| 1. |
- Alstermark, Bror, et al.
(författare)
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The lateral reticular nucleus : integration of descending and ascending systems regulating voluntary forelimb movements
- 2015
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Ingår i: Frontiers in Computational Neuroscience. - : Frontiers Media SA. - 1662-5188. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Cerebellar control of movements is dependent on mossy fiber input conveying information about sensory and premotor activity in the spinal cord. While much is known about spino-cerebellar systems, which provide the cerebellum with detailed sensory information, much less is known about systems conveying motor information. Individual motoneurones do not have projections to spino-cerebellar neurons. Instead, the fastest route is from last order spinal interneurons. In order to identify the networks that convey ascending premotor information from last order interneurons, we have focused on the lateral reticular nucleus (LRN), which provides the major mossy fiber input to cerebellum from spinal interneuronal systems. Three spinal ascending systems to the LRN have been investigated: the C3-C4 propriospinal neurones (PNs), the ipsilateral forelimb tract (iFT) and the bilateral ventral flexor reflex tract (bVFRT). Voluntary forelimb movements involve reaching and grasping together with necessary postural adjustments and each of these three interneuronal systems likely contribute to specific aspects of forelimb motor control. It has been demonstrated that the command for reaching can be mediated via C3-C4 PNs, while the command for grasping is conveyed via segmental interneurons in the forelimb segments. Our results reveal convergence of ascending projections from all three interneuronal systems in the LRN, producing distinct combinations of excitation and inhibition. We have also identified a separate descending control of LRN neurons exerted via a subgroup of cortico-reticular neurones. The LRN projections to the deep cerebellar nuclei exert a direct excitatory effect on descending motor pathways via the reticulospinal, vestibulospinal, and other supraspinal tracts, and might play a key role in cerebellar motor control. Our results support the hypothesis that the LRN provides the cerebellum with highly integrated information, enabling cerebellar control of complex forelimb movements.
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| 2. |
- Bahuguna, Jyotika, et al.
(författare)
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Homologous Basal Ganglia Network Models in Physiological and Parkinsonian Conditions
- 2017
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Ingår i: Frontiers in Computational Neuroscience. - : FRONTIERS MEDIA SA. - 1662-5188. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- The classical model of basal ganglia has been refined in recent years with discoveries of subpopulations within a nucleus and previously unknown projections. One such discovery is the presence of subpopulations of arkypallidal and prototypical neurons in external globus pallidus, which was previously considered to be a primarily homogeneous nucleus. Developing a computational model of these multiple interconnected nuclei is challenging, because the strengths of the connections are largely unknown. We therefore use a genetic algorithm to search for the unknown connectivity parameters in a firing rate model. We apply a binary cost function derived from empirical firing rate and phase relationship data for the physiological and Parkinsonian conditions. Our approach generates ensembles of over 1,000 configurations, or homologies, for each condition, with broad distributions for many of the parameter values and overlap between the two conditions. However, the resulting effective weights of connections from or to prototypical and arkypallidal neurons are consistent with the experimental data. We investigate the significance of the weight variability by manipulating the parameters individually and cumulatively, and conclude that the correlation observed between the parameters is necessary for generating the dynamics of the two conditions. We then investigate the response of the networks to a transient cortical stimulus, and demonstrate that networks classified as physiological effectively suppress activity in the internal globus pallidus, and are not susceptible to oscillations, whereas parkinsonian networks show the opposite tendency. Thus, we conclude that the rates and phase relationships observed in the globus pallidus are predictive of experimentally observed higher level dynamical features of the physiological and parkinsonian basal ganglia, and that the multiplicity of solutions generated by our method may well be indicative of a natural diversity in basal ganglia networks. We propose that our approach of generating and analyzing an ensemble of multiple solutions to an underdetermined network model provides greater confidence in its predictions than those derived from a unique solution, and that projecting such homologous networks on a lower dimensional space of sensibly chosen dynamical features gives a better chance than a purely structural analysis at understanding complex pathologies such as Parkinson's disease.
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| 3. |
- Belic, Jovana, 1987-, et al.
(författare)
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Decoding of human hand actions to handle missing limbs in neuroprosthetics
- 2015
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Ingår i: Frontiers in Computational Neuroscience. - : Frontiers Research Foundation. - 1662-5188. ; 9:27, s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- The only way we can interact with the world is through movements, and our primary interactions are via the hands, thus any loss of hand function has immediate impact on our quality of life. However, to date it has not been systematically assessed how coordination in the hand's joints affects every day actions. This is important for two fundamental reasons. Firstly, to understand the representations and computations underlying motor control “in-the-wild” situations, and secondly to develop smarter controllers for prosthetic hands that have the same functionality as natural limbs. In this work we exploit the correlation structure of our hand and finger movements in daily-life. The novelty of our idea is that instead of averaging variability out, we take the view that the structure of variability may contain valuable information about the task being performed. We asked seven subjects to interact in 17 daily-life situations, and quantified behavior in a principled manner using CyberGlove body sensor networks that, after accurate calibration, track all major joints of the hand. Our key findings are: (1) We confirmed that hand control in daily-life tasks is very low-dimensional, with four to five dimensions being sufficient to explain 80–90% of the variability in the natural movement data. (2) We established a universally applicable measure of manipulative complexity that allowed us to measure and compare limb movements across tasks. We used Bayesian latent variable models to model the low-dimensional structure of finger joint angles in natural actions. (3) This allowed us to build a naïve classifier that within the first 1000 ms of action initiation (from a flat hand start configuration) predicted which of the 17 actions was going to be executed—enabling us to reliably predict the action intention from very short-time-scale initial data, further revealing the foreseeable nature of hand movements for control of neuroprosthetics and tele operation purposes. (4) Using the Expectation-Maximization algorithm on our latent variable model permitted us to reconstruct with high accuracy (<5–6° MAE) the movement trajectory of missing fingers by simply tracking the remaining fingers. Overall, our results suggest the hypothesis that specific hand actions are orchestrated by the brain in such a way that in the natural tasks of daily-life there is sufficient redundancy and predictability to be directly exploitable for neuroprosthetics.
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| 4. |
- Bozhko, Dmitrii V., et al.
(författare)
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BCNNM : A Framework for in silico Neural Tissue Development Modeling
- 2021
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Ingår i: Frontiers in Computational Neuroscience. - : Frontiers Media S.A.. - 1662-5188. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- Cerebral ("brain") organoids are high-fidelity in vitro cellular models of the developing brain, which makes them one of the go-to methods to study isolated processes of tissue organization and its electrophysiological properties, allowing to collect invaluable data for in silico modeling neurodevelopmental processes. Complex computer models of biological systems supplement in vivo and in vitro experimentation and allow researchers to look at things that no laboratory study has access to, due to either technological or ethical limitations. In this paper, we present the Biological Cellular Neural Network Modeling (BCNNM) framework designed for building dynamic spatial models of neural tissue organization and basic stimulus dynamics. The BCNNM uses a convenient predicate description of sequences of biochemical reactions and can be used to run complex models of multi-layer neural network formation from a single initial stem cell. It involves processes such as proliferation of precursor cells and their differentiation into mature cell types, cell migration, axon and dendritic tree formation, axon pathfinding and synaptogenesis. The experiment described in this article demonstrates a creation of an in silico cerebral organoid-like structure, constituted of up to 1 million cells, which differentiate and self-organize into an interconnected system with four layers, where the spatial arrangement of layers and cells are consistent with the values of analogous parameters obtained from research on living tissues. Our in silico organoid contains axons and millions of synapses within and between the layers, and it comprises neurons with high density of connections (more than 10). In sum, the BCNNM is an easy-to-use and powerful framework for simulations of neural tissue development that provides a convenient way to design a variety of tractable in silico experiments.
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| 5. |
- Brocke, Ekaterina, et al.
(författare)
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Efficient Integration of Coupled Electrical-Chemical Systems in Multiscale Neuronal Simulations
- 2016
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Ingår i: Frontiers in Computational Neuroscience. - : Frontiers Media SA. - 1662-5188. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Multiscale modeling and simulations in neuroscience is gaining scientific attention due to its growing importance and unexplored capabilities. For instance, it can help to acquire better understanding of biological phenomena that have important features at multiple scales of time and space. This includes synaptic plasticity, memory formation and modulation, homeostasis. There are several ways to organize multiscale simulations depending on the scientific problem and the system to be modeled. One of the possibilities is to simulate different components of a multiscale system simultaneously and exchange data when required. The latter may become a challenging task for several reasons. First, the components of a multiscale system usually span different spatial and temporal scales, such that rigorous analysis of possible coupling solutions is required. Then, the components can be defined by different mathematical formalisms. For certain classes of problems a number of coupling mechanisms have been proposed and successfully used. However, a strict mathematical theory is missing in many cases. Recent work in the field has not so far investigated artifacts that may arise during coupled integration of different approximation methods. Moreover, in neuroscience, the coupling of widely used numerical fixed step size solvers may lead to unexpected inefficiency. In this paper we address the question of possible numerical artifacts that can arise during the integration of a coupled system. We develop an efficient strategy to couple the components comprising a multiscale test problem in neuroscience. We introduce an efficient coupling method based on the second-order backward differentiation formula (BDF2) numerical approximation. The method uses an adaptive step size integration with an error estimation proposed by Skelboe (2000). The method shows a significant advantage over conventional fixed step size solvers used in neuroscience for similar problems. We explore different coupling strategies that define the organization of computations between system components. We study the importance of an appropriate approximation of exchanged variables during the simulation. The analysis shows a substantial impact of these aspects on the solution accuracy in the application to our multiscale neuroscientific test problem. We believe that the ideas presented in the paper may essentially contribute to the development of a robust and efficient framework for multiscale brain modeling and simulations in neuroscience.
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| 6. |
- Carannante, Ilaria, et al.
(författare)
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Data-Driven Model of Postsynaptic Currents Mediated by NMDA or AMPA Receptors in Striatal Neurons
- 2022
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Ingår i: Frontiers in Computational Neuroscience. - : Frontiers Media SA. - 1662-5188. ; 16
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Tidskriftsartikel (refereegranskat)abstract
- The majority of excitatory synapses in the brain uses glutamate as neurotransmitter, and the synaptic transmission is primarily mediated by AMPA and NMDA receptors in postsynaptic neurons. Here, we present data-driven models of the postsynaptic currents of these receptors in excitatory synapses in mouse striatum. It is common to fit two decay time constants to the decay phases of the current profiles but then compute a single weighted mean time constant to describe them. We have shown that this approach does not lead to an improvement in the fitting, and, hence, we present a new model based on the use of both the fast and slow time constants and a numerical calculation of the peak time using Newton's method. Our framework allows for a more accurate description of the current profiles without needing extra data and without overburdening the comptuational costs. The user-friendliness of the method, here implemented in Python, makes it easily applicable to other data sets.
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| 7. |
- Feldwisch-Drentrup, Hinnerk, et al.
(författare)
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Identification of preseizure states in epilepsy: a data-driven approach for multichannel EEG recordings
- 2011
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Ingår i: Frontiers in Computational Neuroscience. - : Frontiers Research Foundation. - 1662-5188. ; 5:32
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Tidskriftsartikel (refereegranskat)abstract
- The retrospective identification of preseizure states usually bases on a time-resolved characterization of dynamical aspects of multichannel neurophysiologic recordings that can be assessed with measures from linear or non-linear time series analysis. This approach renders time profiles of a characterizing measure - so-called measure profiles - for different recording sites or combinations thereof. Various downstream evaluation techniques have been proposed to single out measure profiles that carry potential information about preseizure states. These techniques, however, rely on assumptions about seizure precursor dynamics that might not be generally valid or face the statistical problem of multiple testing. Addressing these issues, we have developed a method to preselect measure profiles that carry potential information about preseizure states, and to identify brain regions associated with seizure precursor dynamics. Our data-driven method is based on the ratio S of the global to local temporal variance of measure profiles. We evaluated its suitability by retrospectively analyzing long-lasting multichannel intracranial EEG recordings from 18 patients that included 133 focal onset seizures, using a bivariate measure for the strength of interactions. In 17/18 patients, we observed S to be significantly correlated with the predictive performance of measure profiles assessed retrospectively by means of receiver-operating-characteristic statistics. Predictive performance was higher for measure profiles preselected with S than for a manual selection using information about onset and spread of seizures. Across patients, highest predictive performance was not restricted to recordings from focal areas, thus supporting the notion of an extended epileptic network in which even distant brain regions contribute to seizure generation. We expect our method to provide further insight into the complex spatial and temporal aspects of the seizure generating process.
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| 8. |
- Fiebig, Florian, et al.
(författare)
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Memory consolidation from seconds to weeks : a three-stage neural network model with autonomous reinstatement dynamics
- 2014
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Ingår i: Frontiers in Computational Neuroscience. - : Frontiers Media SA. - 1662-5188. ; 8, s. 64-
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Tidskriftsartikel (refereegranskat)abstract
- Declarative long-term memories are not created in an instant. Gradual stabilization and temporally shifting dependence of acquired declarative memories in different brain regions called systems consolidation- can be tracked in time by lesion experiments. The observation of temporally graded retrograde amnesia(RA) following hippocampal lesions points to a gradual transfer of memory from hippocampus to neocortical long-term memory. Spontaneous reactivations of hippocampal memories, asobserved in place cell reactivations during slow wave- sleep, are supposed to driven eocortical reinstatements and facilitate this process. We proposea functional neural network implementation of these ideas and further more suggest anextended three-state framework that includes the prefrontal cortex( PFC). It bridges the temporal chasm between working memory percepts on the scale of seconds and consolidated long-term memory on the scale of weeks or months. Wes how that our three-stage model can autonomously produce the necessary stochastic reactivation dynamics for successful episodic memory consolidation. There sulting learning system is shown to exhibit classical memory effects seen in experimental studies, such as retrograde and anterograde amnesia(AA) after simulated hippocampal lesioning; further more the model reproduces peculiar biological findings on memory modulation, such as retrograde facilitation of memory after suppressed acquisition of new longterm memories- similar to the effects of benzodiazepines on memory.
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| 9. |
- Gallistel, Charles Randy, et al.
(författare)
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Quantitative properties of the creation and activation of a cell-intrinsic duration-encoding engram
- 2022
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Ingår i: Frontiers in Computational Neuroscience. - : Frontiers Media SA. - 1662-5188. ; 16, s. 1-27
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Tidskriftsartikel (refereegranskat)abstract
- The engram encoding the interval between the conditional stimulus (CS) and the unconditional stimulus (US) in eyeblink conditioning resides within a small population of cerebellar Purkinje cells. CSs activate this engram to produce a pause in the spontaneous firing rate of the cell, which times the CS-conditional blink. We developed a Bayesian algorithm that finds pause onsets and offsets in the records from individual CS-alone trials. We find that the pause consists of a single unusually long interspike interval. Its onset and offset latencies and their trial-to-trial variability are proportional to the CS-US interval. The coefficient of variation (CoV = σ/μ) are comparable to the CoVs for the conditional eye blink. The average trial-to-trial correlation between the onset latencies and the offset latencies is close to 0, implying that the onsets and offsets are mediated by two stochastically independent readings of the engram. The onset of the pause is step-like; there is no decline in firing rate between the onset of the CS and the onset of the pause. A single presynaptic spike volley suffices to trigger the reading of the engram; and the pause parameters are unaffected by subsequent volleys. The Fano factors for trial-to-trial variations in the distribution of interspike intervals within the intertrial intervals indicate pronounced non-stationarity in the endogenous spontaneous spiking rate, on which the CS-triggered firing pause supervenes. These properties of the spontaneous firing and of the engram read out may prove useful in finding the cell-intrinsic, molecular-level structure that encodes the CS-US interval.
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| 10. |
- Henningson, Måns, 1964, et al.
(författare)
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Analysis and Modeling of Subthreshold Neural Multi-Electrode Array Data by Statistical Field Theory
- 2017
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Ingår i: Frontiers in Computational Neuroscience. - : Frontiers Media SA. - 1662-5188. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- Multi-electrode arrays (MEA) are increasingly used to investigate spontaneous neuronal network activity. The recorded signals comprise several distinct components: Apart from artefacts without biological significance, one can distinguish between spikes (action potentials) and subthreshold fluctuations (local fields potentials). Here we aim to develop a theoretical model that allows for a compact and robust characterization of subthreshold fluctuations in terms of a Gaussian statistical field theory in two spatial and one temporal dimension. What is usually referred to as the driving noise in the context of statistical physics is here interpreted as a representation of the neural activity. Spatial and temporal correlations of this activity give valuable information about the connectivity in the neural tissue. We apply our methods on a dataset obtained from MEA-measurements in an acute hippocampal brain slice from a rat. Our main finding is that the empirical correlation functions indeed obey the logarithmic behaviour that is a general feature of theoretical models of this kind. We also find a clear correlation between the activity and the occurence of spikes. Another important insight is the importance of correcly separating out certain artefacts from the data before proceeding with the analysis.
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