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Computational neuroscience

Major publications

    1. H.-J. Jiang, J. Aćimović, T. Manninen, I. Ahokainen, J. Stapmanns, M. Lehtimäki, M. Diesmann, S.J. van Albada, H.E. Plesser, M.-L. Linne. Modeling neuron-astrocyte interactions in neural networks using distributed simulation. PLoS Computational Biology 21(9):e1013503, 2025. https://doi.org/10.1371/journal.pcbi.1013503

    2. M.-L. Linne. Computational modeling of neuron-glia signaling interactions to unravel cellular and neural circuit functioning. Current Opinion in Neurobiology 85:102838, 2024. https://doi.org/10.1016/j.conb.2023.102838

    3. M. Lehtimäki, L. Paunonen, M.-L. Linne. Accelerating neural ODEs using model order reduction. IEEE Transactions on Neural Networks and Learning Systems 35(1):519‒531, 2024. https://doi.org/10.1109/TNNLS.2022.3175757

    4. F. Tesler, M.-L. Linne, A. Destexhe. Modeling the relationship between neuronal activity and the BOLD signal: Contributions from astrocyte calcium dynamics. (Nature) Scientific Reports 13(1):6451, 2023. https://doi.org/10.1038/s41598-023-32618-0 .

    5. Manninen, T, Saudargiene, A, Linne, ML. Astrocyte-mediated spike-timing-dependent long-term depression modulates synaptic properties in the developing cortex. PLoS Comput Biol. 2020;16 (11):e1008360. doi: 10.1371/journal.pcbi.1008360. PubMed PMID:33170856 PubMed Central PMC7654831.

 

Updated list of publications: https://orcid.org/0000-0003-2577-7329

2026

  • Network-level restoration of impaired memory function in early Alzheimer's disease via GluN2B modulation: insights from a hippocampal CA1-CA3 model

    Memory storage and recall are essential tasks for the healthy function of the hippocampus, adversely affected by neurodegenerative diseases, such as Alzheimer's Disease (AD). Pathological levels of amyloid-β peptide (Aβ), found already in early-stage AD, can disrupt the NMDAR-dependent plasticity at hippocampal networks. Here we investigate these effects in a biophysically detailed computational model of a hippocampal CA1-CA3 network and examine potential mechanisms of functional recovery. We...

  • Biophysically inspired mean-field model of neuronal populations driven by ion-exchange mechanisms

    Whole-brain simulations are a valuable tool for gaining insight into the multiscale processes that regulate brain activity. Due to the complexity of the brain, it is impractical to include all microscopic details in a simulation. Hence, researchers often simulate the brain as a network of coupled neural masses, each described by a mean-field model. These models capture the essential features of neuronal populations while approximating most biophysical details. However, it may be important to...

  • A unified model of short- and long-term plasticity: Effects on network connectivity and information capacity

    Activity-dependent synaptic plasticity is a fundamental learning mechanism that shapes the connectivity and activity of neural circuits. Existing computational models of Spike-Timing-Dependent Plasticity (STDP) capture long-term synaptic changes with varying degrees of biological detail. A common approach is to neglect the influence of short-term dynamics on long-term plasticity, which may be an oversimplification for certain neuron types. Thus, there is a need for new models to investigate how...

  • Multimodal evidence for a mechanistic model of working memory deficits in schizophrenia

    Working memory (WM) deficits are central to schizophrenia (SCZ), yet their mechanistic basis remains unclear. We combined computational modelling with genetic, transcriptomic, behavioural, and fMRI data to construct a mechanistic account of WM impairment in SCZ. Post-mortem RNA expression from prefrontal and anterior cingulate cortex (ACC) was integrated with single-cell, network, and synaptic plasticity models to show how SCZ-related changes in ion channel-encoding and plasticity-regulating...

  • Computational Modelling of Novelty Detection in the Mismatch Negativity Protocols and Its Impairments in Schizophrenia

    The human auditory system rapidly distinguishes between novel and familiar sounds, a process reflected in mismatch negativity (MMN), an electroencephalogram (EEG)-based biomarker of auditory novelty detection. MMN is impaired in psychiatric conditions, most notably schizophrenia (SCZ), yet the neuronal mechanisms underlying this deficit remain unclear. Here, we combined computational modelling and genetic analyses to investigate how SCZ-associated cellular abnormalities affect auditory novelty...

2025

  • Biophysically inspired mean-field model of neuronal populations driven by ion-exchange mechanisms

    Whole-brain simulations are a valuable tool for gaining insight into the multiscale processes that regulate brain activity. Due to the complexity of the brain, it is impractical to include all microscopic details in a simulation. Hence, researchers often simulate the brain as a network of coupled neural masses, each described by a mean-field model. These models capture the essential features of neuronal populations while approximating most biophysical details. However, it may be important to...

  • Computational modelling of schizophrenia-associated alterations of ion-channel-encoding gene expression predicts a decrease in delta power

    Schizophrenia presents with a wide range of phenotypes that can help to understand the the mechanisms of the disease. Among these, alterations in delta oscillations are especially amenable to experimental investigation, yet the mechanisms underlying these changes remain insufficiently understood. Biophysically detailed computational modeling offers a powerful approach to investigate these phenomena, as it enables multi-scale integration of genetic and electrophysiological data. In this study, we...

  • Computational modelling of novelty detection in the mismatch negativity protocols and its impairments in schizophrenia

    The human auditory system rapidly distinguishes between novel and familiar sounds, a process reflected in mismatch negativity (MMN), an EEG-based biomarker of auditory novelty detection. MMN is impaired in psychiatric conditions, most notably schizophrenia (SCZ), yet the neuronal mechanisms underlying this deficit remain unclear. Here, we combined computational modelling and genetic analyses to investigate how SCZ-associated cellular abnormalities affect auditory novelty detection. We developed...

  • Modeling neuron-astrocyte interactions in neural networks using distributed simulation

    Astrocytes engage in local interactions with neurons, synapses, other glial cell types, and the vasculature through intricate cellular and molecular processes, playing an important role in brain information processing, plasticity, cognition, and behavior. This study advances understanding of local interactions and self-organization of neuron-astrocyte networks and contributes to the broader investigation of their potential relationship with global activity regimes and overall brain function. We...

  • Pre- and Post-synaptic Mechanisms of Neuronal Inhibition Assessed Through Biochemically Detailed Modeling of GABA<sub>B</sub> Receptor Signaling

    GABA(B) receptors (GABA(B)Rs) are an important building block in neural activity. Despite their widely hypothesized role in many basic neuronal functions and mental disorder symptomatology, there is a lack of biophysically and biochemically detailed models of these receptors and the way they mediate neuronal inhibition. Here, we developed a computational model for the activation of GABA(B)Rs and its effects on the activation of G protein-coupled inwardly rectifying potassium (GIRK) channels as...

2024

  • The coming decade of digital brain research: A vision for neuroscience at the intersection of technology and computing

    In recent years, brain research has indisputably entered a new epoch, driven by substantial methodological advances and digitally enabled data integration and modelling at multiple scales-from molecules to the whole brain. Major advances are emerging at the intersection of neuroscience with technology and computing. This new science of the brain combines high-quality research, data integration across multiple scales, a new culture of multidisciplinary large-scale collaboration, and translation...

  • Genetic mechanisms for impaired synaptic plasticity in schizophrenia revealed by computational modeling

    Schizophrenia phenotypes are suggestive of impaired cortical plasticity in the disease, but the mechanisms of these deficits are unknown. Genomic association studies have implicated a large number of genes that regulate neuromodulation and plasticity, indicating that the plasticity deficits have a genetic origin. Here, we used biochemically detailed computational modeling of postsynaptic plasticity to investigate how schizophrenia-associated genes regulate long-term potentiation (LTP) and...

  • Computational modeling of neuron-glia signaling interactions to unravel cellular and neural circuit functioning

    Glial cells have been shown to be vital for various brain functions, including homeostasis, information processing, and cognition. Over the past 30 years, various signaling interactions between neuronal and glial cells have been shown to underlie these functions. This review summarizes the interactions, particularly between neurons and astrocytes, which are types of glial cells. Some of the interactions remain controversial in part due to the nature of experimental methods and preparations used....

  • Accelerating Neural ODEs Using Model Order Reduction

    Embedding nonlinear dynamical systems into artificial neural networks is a powerful new formalism for machine learning. By parameterizing ordinary differential equations (ODEs) as neural network layers, these Neural ODEs are memory-efficient to train, process time series naturally, and incorporate knowledge of physical systems into deep learning (DL) models. However, the practical applications of Neural ODEs are limited due to long inference times because the outputs of the embedded ODE layers...

2023

  • Genetic mechanisms for impaired synaptic plasticity in schizophrenia revealed by computational modelling

    Schizophrenia phenotypes are suggestive of impaired cortical plasticity in the disease, but the mechanisms of these deficits are unknown. Genomic association studies have implicated a large number of genes that regulate neuromodulation and plasticity, indicating that the plasticity deficits have a genetic origin. Here, we used biochemically detailed computational modelling of post-synaptic plasticity to investigate how schizophrenia-associated genes regulate long-term potentiation (LTP) and...

  • Modeling the relationship between neuronal activity and the BOLD signal: contributions from astrocyte calcium dynamics

    Functional magnetic resonance imaging relies on the coupling between neuronal and vascular activity, but the mechanisms behind this coupling are still under discussion. Recent experimental evidence suggests that calcium signaling may play a significant role in neurovascular coupling. However, it is still controversial where this calcium signal is located (in neurons or elsewhere), how it operates and how relevant is its role. In this paper we introduce a biologically plausible model of the...

  • Analysis of Network Models with Neuron-Astrocyte Interactions

    Neural networks, composed of many neurons and governed by complex interactions between them, are a widely accepted formalism for modeling and exploring global dynamics and emergent properties in brain systems. In the past decades, experimental evidence of computationally relevant neuron-astrocyte interactions, as well as the astrocytic modulation of global neural dynamics, have accumulated. These findings motivated advances in computational glioscience and inspired several models integrating...

2022

  • Biophysically inspired mean-field model of neuronal populations driven by ion-exchange mechanisms

    Whole-brain simulations are a valuable tool for gaining insight into the multiscale processes that regulate brain activity. Due to the complexity of the brain, it is impractical to include all microscopic details in a simulation. Hence, researchers often simulate the brain as a network of coupled neural masses, each described by a mean-field model. These models capture the essential features of neuronal populations while approximating most biophysical details. However, it may be important to...

  • Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows

    Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at...

  • Neuron-Glia Interactions and Brain Circuits

    Recent evidence suggests that glial cells take an active role in a number of brain functions that were previously attributed solely to neurons. For example, astrocytes, one type of glial cells, have been shown to promote coordinated activation of neuronal networks, modulate sensory-evoked neuronal network activity, and influence brain state transitions during development. This reinforces the idea that astrocytes not only provide the "housekeeping" for the neurons, but that they also play a vital...

2021

  • Biophysically inspired mean-field model of neuronal populations driven by ion-exchange mechanisms

    Whole-brain simulations are a valuable tool for gaining insight into the multiscale processes that regulate brain activity. Due to the complexity of the brain, it is impractical to include all microscopic details in a simulation. Hence, researchers often simulate the brain as a network of coupled neural masses, each described by a mean-field model. These models capture the essential features of neuronal populations while approximating most biophysical details. However, it may be important to...

  • Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows

    Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at...

  • Neuron-Glia Interactions and Brain Circuits

    Recent evidence suggests that glial cells take an active role in a number of brain functions that were previously attributed solely to neurons. For example, astrocytes, one type of glial cells, have been shown to promote coordinated activation of neuronal networks, modulate sensory-evoked neuronal network activity, and influence brain state transitions during development. This reinforces the idea that astrocytes not only provide the "housekeeping" for the neurons, but that they also play a vital...

2020

  • Biophysically inspired mean-field model of neuronal populations driven by ion-exchange mechanisms

    Whole-brain simulations are a valuable tool for gaining insight into the multiscale processes that regulate brain activity. Due to the complexity of the brain, it is impractical to include all microscopic details in a simulation. Hence, researchers often simulate the brain as a network of coupled neural masses, each described by a mean-field model. These models capture the essential features of neuronal populations while approximating most biophysical details. However, it may be important to...

  • Computational Modelling of Novelty Detection in the Mismatch Negativity Protocols and Its Impairments in Schizophrenia

    The human auditory system rapidly distinguishes between novel and familiar sounds, a process reflected in mismatch negativity (MMN), an electroencephalogram (EEG)-based biomarker of auditory novelty detection. MMN is impaired in psychiatric conditions, most notably schizophrenia (SCZ), yet the neuronal mechanisms underlying this deficit remain unclear. Here, we combined computational modelling and genetic analyses to investigate how SCZ-associated cellular abnormalities affect auditory novelty...

  • Modeling neuron-astrocyte interactions in neural networks using distributed simulation

    Astrocytes engage in local interactions with neurons, synapses, other glial cell types, and the vasculature through intricate cellular and molecular processes, playing an important role in brain information processing, plasticity, cognition, and behavior. This study advances understanding of local interactions and self-organization of neuron-astrocyte networks and contributes to the broader investigation of their potential relationship with global activity regimes and overall brain function. We...

  • Astrocyte-mediated spike-timing-dependent long-term depression modulates synaptic properties in the developing cortex

    Astrocytes have been shown to modulate synaptic transmission and plasticity in specific cortical synapses, but our understanding of the underlying molecular and cellular mechanisms remains limited. Here we present a new biophysicochemical model of a somatosensory cortical layer 4 to layer 2/3 synapse to study the role of astrocytes in spike-timing-dependent long-term depression (t-LTD) in vivo. By applying the synapse model and electrophysiological data recorded from rodent somatosensory cortex,...

  • Neuroinformatics and Computational Modelling as Complementary Tools for Neurotoxicology Studies

    Neuroinformatics is an area of science that aims to integrate neuroscience data and develop modern computational tools to increase our understanding of the functions of the nervous system in health and disease. Neuroinformatics tools include, among others, databases for storing and sharing data, repositories for managing documents and source code, and software tools for analysing, modelling and simulating signals and images. This MiniReview aims to present the state of the art in...

2019

  • Unique Features of Network Bursts Emerge From the Complex Interplay of Excitatory and Inhibitory Receptors in Rat Neocortical Networks

    Spontaneous network activity plays a fundamental role in the formation of functional networks during early development. The landmark of this activity is the recurrent emergence of intensive time-limited network bursts (NBs) rapidly spreading across the entire dissociated culture in vitro. The main excitatory mediators of NBs are glutamatergic alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) and N-Methyl-D-aspartic-acid receptors (NMDARs) that express fast and slow ion...

  • Biophysical Psychiatry-How Computational Neuroscience Can Help to Understand the Complex Mechanisms of Mental Disorders

    The brain is the most complex of human organs, and the pathophysiology underlying abnormal brain function in psychiatric disorders is largely unknown. Despite the rapid development of diagnostic tools and treatments in most areas of medicine, our understanding of mental disorders and their treatment has made limited progress during the last decades. While recent advances in genetics and neuroscience have a large potential, the complexity and multidimensionality of the brain processes hinder the...