulating the neuronal activities in the mammalian cortex, where they exhibit different firing patterns. Pyramidal neurons mainly exhibit regular-spiking firing patterns, while interneurons have fast-spiking firing patterns. Cortical neurons have distinct action potential onset dynamics, [...] Read more.
Pyramidal neurons and interneurons play critical roles in regulating the neuronal activities in the mammalian cortex, where they exhibit different firing patterns. Pyramidal neurons mainly exhibit regular-spiking firing patterns, while interneurons have fast-spiking firing patterns. Cortical neurons have distinct action potential onset dynamics, in which the evoked action potential is rapid and highly variable. However, it is still unclear how cortical regular-spiking and fast-spiking neurons discriminate between different types of stimuli by changing their action potential onset parameters. Thus, we used intracellular recordings of regular-spiking and fast-spiking neurons, taken from layer 2/3 in the somatosensory cortex of adult mice, to investigate changes in the action potential waveform in response to two distinct stimulation protocols: the conventional step-and-hold and frozen noise. The results show that the frozen noise stimulation paradigm evoked more rapid action potential with lower threshold potential in both neuron types. Nevertheless, the difference in the action potential rapidity in response to different stimuli was significant in regular-spiking pyramidal neurons while insignificant in fast-spiking interneurons. Furthermore, the threshold variation was significantly higher for regular-spiking neurons than for fast-spiking neurons. Our findings demonstrate that different types of cortical neurons exhibit various onset dynamics of the action potentials, implying that different mechanisms govern the initiation of action potentials across cortical neuron subtypes. Full article
(This article belongs to the Special Issue New Insights into Computational Neuroscience)
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27 pages, 1422 KiB  
Article
Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices
by Bruno Golosio, Jose Villamar, Gianmarco Tiddia, Elena Pastorelli, Jonas Stapmanns, Viviana Fanti, Pier Stanislao Paolucci, Abigail Morrison and Johanna Senk
Appl. Sci. 2023, 13(17), 9598; https://doi.org/10.3390/app13179598 - 24 Aug 2023
Cited by 1 | Viewed by 1664
Abstract
Simulation speed matters for neuroscientific research: this includes not only how quickly the simulated model time of a large-scale spiking neuronal network progresses but also how long it takes to instantiate the network model in computer memory. On the hardware side, acceleration via [...] Read more.
Simulation speed matters for neuroscientific research: this includes not only how quickly the simulated model time of a large-scale spiking neuronal network progresses but also how long it takes to instantiate the network model in computer memory. On the hardware side, acceleration via highly parallel GPUs is being increasingly utilized. On the software side, code generation approaches ensure highly optimized code at the expense of repeated code regeneration and recompilation after modifications to the network model. Aiming for a greater flexibility with respect to iterative model changes, here we propose a new method for creating network connections interactively, dynamically, and directly in GPU memory through a set of commonly used high-level connection rules. We validate the simulation performance with both consumer and data center GPUs on two neuroscientifically relevant models: a cortical microcircuit of about 77,000 leaky-integrate-and-fire neuron models and 300 million static synapses, and a two-population network recurrently connected using a variety of connection rules. With our proposed ad hoc network instantiation, both network construction and simulation times are comparable or shorter than those obtained with other state-of-the-art simulation technologies while still meeting the flexibility demands of explorative network modeling. Full article
(This article belongs to the Special Issue New Insights into Computational Neuroscience)
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10 pages, 376 KiB  
Protocol
Identifying Neurobiological Markers in Obsessive–Compulsive Disorder: A Study Protocol for a Cross-Sectional Study in Subgroups of Differing Phenotype
by Pasquale Paribello, Bernardo Carpiniello, Roberto Murgia, Antonio Andrea Porcheddu, Sabrina El-Kacemi, Marco Pinna, Martina Contu, Giulia Costa, Rossella Barbarossa, Egea Sanna, Sara Carucci, Alessandro Zuddas, Paola Fadda, Simona Dedoni, Carlotta Siddi, Patrizia Congiu, Michela Figorilli, Michela Fanzecco, Monica Puligheddu, Antonella Gagliano, Federica Pinna, Maria Scherma and Mirko Manchiaadd Show full author list remove Hide full author list
Appl. Sci. 2023, 13(12), 7306; https://doi.org/10.3390/app13127306 - 19 Jun 2023
Viewed by 2206
Abstract
Obsessive–compulsive disorder (OCD) represents a frequent and highly disabling mental disorder. Past attempts to characterize different disease subgroups focused on the time of onset (late vs. early onset), presence of insight (poor insight), and post-infectious forms (pediatric acute-onset neuropsychiatric syndrome, PANS). Each subgroup [...] Read more.
Obsessive–compulsive disorder (OCD) represents a frequent and highly disabling mental disorder. Past attempts to characterize different disease subgroups focused on the time of onset (late vs. early onset), presence of insight (poor insight), and post-infectious forms (pediatric acute-onset neuropsychiatric syndrome, PANS). Each subgroup may be associated with a differing impact on cognition, functioning, sleep quality, and treatment response profile. Certain lines of evidence suggest brain-derived neurotrophic factor (BDNF) levels may differ between individuals living with OCD as compared with controls, but there is a lack of evidence on the variation of BDNF levels in OCD subgroups. Lastly, the potential of assessing inflammatory states, electroencephalogram, and polysomnography to characterize these subtypes has been hardly explored. Estimates of drug-resistance rates indicate that 20% and up to 65% of affected adults and up to 35% of the pediatric population may not benefit from pharmacological treatments. At least part of the variability in treatment response could depend on the underlying biological heterogeneity. In the present project, we aim to increase the accuracy in characterizing the phenotypical and biological signature for the different OCD subtypes through clinical, cognitive, and sleep markers, along with other possible markers that may be biologically plausible. Full article
(This article belongs to the Special Issue New Insights into Computational Neuroscience)
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