Marja-Leena Linne

• Head of Department at Tampere University

GABAB receptors (GABABRs) 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 computation...

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....

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....

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 aims to enhance computational modeling of local interactions between neurons and as...

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 detaile...

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 co...

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 detaile...

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 l...

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 o...

Brain research has in recent years indisputably entered a new epoch, driven by substantial methodological advances and digitally enabled data integration and modeling at multiple scales – from molecules to the whole system. Major advances are emerging at the intersection of neuroscience with technology and computing. This new science of the brain i...

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 mo...

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 (D...

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 transit...

Foreword from the editors. We hosted four keynote speakers: Wolf Singer, Bill Bialek, Danielle Bassett, and Sonja Gruen. They enlightened us about computations in the cerebral cortex, the reduction of high-dimensional data, the emerging field of computational psychiatry, and the significance of spike patterns in motor cortex. From the submissions,...

Spontaneous network bursts, the intervals of intense network-wide activity interleaved with longer periods of sparse activity, are a hallmark phenomenon observed in cortical networks at postnatal developmental stages. Generation, propagation and termination of network bursts depend on a combination of synaptic, cellular and network mechanisms; howe...

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 mod...

Functional magnetic resonance imaging (fMRI) 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 sign...

Most biological brains, as well as artificial neural networks, are capable of performing multiple tasks [1]. The mechanisms through which simultaneous tasks are performed by the same set of units are not yet entirely clear. Such systems can be modular or mixed selective through some variables such as sensory stimulus [2,3]. Based on simple tasks st...

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-...

Mathematical modeling of biological neuronal networks is important in order to increase understanding of the brain and develop systems capable of brain-like learning. While mathematical analysis of these comprehensive, stochastic, and complex models is intractable, and their numerical simulation is very resource intensive, mean-field modeling is an...

In this study mathematical model order reduction is applied to a nonlinear model of a network of biophysically realistic heterogeneous neurons. The neuron model describes a pyramidal cell in the hippocampal CA3 area of the brain and includes a state-triggered jump condition. The network displays synchronized firing of action potentials (spikes), a...

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...

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...

The current trend in computational neuroscience is to incorporate multiple physical levels of the brain into mathematical models. Such comprehensive models with accurate system dynamics are necessary in order to increase understanding of different mechanisms in the brain. Mathematical analysis of these models is intractable, hence numerical method...

Multi-scale models in neuroscience typically integrate detailed biophysical and neurobiological phenomena from molecular level up to network and system levels. These models are very challenging to simulate. Model Order Reduction (MOR) is an established method in engineering sciences, such as control theory, for improving computational efficiency of...

Astrocytes have been shown to participate in a variety of brain functions. These include homeostasis, metabolism, neuronal survival in pathological circumstances, and neurovascular coupling. Since astrocytes extend their processes into close proximity to synapses, it has also been proposed that they take active roles in synaptic transmission, learn...

Multi-scale models in neuroscience typically integrate detailed biophysical and neurobiological phenomena from molecular level up to network and system levels. These models are very challenging to simulate. Model Order Reduction (MOR) is an established method in engineering sciences, such as control theory, for improving computational efficiency of...

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,...

Astrocytes are active participants in brain physiology and a known target of pathological processes of several diseases. Using a mathematical model of a tripartite synapse, we investigated the effects of astrocyte intracellular \(\beta \)-amyloid 1-42 fragments on astrocyte Ca²⁺ signaling and synaptic signal transmission. Our results show that with...

The viability and morphological differentiation of the neuronal cells are often enhanced by attachment on surface coating proteins or polymers. Laminin is a basal membrane protein and one of the matrix components in the nervous system. Polyethyleneimine is a positively charged polymer widely used for improving attachment of cell cultures. The aim o...

The possibility to replicate and reproduce published research results is one of the biggest challenges in all areas of science. In computational neuroscience, there are thousands of models available. However, it is rarely possible to reimplement the models based on the information in the original publication, let alone rerun the models just because...

The computational neuroscience field has heavily concentrated on the modeling of neuronal functions, largely ignoring other brain cells, including one type of glial cell, the astrocytes. Despite the short history of modeling astrocytic functions, we were delighted about the hundreds of models developed so far to study the role of astrocytes, most o...

Abstract of poster presentation at OCNS-2017

Spiking neural networks represent a third generation of artificial neural networks and are inspired by computational principles of neurons and synapses in the brain. In addition to neuronal mechanisms, astrocytic signaling can influence information transmission, plasticity and learning in the brain. In this study, we developed a new computational m...

In this study a nonlinear mathematical model of plasticity in the brain is reduced using the Proper Orthogonal Decomposition and Discrete Empirical Interpolation Method. Such methods are remarkably useful for connecting reduced small scale models via the inputs and outputs to form optimally performing large scale models. Novel results were obtained...

The scientific community across all disciplines faces the same challenges of ensuring accessibility, reproducibility, and efficient comparability of scientific results. Computational neuroscience is a rapidly developing field, where reproducibility and comparability of research results have gained increasing interest over the past years. As the num...

Human SH-SY5Y neuroblastoma cells maintain their potential for differentiation and regression in culture conditions. The induction of differentiation could serve as a strategy to inhibit cell proliferation and tumor growth. Previous studies have shown that differentiation of SH-SY5Y cells can be induced by all-trans-retinoic-acid (RA) and cholester...

There is increasing evidence that astrocytes not only interact with each other but also with adjacent neurons in the neural circuitry in a variety of brain areas. Many biophysical and biochemical mechanisms have been proposed to explain these interactions in vitro. Based on the experimental literature, the mechanisms involved seem to depend not onl...

We addressed the principles of micro-level organization of neuronal circuits and explored how the neuronal morphology constrains this organization. Several studies have demonstrated the non-trivial properties of the network connectivity using in vitro recordings from multiple neurons [1-3], yet it is unclear to what extent this structure reflects r...

Stochastic modeling plays an essential role in the study of noise and random fluctuations in biochemical signaling of neural systems. Here, we study numerically noisy fluctuations produced by two different stochastic differential equation models, the chemical Langevin equation (CLE) model [1] and the rate constant stochastic differential equation (...

We developed a two-level statistical model that addresses the question of how properties of neurite morphology shape the large-scale network connectivity. We adopted a low-dimensional statistical description of neurites. From the neurite model description we derived the expected number of synapses, node degree, and the effective radius, the maximal...

In this chapter, we review the principal astrocyte functions and the interactions between neurons and astrocytes. We then address how the experimentally observed functions have been verified in computational models and review recent experimental literature on astrocyte-neuron interactions. Benefits of computational neuroscience work are highlighted...

Inositol 1,4,5-trisphosphate receptor (IP3R) is a ubiquitous intracellular calcium (Ca2+) channel which has a major role in controlling Ca2+ levels in neurons. A variety of computational models have been developed to describe the kinetic function of IP3R under different conditions. In the field of computational neuroscience, it is of great interest...

The question of how the structure of a neuronal network affects its functionality has gained a lot of attention in neuroscience. However, the vast majority of the studies on structure-dynamics relationships consider few types of network structures and assess limited numbers of structural measures. In this in silico study, we employ a wide diversity...

CC in FF networks as a function of parameter , and, for comparison, the respective values of RN, LCN1, LCN2 and NETM networks. By the increase of the CC of FF networks approaches that of the extreme FF networks, yet remains lower than that in LCN1, LCN2 or even NM. Different colors represent networks with different (binomial) in-degree distribution...

Number (mean and std, ) of FF-motifs in FF networks as a function of parameter , and, for comparison, the respective values of RN, LCN1 and NETM networks. The in-degree distribution of FF, RN and LCN1 networks is chosen as binomial with the shown average connectivities. (EPS)

In-degree distributions for NM networks with connection probabilities (orange), (purple), and (blue). The dashed lines show the binomial PDFs with these connection probabilities, and the legend shows the KL-divergence of the NM in-degree distributions from these binomial distributions. The obtained values of are considerably small – the correspondi...

Statistics (mean and std) on the activity properties of different network classes. For the network classes that allow the use of the strength parameter (WS1, WS2, FF, L2, L3, L4 and L6), only the statistics for the extreme networks () shown. (EPS)

Supporting Information. (PDF)

Distribution of eigenvalues of L2, L3, L4 and L6 networks with parameter and . Different colors represent networks with different binomial in-degree distributions, average connectivities chosen as (orange), (purple), and (blue). Each plot shows the combined spectra of networks. The corresponding spectra for RN, LCN1, LCN2 and NETMORPH networks are...

CC is most the determinant graph property in large networks with binomial in-degree, while MEig is the most relevant in large networks with power-law distributed in-degree. The upmost panel shows the burst count statistics for the extreme networks, see Fig. S5 for reference. The second and third panels show the prediction errors of burst count in l...

The sustained activity in recurrent networks has been under wide computational examination in studies concerning, e.g., working memory and epilepsy. Synaptic and cellular mechanisms for sustained activity have been reviewed in [1], and the optimal structural features for sustained activity have been sought for in [2]. In this work, we analyze the e...

Networks of neurons possess distinct structural organi-zation that constraints generated activity patterns, and consequently, the functions of the system. The emer-gence of the network structure can be understood by studying the rules that govern growth of neurons and their self-organization into neuronal circuits. We analyze these rules using a co...

Modeling the mechanisms of astrocytic calcium signals is important, as astrocytes have an essential role in regulating the neuronal microenvironment of the central nervous system [1,2]. The results of the wet-lab and clinical studies can be complemented by mathematical models to gain better understanding of the complex molecular level interactions...

In this work we study the excitatory AMPA, and NMDA, and inhibitory GABA A receptor mediated dynamical changes in neuronal networks of neonatal rat cortex in vitro. Extracellular network-wide activity was recorded with 59 planar electrodes simultaneously under different pharmacological conditions. We analyzed the changes of overall network activity...

Biochemical systems are inherently noisy due to the discrete reaction events that occur in a random manner. Although noise is often perceived as a disturbing factor, the system might actually benefit from it. In order to understand the role of noise better, its quality must be studied in a quantitative manner. Computational analysis and modeling pl...

Neuronal networks exhibit a wide diversity of structures, which contributes to the diversity of the dynamics therein. The presented work applies an information theoretic framework to simultaneously analyze structure and dynamics in neuronal networks. Information diversity within the structure and dynamics of a neuronal network is studied using the...

To better understand the complex molecular level interactions seen in the pathogenesis of Alzheimer's disease, the results of the wet-lab and clinical studies can be complemented by mathematical models. Astrocytes are known to become reactive in Alzheimer's disease and their ionic equilibrium can be disturbed by interaction of the released and accu...

An essential phenomenon of the functional brain is synaptic plasticity which is associated with changes in the strength of synapses between neurons. These changes are affected by both extracellular and intracellular mechanisms. For example, intracellular phosphorylation-dephosphorylation cycles have been shown to possess a special role in synaptic...

We simulate the growth of neuronal networks using the two recently published tools, NETMORPH and CX3D. The goals of the work are (1) to examine and compare the simulation tools, (2) to construct a model of growth of neocortical cultures, and (3) to characterize the changes in network connectivity during growth, using standard graph theoretic method...

The goal of this work is to present a computational frame- work on how to combine experimentally obtained data from calcium oscillations with modeling studies to un- derstand the mechanisms leading to complex interactions between amyloid-β peptide and neurotransmitters in glial cells. Experimental work has provided evidence that a failure in the pr...

The time evolution of chemical systems is traditionally modeled using deterministic ordinary differential equa- tions. Chemical reactions, however, are random in na- ture, and the deterministic approach is valid only for a restricted class of systems. Stochastic models take ran- dom fluctuations into account and are thus more realistic. In this wor...

Neuronal phosphorylation-dephosphorylation cycles have been shown to be important in the induction and mainte- nance of activity-dependent plastic modifications. In these cycles, protein kinases add phosphates to proteins and, on the other hand, phosphatases remove phosphates. Long- lasting, activity-dependent plastic modifications may pro- vide th...

Whendevelopingparameteroptimizationmethodsforsto- chastic models it is imperative to be able to compare the model output with the learning data. Due to stochastic- ity, it is not enough to study the norm of the difference between the model output and the learning data. This is the case also with models for cerebellar granule cell which exhibits sto...

More than a hundred biochemical species, activated by neurotransmitters binding to transmembrane receptors, are important in long-term potentiation (LTP) and long-term depression (LTD). To investigate which species and interactions are critical for synaptic plasticity, many computational postsynaptic signal transduction models have been developed....

Long-term activity-dependent strengthening (LTP) and weakening (LTD) of synapses are two forms of synaptic plasticity. Both LTP and LTD participate in storing information and inducing processes that ultimately lead to learning and memory (e.g., [1]). Several mechanisms have been shown to be the reason for changes in synaptic strength, for example c...

Meeting abstracts - A single PDF containing all abstracts in this

Transient rises in cytosolic calcium concentration play a crucial role in initiating long-term depression (LTD) of synapticactivity. Calciumrelease fromendoplasmicretic- ulum is particularly important in LTD. In Purkinje cells, thereleaseis mediatedbyinositol-1,4,5-trisphosphate(IP3) receptors (IP3Rs) that are highly expressed in dendritic spines....

Neurons in the brain express intrinsic dynamic behavior which is known to be stochastic in nature. A crucial question in building models of neuronal excitability is how to be able to mimic the dynamic behavior of the biological counterpart accurately and how to perform simulations in the fastest possible way. The well-established Hodgkin-Huxley for...

Several stochastic simulation tools have been developed recently for studying cellular signaling systems. These systems consist of reactions involving minute quantities of molecules. Therefore, the dynamic time-series behavior of these signaling systems needs to be studied by stochastic means. We evaluate and compare simulation tools which utilize...

Despite the transient phenomenon of activation of G-pro-tein by primary messengers on the plasma membrane, the protein kinase C (PKC) gets activated via the amplified signaling cascade stimulated by G-protein. The second messengers activating PKC play a vital role in this cascade. Therefore it is important to mimic their behavior in the model, wher...

This study evaluates parameter estimation methodology in the context of neuronal signaling networks. Based on the results of a previous study, four parameter estimation methods, Evolutionary Programming, Genetic Algorithm, Multistart, and Levenberg-Marquardt, are selected. All the reaction rate constants of the test case, the protein kinase C (PKC)...

In this work, firefly luciferase activities are studied to create a new model for the kinetics of the system. In the previous studies, the experimental and simulation results of light intensities generated from a firefly luciferin-luciferase system have not been identical. We show that small changes in the previous theoretical model allow a far bet...

Two computational methods for estimating the cell cycle phase distribution of a budding yeast (Saccharomyces cerevisiae) cell population are presented. The first one is a nonparametric method that is based on the analysis of DNA content in the individual cells of the population. The DNA content is measured with a fluorescence-activated cell sorter...

Sequential Monte Carlo based maximum likelihood estimation offers a compu-tationally efficient estimation methodology for tuning the parameter values of stochastic cell and molecular biological models. In this work, we consider a model for calcium binding reactions. The calcium binding model is simulated here with stochastic methods. Six estimation...

Mathematical modeling and simulation of dynamic biochemical systems are receiving considerable attention due to the increasing availability of experimental knowledge of complex intracellular functions. In addition to deterministic approaches, several stochastic approaches have been developed for simulating the time-series behavior of biochemical sy...

We introduce a new approach to model the behavior of neuronal signal transduction networks using stochastic differential equations. We present first a mathematical formulation for a stochastic model of protein kinase C pathway. Different kinds of numerical integration methods, including the explicit and implicit Euler-Maruyama methods, are used to...

This work is a suitability study of the different optimization methods for automated parameter estimation (fitting) in the context of neuronal signaling networks. The Gepasi simulation software is used in this study since it provides a relatively good variety of optimization methods. All the available methods are used to estimate the values of reac...

We model the intrinsic dynamic behavior of a neuron using stochastic differential equations and Brownian motion. Basis of our work is the deterministic one-compartmental multi-conductance model of cerebellar granule cell. We develop a novel modeling approach for our test neuron by incorporating the stochasticity inherently present in the operation...

We studied the properties and roles of large-conductance calcium-sensitive potassium channels (BKCa) in cultured rat cerebellar granule neurons. In intact cells these channels had chord conductances of 130-200 pS. The conductance, threshold for channel activation and the mean open time and open probability depend on the age of cells. In computer si...

A new automated image analysis method for quantification of fluorescent dots is presented. This method facilitates counting the number of fluorescent puncta in specific locations of individual cells and also enables estimation of the number of cells by detecting the labeled nuclei. The method is here used for counting the AM1-43 labeled fluorescent...

Several stochastic simulation tools have been developed recently for cell signaling. A comparative evaluation of the stochastic simulation tools is needed to highlight the current state of the development. In our study, we have chosen to evaluate three stochastic simulation tools: Dizzy, Systems Biology Toolbox, and Copasi, using our own MATLAB imp...

Several discrete, as well as continuous, stochastic ap-proaches have been developed for the time-series sim-ulation of biochemical systems. Stochastic approaches, in general, are needed because chemical reactions in-volve discrete, random collisions between individual chem-ical species. One of the well-known discrete stochastic approaches is the co...

Motivation: Simulation of dynamic biochemical systems is receiving considerable attention due to increasing availability of experimental data of complex cellular functions. Numerous simulation tools have been developed for numerical simulation of the behavior of a system described in mathematical form. However, there exist only a few evaluation st...

The activation of various intracellular biochemical pathways is influenced by the electroresponsiveness of the neuron. There exists a close and constant co-operation between the membrane-bound cellular functions and intracellular signaling. This work aims at integrating the biophysical and biochemical data available for the cultured cerebellar gran...

This work presents the basic design and tests of a device designed for detecting the contact between a microinjection pipette and cell membrane. The device facilitates the automation of the microinjection procedure of living adherent cells. The measurement of the contact is based on measuring the resistance of the pipette. Breakage and clogging of...

A method for estimating the distribution of a synchronized budding yeast (Saccharomyces cerevisiae) cell population is presented. The method is based on the analysis of the amounts of genetic material in the individual cells of the population. The analysis is done using data that are obtained with a fluorescence-activated cell sorter by using a nov...

We have developed a biophysical model of a cultured rat cerebellar granule neuron and simulated its excitability under different experimental conditions. The basic excitability properties of such a small neuron; the specific action potential waveforms, the overall firing patterns induced by current stimulations, and the linear frequency-current rel...

An approach for estimating the distribution of a synchronized budding yeast (Saccharomyces cerevisiae) cell population is discussed. This involves estimation of the phase of the cell cycle for each cell. The approach is based on counting the number of buds of different sizes in budding yeast images. An image processing procedure is presented for th...

We introduce several approaches to improve the quality of gene expression data obtained from time-series measurements by applying signal processing tools. Performance of the proposed methods are examined using both simulated and real yeast gene expression data. In particular, we concentrate especially on a smoothing effect caused by the distributio...

Cerebellum is important in controlling, fine-tuning and predicting movements. Here a mathematical model of the cerebellar granule neuron was developed to help to explain the dynamics of the non-linear ionic currents underlying the neuronal excitability. The model neuron was able to reproduce the experimentally recorded firing pattern, when six volt...