Papers by Aušra Saudargienė
Bio Systems, Jan 10, 2015
A computational model of a biochemical network underlying synaptic plasticity is combined with si... more A computational model of a biochemical network underlying synaptic plasticity is combined with simulated on-going electrical activity in a model of a hippocampal pyramidal neuron to study the impact of synapse location and inhibition on synaptic plasticity. The simulated pyramidal neuron is activated by the realistic stimulation protocol of causal and anticausal spike pairings of presynaptic and postsynaptic action potentials in the presence and absence of spatially targeted inhibition provided by basket, bistratified and oriens-lacunosum moleculare (OLM) interneurons. The resulting Spike-timing-dependent plasticity (STDP) curves depend strongly on the number of pairing repetitions, the synapse location and the timing and strength of inhibition.
Neuron-Glia Interactions and Brain Circuits
Advances in experimental medicine and biology, 2022
Recent evidence suggests that glial cells take an active role in a number of brain functions that... more 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 role in supporting and expanding the functions of brain circuits and networks. Despite this accumulated knowledge, the field of computational neuroscience has mostly focused on modeling neuronal functions, ignoring the glial cells and the interactions they have with the neurons. In this chapter, we introduce the biology of neuron-glia interactions, summarize the existing computational models and tools, and emphasize the glial properties that may be important in modeling brain funct...
Dependence of neuronal synaptic plasticity on NMDA receptor type : a computational study
Taikomosios informatikos katedraVytauto Didžiojo universiteta
Static 99mTc-tetrofosmin scintigraphy predicts chemotherapy response in small cell lung cancer
Abstracts annual congress of the EANM, September 30-October 4, 2006, Athens, Greece. ISSN: 1619-7... more Abstracts annual congress of the EANM, September 30-October 4, 2006, Athens, Greece. ISSN: 1619-7089 (electronic version)Kauno medicinos universiteto klinikosTaikomosios informatikos katedraVytauto Didžiojo universiteta
POSTER PRESENTATION Open Access Interaction of inhibition and synaptic plasticity in a model of the hippocampal CA1 microcircuit
Full list of author information is available at the end of the article Cellular activity in the C... more Full list of author information is available at the end of the article Cellular activity in the CA1 area of the hippocampus waxes and wanes at theta frequency (4-7Hz) when a rat is exploring an environment. Perisomatic inhibition onto pyramidal cells from basket cells and axoaxonic cells tends to be strongest out of phase with pyramidal cell activity, in a so-called storage cycle, whereas dendritic inhibition, mediated by bistratified and oriens lacunosum-moleculare (OLM) cells is strongest in phase with pyramidal cell activity [1], in a so-called recall cycle. Synaptic plasticity also varies across the theta cycle, from strong LTP to LTD, putatively corresponding to the storage and recall cycles for information patterns encoded in pyramidal cell activity [2]. The mechanisms underpinning the phasic changes in plasticity are not
Intelligent Computing Theories and Application, 2017
Spiking neural networks represent a third generation of artificial neural networks and are inspir... more 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 model to better understand the dynamics of mechanisms that lead to changes in information processing between a postsynaptic neuron and an astrocyte. We used a classical stimulation protocol of long-term plasticity to test the model functionality. The long-term goal of our work is to develop extended synapse models including neuron-astrocyte interactions to address plasticity and learning in cortical synapses. Our modeling studies will advance the development of novel learning algorithms to be used in the extended synapse models and spiking neural networks. The novel algorithms can provide a basis for artificial intelligence systems that can emulate the functionality of mammalian brain.
Psichologinių tyrimų duomenų analizė
Statistika su SPSS psichologiniuose tyrimuose : mokomoji knyga
Psichologijos katedraTaikomosios informatikos katedraVytauto Didžiojo universiteta
M-tolimosios metastazės, N-plaučių vėžio išplitimas į limfmazgius, T-pirminio naviko stadija, Tis... more M-tolimosios metastazės, N-plaučių vėžio išplitimas į limfmazgius, T-pirminio naviko stadija, Tis-karcinoma in situ, Tx-pirminio naviko neįmanoma įvertinti.
Radiomic features of amygdala nuclei and hippocampus subfields help to predict subthalamic deep brain stimulation motor outcomes for Parkinson‘s disease patients
Frontiers in Neuroscience
Background and purposeThe aim of the study is to predict the subthalamic nucleus (STN) deep brain... more Background and purposeThe aim of the study is to predict the subthalamic nucleus (STN) deep brain stimulation (DBS) outcomes for Parkinson’s disease (PD) patients using the radiomic features extracted from pre-operative magnetic resonance images (MRI).MethodsThe study included 34 PD patients who underwent DBS implantation in the STN. Five patients (15%) showed poor DBS motor outcome. All together 9 amygdalar nuclei and 12 hippocampus subfields were segmented using Freesurfer 7.0 pipeline from pre-operative MRI images. Furthermore, PyRadiomics platform was used to extract 120 radiomic features for each nuclei and subfield resulting in 5,040 features. Minimum Redundancy Maximum Relevance (mRMR) feature selection method was employed to reduce the number of features to 20, and 8 machine learning methods (regularized binary logistic regression (LR), decision tree classifier (DT), linear discriminant analysis (LDA), naive Bayes classifier (NB), kernel support vector machine (SVM), deep fe...
PLOS Computational Biology, 2020
Astrocytes have been shown to modulate synaptic transmission and plasticity in specific cortical ... more 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, we show that a signal from a postsynaptic neuron, orchestrated by endocannabinoids, astrocytic calcium signaling, and presynaptic N-methyl-D-aspartate receptors coupled with calcineurin signaling, induces t-LTD which is sensitive to the temporal difference between post- and presynaptic firing. We predict for the first time the dynamics of astrocyte-mediated molecular mechanisms underlying t-LTD and link complex biochemical networks at presynaptic, postsynaptic, and astrocytic sites to the ti...
Spike-timing-dependent plasticity (STDP) is a form of bidirectional change in synaptic strength t... more Spike-timing-dependent plasticity (STDP) is a form of bidirectional change in synaptic strength that depends on the temporal order and temporal difference of the pre-and postsynaptic activity . The synapse undergoes long-term potentiation (LTP) if the presynaptic spike precedes the postsynaptic spike, and exhibits longterm depression (LTD) if the temporal order is reversed. Recent physiological observations suggest that the form of plasticity at a synapse depends not only on the timing of the pre-and postsynaptic activity but also on the location of the synapse on the dendritic tree [2]. We proposed a biophysical model of STDP predicting that learning rules are location-dependent . Numerous modeling studies investigate molecular mechanisms of synaptic plasticity (e.g.
Modeling astrocyte-neuron interactions in a tripartite synapse
BMC Neuroscience, 2014
Artificial neural networks (ANNs) are usually homoge- nous in respect to the used learning algori... more Artificial neural networks (ANNs) are usually homoge- nous in respect to the used learning algorithms. On the other hand, recent physiological observations suggest that in bio- logical neurons synapses undergo changes according to lo- cal learning rules. In this study we present a biophysically motivated learning rule which is influenced by the shape of the correlated signals and results in a learning charac- teristic which depends on the dendritic site. We investigate this rule in a biophysical model as well as in the equiva- lent artificial neural network model. As a consequence of our local rule we observe that transitions from differential Hebbian to plain Hebbian learning can coexist at the same neuron. Thus, such a rule could be used in an ANN to cre- ate synapses with entirely different learning properties at the same network unit in a controlled way.
Neural Computation, 2004
In this article, we present a biophysical model of STDP based on a differential Hebbian learning ... more In this article, we present a biophysical model of STDP based on a differential Hebbian learning rule (ISO learning). This rule correlates presynaptically the NMDA channel conductance with the derivative of the membrane potential at the synapse as the postsynaptic signal. The model is able to reproduce the generic STDP weight change characteristic. We nd that (1) The actual shape of the weight change curve strongly depends on the NMDA channel characteristics and on the shape of the membrane potential at the synapse.
BMC Neuroscience, 2011
Cellular activity in the CA1 area of the hippocampus waxes and wanes at theta frequency (4-7Hz) w... more Cellular activity in the CA1 area of the hippocampus waxes and wanes at theta frequency (4-7Hz) when a rat is exploring an environment. Perisomatic inhibition onto pyramidal cells from basket cells and axoaxonic cells tends to be strongest out of phase with pyramidal cell activity, in a so-called storage cycle, whereas dendritic inhibition, mediated by bistratified and oriens lacunosum-moleculare (OLM) cells is strongest in phase with pyramidal cell activity [1], in a so-called recall cycle. Synaptic plasticity also varies across the theta cycle, from strong LTP to LTD, putatively corresponding to the storage and recall cycles for information patterns encoded in pyramidal cell activity . The mechanisms underpinning the phasic changes in plasticity are not clear, but it is likely that inhibition plays a role by affecting levels of electrical activity and calcium levels at synapses. Calcium levels at dendritic synapses could reach the amplitudes required for LTP when inhibition is restricted to the perisomatic region, but may be restricted to amplitudes that result in LTD when inhibition is strong in the dendrites.
BMC Neuroscience, 2010
Spike-timing-dependent plasticity (STDP) is a form of bidirectional change in synaptic strength t... more Spike-timing-dependent plasticity (STDP) is a form of bidirectional change in synaptic strength that depends on the temporal order and temporal difference of the pre-and postsynaptic activity . The synapse undergoes long-term potentiation (LTP) if the presynaptic spike precedes the postsynaptic spike, and exhibits longterm depression (LTD) if the temporal order is reversed. Recent physiological observations suggest that the form of plasticity at a synapse depends not only on the timing of the pre-and postsynaptic activity but also on the location of the synapse on the dendritic tree [2]. We proposed a biophysical model of STDP predicting that learning rules are location-dependent . Numerous modeling studies investigate molecular mechanisms of synaptic plasticity (e.g.
Biological Cybernetics, 2005
Recent indirect experimental evidence suggests that synaptic plasticity changes along the dendrit... more Recent indirect experimental evidence suggests that synaptic plasticity changes along the dendrites of a neuron. Here we present a synaptic plasticity rule which is controlled by the properties of the pre-and post-synaptic signals. Using recorded membrane traces of back-propagating and dendritic spikes we demonstrate that LTP and LTD will depend specifically on the shape of the post-synaptic depolarization at a given dendritic site. We find that asymmetrical spiketiming dependent plasticity (STDP) can be replaced by temporally symmetrical plasticity within physiologically relevant time-windows if the post-synaptic depolarization rises shallow. Pre-synaptically the rule depends on the NMDA channel characteristic and the model predicts that an increase in M g 2+ will attenuate the STDP curve without changing its shape. Furthermore, the model suggests that the profile of LTD should be governed by the post-synaptic signal while that of LTP mainly depends on the pre-synaptic signal shape.
International Journal of Cardiology, 2015
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Papers by Aušra Saudargienė