3,160 research outputs found

    Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective

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    On metrics of density and power efficiency, neuromorphic technologies have the potential to surpass mainstream computing technologies in tasks where real-time functionality, adaptability, and autonomy are essential. While algorithmic advances in neuromorphic computing are proceeding successfully, the potential of memristors to improve neuromorphic computing have not yet born fruit, primarily because they are often used as a drop-in replacement to conventional memory. However, interdisciplinary approaches anchored in machine learning theory suggest that multifactor plasticity rules matching neural and synaptic dynamics to the device capabilities can take better advantage of memristor dynamics and its stochasticity. Furthermore, such plasticity rules generally show much higher performance than that of classical Spike Time Dependent Plasticity (STDP) rules. This chapter reviews the recent development in learning with spiking neural network models and their possible implementation with memristor-based hardware

    Neural mechanisms of social learning in the female mouse

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    Social interactions are often powerful drivers of learning. In female mice, mating creates a long-lasting sensory memory for the pheromones of the stud male that alters neuroendocrine responses to his chemosignals for many weeks. The cellular and synaptic correlates of pheromonal learning, however, remain unclear. We examined local circuit changes in the accessory olfactory bulb (AOB) using targeted ex vivo recordings of mating-activated neurons tagged with a fluorescent reporter. Imprinting led to striking plasticity in the intrinsic membrane excitability of projection neurons (mitral cells, MCs) that dramatically curtailed their responsiveness, suggesting a novel cellular substrate for pheromonal learning. Plasticity was selectively expressed in the MC ensembles activated by the stud male, consistent with formation of memories for specific individuals. Finally, MC excitability gained atypical activity-dependence whose slow dynamics strongly attenuated firing on timescales of several minutes. This unusual form of AOB plasticity may act to filter sustained or repetitive sensory signals.R21 DC013894 - NIDCD NIH HH

    Shaping of Spike-Timing-Dependent Plasticity curve using interneuron and calcium dynamics

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    The field of Computational Neuroscience is where neuroscience and computational modelling merge together. It is an ever-emerging area of research where the level of biological modelling can range from small-scale cellular models, to the larger network scale models. This MSc Thesis will detail the research carried out when looking at a small network of two neurons. These neurons have been modelled with a high level of detail, with the intention of using it to study the phenomenon of Spike-Timing-Dependent Plasticity (or STDP). Spike-Timing-Dependent Plasticity is the occurrence of either a strengthening or weakening in connection between two neurons, depending on the temporal order of stimulation between them. A major part of the work detailed is the focus on what mechanisms are responsible for these changes in plasticity, with the goal of representing the mechanisms in a single learning rule. The results found can be directly compared to data previously seen by scientists who worked on in-vitro experiments. The research then goes on to look at further applications of the model, in particular, looking at certain deficits seen in people with Schizophrenia. We modify the model to include these cellular impairments, then observe how this affects the standard STDP curve and thus affects the strengthening/weakening between the two neurons

    Temporal discrimination from the interaction between dynamic synapses and intrinsic subthreshold oscillations

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    The interaction between synaptic and intrinsic dynamics can efficiently shape neuronal input-output relationships in response to temporally structured spike trains. We use a neuron model with subthresh-old oscillations receiving inputs through a synapse with short-term depression and facilitation to show that the combination of intrinsic subthreshold and synaptic dynamics leads to channel-specific nontrivial responses and recognition of specific temporal structures. Our study employs the Generalized Integrate and-Fire (GIF) model, which can be subjected to analytical characterization. We map the temporal structure of spike input trains to the type of spike response, and show how the emergence of nontrivial input- output preferences is modulated by intrinsic and synaptic parameters in a synergistic manner. We demonstrate that these temporal input discrimination properties are robust to noise and to variations in synaptic strength. Furthermore, we also illustrate the presence of these input-output relationships in conductance-based models. Our results suggest a widespread computationally economic and easily tunable mechanism for temporal information discrimination in single neurons. (c) 2020 Elsevier B.V. All rights reserved.This work was supported AEI/FEDER grants FIS2017-84256-P (JJT) and PGC2018-095895-B-I00, DPI2015-65833-P (RL & PV)

    The fine scale structure of synaptic inputs in developing hippocampal neurons

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    Structure-function analysis on the level of individual synapses

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    Excitatory synapses in the mammalian brain are made on small protrusions of the postsynaptic cell called dendritic spines. Dendritic spines are highly variable in their morphology and in their microanatomy (e.g. presence of subsynaptic organelles). It is unclear whether and how variability in spine morphological and anatomical properties translates into differences in synaptic function. Using two photon imaging, we analyzed how spine properties can affect synaptic signals and the potential for synaptic plasticity at single identified spine synapses. We show that synaptic signals can be tightly regulated on the level of individual synapses and that differences in spine morphology and microanatomy regulate synaptic function. We also provide evidence for the existence of functionally distinct populations of synapses in regard to their potential for synaptic plasticity. The present thesis is subdivided into three main sections. The first section is dedicated to the analysis of the function of specialized subsynaptic organelles in regulating synaptic plasticity. In the second section we studied the impact of spine morphology on synaptic signals and in the third section we examined whether critical proteins can be tagged to individual synapses in response to plasticity inducing stimuli. In pyramidal cells, only a subset of dendritic spines contains endoplasmic reticulum (ER). Spine ER often forms a β€˜spine apparatus’, a specialized organelle with unknown function. It is unclear whether these specialized subsynaptic structures can affect the function of the synapse on the spine head. The possible involvement of spine ER in shaping spine calcium transients, a key trigger for synaptic plasticity, raises the possibility that spine ER could modulate the potential of a given synapse to undergo activity dependent modifications. Using a genetic approach to label the ER in living neurons, we find that the ER preferentially localizes to spines containing strong synapses. We demonstrate that spine ER represents a specialized calcium signaling machinery required for the induction of metabotropic glutamate receptor dependent long term depression at individual synapses. We demonstrate that different subsets of synapses exist in regard to their potential to undergo specific forms of plasticity. Spine ER represents the anatomical correlate for a mechanism by which strong synapses can be retuned in an activity dependent manner. Dendritic spines are separated from their parent dendrite by a thin spine neck. The spine neck slows down diffusion of molecules from the spine head to the parent dendrite, allowing spine-specific action of second messengers and activated enzymes. The resistance of the spine neck is crucial in determining whether spines can also be considered electrical compartments. Only a high enough spine neck resistance leads to electrical compartmentalization and activation of voltage gated channels in the spine in response to synaptic stimulation. We show that spine neck resistance can change in an activity dependent manner. Using single spine calcium imaging as a reporter of NMDA receptor activation and spine head depolarization, we show that spines can indeed act as electrical compartments. Using pharmacological experiments and modeling, we demonstrate that different voltage dependent channels cooperatively participate in shaping spine head depolarization and spine calcium transients. We also show that in vivo the spine neck resistance is higher compared to the situation in acutely sliced brain tissue, demonstrating that in the living animal a higher fraction of spines can be considered electrical compartments compared to the in vitro situation. We provide strong evidence that the spine neck can profoundly affect synaptic calcium signals. Biochemical and electrical compartmentalization is dynamically regulated in an activity dependent way. Spine calcium signals can activate key signaling cascades responsible for the induction of synaptic plasticity. Long term potentiation (LTP) has been shown to require the activity of CaMKII, a serine/ threonine kinase. A chemical protocol leading to LTP has been shown to induce translocation of CaMKII to dendritic spines. It is however unclear whether this molecule acts at single synapses or whether it can spread and modulate neighboring synapses in response to more physiological protocols. Using a new optical approach to induce LTP at single visualized synapses, we show that LTP induction is accompanied by a long-lasting increase of CaMKII at the stimulated synapse. This increase was specific to the stimulated spine and did not spread to neighboring spines. We provide evidence that CaMKII acts locally, on the micrometer scale, to regulate plasticity. We show that the concentration of proteins involved in regulating synaptic plasticity can be tightly regulated at the level of single synapses

    Contributions to models of single neuron computation in striatum and cortex

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    A deeper understanding is required of how a single neuron utilizes its nonlinear subcellular devices to generate complex neuronal dynamics. Two compartmental models of cortex and striatum are accurately formulated and firmly grounded in the experimental reality of electrophysiology to address the questions: how striatal projection neurons implement location-dependent dendritic integration to carry out association-based computation and how cortical pyramidal neurons strategically exploit the type and location of synaptic contacts to enrich its computational capacities.Neuronale Zellen transformieren kontinuierliche Signale in diskrete Zeitserien von Aktionspotentialen und kodieren damit Perzeptionen und interne ZustΓ€nde. Kompartiment-Modelle werden formuliert von Nervenzellen im Kortex und Striatum, die elektrophysiologisch fundiert sind, um spezifische Fragen zu adressieren: i) Inwiefern implementieren Projektionen vom Striatum ortsabhΓ€ngige dendritische Integration, um Assoziationens-basierte Berechnungen zu realisieren? ii) Inwiefern nutzen kortikale Zellen den Typ und den Ort, um die durch sie realisierten Berechnungen zu optimieren

    μ†Œλ‡Œ 퍼킨지 세포 λ‚΄μž¬μ  ν₯λΆ„μ„±μ˜ ν™œλ™-의쑴적 쑰절

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    ν•™μœ„λ…Όλ¬Έ (박사)-- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› : μ˜κ³ΌλŒ€ν•™ μ˜κ³Όν•™κ³Ό, 2019. 2. 김상정.Learning rule has been thought to be implemented by activity-dependent modifications of synaptic function and neuronal excitability which contributing to maximization the information flow in the neural network. Since the sensory information is conveyed by forms of action potential (AP) firing, the plasticity of the intrinsic excitability (intrinsic plasticity) has been highlighted the computational feature of the brain. Given the cerebellar Purkinje cells (PCs) is the sole output neurons in the cerebellar cortex, coordination of the synaptic plasticity at the parallel fiber (PF) to PC synapses including long-term depression (LTD) and long-term potentiation (LTP) but also the intrinsic plasticity may play a essential role in information processing in the cerebellum. In this Dissertation, I have investigated several features of intrinsic plasticity in the cerebellar PCs in an activity-dependent manner and their cellular mechanism. Furthermore, the functional implications of the intrinsic plasticity in the cerebellum-dependent behavioral output are discussed. Firstly, I first cover the ion channels regulating the spiking activity of the cerebellar PCs and the cellular mechanisms of the plastic changes in excitability. Various ion channels indeed harmonize the cellular activity and shaping the optimal ranges of the neuronal excitability. Among the ion channels expressed in the cerebellar PCs, hyperpolarization-activated cyclic nucleotide-gated (HCN) channels contribute to the non-Hebbian homeostatic intrinsic plasticity in the cerebellar PCs. Chronic activity-deprivation of PC activity caused the upregulation of agonist-independent activity of type 1 metabotropic glutamate receptor (mGluR1). The increased mGluR1 activity consequently enhanced the HCN channel current density through protein kinase A (PKA) pathway thereby downregulation of intrinsic excitability in PCs. In addition, the intrinsic excitability of PCs is found to be modulated by synaptic activity. Of interest, I investigated that the PF-PC LTD is accompanied by LTD of intrinsic excitability (LTD-IE). The LTD-IE indeed shared intracellular signal cascade for governing the synaptic LTD such as large amount of Ca2+ influx, mGluR1, protein kinase C (PKC) and Ca2+-calmodulin-dependent protein kinase II (CaMKII) activation. Interestingly, the LTD-IE reduced PC spike output without changes in patterns of synaptic integration and spike generation, suggesting that the intrinsic plasticity alters the quantity of information rather than the quality of information processing. In consistent, the LTD-IE was shown in the floccular PCs when the PF-PC LTD occurs. Notably, not only the synaptic LTD but also LTD-IE was found to be formed at the conditioned dendritic branch. Thus, synaptic plasticity could significantly affect to the neuronal net output through the synergistic coordination of synaptic and intrinsic plasticity in the dendrosomatic axis of the cerebellar PCs. In conclusion, the activity-dependent modulation of intrinsic excitability may contribute to dynamic tuning of the cerebellar PC output for appropriate signal transduction into the downstream neurons of the cerebellar PCs.생λͺ…μ²΄λŠ” λŠμž„μ—†μ΄ μ£Όλ³€ν™˜κ²½μ— λ°˜μ‘ν•˜μ—¬ 행동을 μˆ˜μ •ν•˜λ©° μ΄λŸ¬ν•œ 적응은 λ³€ν™”ν•˜λŠ” ν™˜κ²½μ—μ„œ 생쑴에 ν•„μˆ˜μ μ΄λ‹€. μ†Œλ‡Œ-μš΄λ™ ν•™μŠ΅μ€ λŒ€ν‘œμ μΈ 적응 ν–‰λ™μ˜ μ˜ˆμ΄λ‹€. λ‹€μ–‘ν•œ 감각 μ‹ ν˜Έλ“€μ΄ μ†Œλ‡Œλ‘œ μ „λ‹¬λ˜μ–΄ 처리된 ν›„ μ†Œλ‡Œ 좜λ ₯을 톡해 μš΄λ™ ν˜‘μ‘μ΄ 이루어진닀. μ΄λŸ¬ν•œ μ†Œλ‡Œ-μš΄λ™ ν•™μŠ΅ 및 μ†Œλ‡Œ κΈ°λŠ₯ 쑰절의 세포 생리학적 κΈ°μ „μœΌλ‘œ μ†Œλ‡Œ 퍼킨지 μ„Έν¬μ˜ μ‹œλƒ…μŠ€ μž₯κΈ°μ €ν•˜κ°€ μ˜€λž«λ™μ•ˆ μ£Όλͺ©λ°›μ•˜λ‹€. 퍼킨지 μ„Έν¬μ˜ μ‹œλƒ…μŠ€ μž₯κΈ°μ €ν•˜κ°€ λ‚˜νƒ€λ‚˜μ§€ μ•ŠλŠ” μœ μ „μž λ³€ν˜• 동물 λͺ¨λΈλ“€μ—μ„œ μ†Œλ‡Œ-μš΄λ™ ν•™μŠ΅μ΄ μ •μƒμ μœΌλ‘œ μΌμ–΄λ‚˜μ§€ μ•ŠλŠ” ν˜„μƒμ΄ κ΄€μ°°λ˜μ—ˆκΈ° λ•Œλ¬Έμ— μ‹œλƒ…μŠ€ μž₯κΈ°μ €ν•˜ 이둠은 였랜 μ‹œκ°„ μ†Œλ‡Œ-μš΄λ™ ν•™μŠ΅μ˜ κΈ°μ „μœΌλ‘œ 지지 λ°›μ•˜λ‹€. ν•˜μ§€λ§Œ 졜근 10λ…„ λ™μ•ˆμ˜ μ—°κ΅¬κ²°κ³ΌλŠ” μ‹œλƒ…μŠ€ μž₯κΈ°μ €ν•˜λ§ŒμœΌλ‘œ μ†Œλ‡Œ-μš΄λ™ ν•™μŠ΅ 및 κΈ°λŠ₯ μ‘°μ ˆμ„ μ„€λͺ…ν•  수 μ—†λ‹€κ³  λ°˜λ°•ν•œλ‹€. 특히 μ†Œλ‡Œ 퍼킨지 μ„Έν¬λŠ” μ†Œλ‡Œ ν”Όμ§ˆλ‘œ μ „λ‹¬λœ κ°κ°μ‹ ν˜Έλ₯Ό μ²˜λ¦¬ν•˜μ—¬ 좜λ ₯을 λ‹΄λ‹Ήν•˜λŠ” μœ μΌν•œ μ‹ κ²½μ„Έν¬μ΄λ―€λ‘œ μš΄λ™ ν•™μŠ΅ μƒν™©μ—μ„œ μ†Œλ‡Œμ˜ 좜λ ₯이 μ–΄λ–»κ²Œ μ‘°μ ˆλ˜λŠ”μ§€λ₯Ό μ΄ν•΄ν•˜λŠ” 것이 μ€‘μš”ν•˜κ²Œ μΈμ‹λ˜μ—ˆλ‹€. 감각 μ‹ ν˜Έκ°€ μ‹ κ²½ 회둜 λ‚΄μ—μ„œ 전달될 λ•Œ ν™œλ™ μ „μ••μ˜ ν˜•νƒœλ‘œ μ „λ‹¬λ˜κΈ° λ•Œλ¬Έμ— ν™œλ™ μ „μ••μ˜ λ°œμƒ λΉˆλ„ 및 νŒ¨ν„΄ 쑰절 양상에 λŒ€ν•œ μ΄ν•΄λŠ” μ†Œλ‡Œ μš΄λ™ ν•™μŠ΅μ˜ 기전을 λ°νžˆλŠ” 데에 μ€‘μš”ν•˜λ‹€. λ³Έ λ°•μ‚¬ν•™μœ„ λ…Όλ¬Έμ—μ„œλŠ” λ¨Όμ € μ†Œλ‡Œ 퍼킨지 μ„Έν¬μ˜ λ‚΄μž¬μ  ν₯뢄성을 μ‘°μ ˆν•˜λŠ” μ—¬λŸ¬κ°€μ§€ 이온 ν†΅λ‘œλ“€μ˜ νŠΉμ„±μ— λŒ€ν•΄ μ •λ¦¬ν•˜κ³  더 λ‚˜μ•„κ°€ λ‚΄μž¬μ  ν₯λΆ„μ„± κ°€μ†Œμ„±μ˜ κΈ°μ „ 및 생리학적 의의λ₯Ό μ œμ‹œν•˜μ˜€λ‹€. μ†Œλ‡Œ 퍼킨지 μ„Έν¬μ˜ ν₯뢄성은 ν™œλ™-의쑴적 κ°€μ†Œμ„±μ„ λ³΄μ΄λŠ”λ°, μ‹œλƒ…μŠ€μ˜ ν™œλ™μ΄ μ•„λ‹Œ μ†Œλ‡Œ 회둜 ν™œλ™μ„±μ˜ μž₯기적인 변화에 λŒ€μ‘ν•˜μ—¬ λ‚˜νƒ€λ‚  수 μžˆλ‹€. μ†Œλ‡Œ 회둜의 ν™œλ™μ„ 2일 κ°„μ˜ tetrodotoxin (TTX, 1Β΅M) 처리λ₯Ό 톡해 μ €ν•΄ν•˜μ˜€μ„ λ•Œ 과뢄극에 μ˜ν•΄ λ°œμƒν•˜λŠ” λ‚΄ν–₯μ „λ₯˜ (Ih) 증가λ₯Ό ν†΅ν•œ μ†Œλ‡Œ 퍼킨지 μ„Έν¬μ˜ ν₯뢄성이 κ°μ†Œλ˜λŠ” 것을 전기생리학적 기둝을 톡해 κ΄€μ°°ν•˜μ˜€λ‹€. μ΄λŸ¬ν•œ μž₯기적인 μ†Œλ‡Œ 회둜의 ν™œλ™μ„± 변화에 μ˜ν•œ 퍼킨지 μ„Έν¬μ˜ λ‚΄μž¬μ  ν₯λΆ„μ„± κ°μ†Œμ˜ 세포생리학적 κΈ°μ „μœΌλ‘œμ„œ λŒ€μ‚¬μ„± κΈ€λ£¨νƒ€λ©”μ΄νŠΈ 수용체의 κΈΈν•­μ œ-λΉ„μ˜μ‘΄μ μΈ ν™œλ™μ„± 증가 및 그둜 μΈν•œ PKA의 증가에 μ˜ν•΄ λ°œμƒν•¨μ„ 생화학 및 전기생리학적 방법을 톡해 규λͺ…ν•˜μ˜€λ‹€. 이처럼 μ†Œλ‡Œ 퍼킨지 μ„Έν¬μ˜ λ‚΄μž¬μ  ν₯뢄성은 μ†Œλ‡Œ 회둜 λ‚΄μ—μ„œ μ—­λ™μ μœΌλ‘œ μ‘°μ ˆλ˜μ–΄ μ†Œλ‡Œ κΈ°λŠ₯을 μ‘°μ ˆν•œλ‹€. 더 λ‚˜μ•„κ°€ 퍼킨지 μ„Έν¬μ˜ ν₯λΆ„μ„± 쑰절과 μ†Œλ‡Œ-κΈ°μ–΅ν˜•μ„±κ³Όμ˜ 관계성을 κ²€μ¦ν•˜κΈ° μœ„ν•΄ μ†Œλ‡Œ-ν•™μŠ΅μ˜ 세포생리학적 κΈ°μ „μœΌλ‘œ μ•Œλ €μ ΈμžˆλŠ” 퍼킨지 세포 μ‹œλƒ…μŠ€ μž₯κΈ°μ €ν•˜ μœ λ„ ν›„ ν₯λΆ„μ„±μ˜ λ³€ν™”λ₯Ό κ΄€μ°°ν•˜μ˜€λ‹€. ν₯λ―Έλ‘­κ²Œλ„ 퍼킨지 μ„Έν¬μ˜ λ‚΄μž¬μ  ν₯λΆ„μ„± μ—­μ‹œ μ‹œλƒ…μŠ€ κ°€μ†Œμ„±κ³Ό λ§ˆμ°¬κ°€μ§€λ‘œ ν‰ν–‰μ„¬μœ μ™€ λ“±λ°˜μ„¬μœ μ˜ ν™œμ„±μ„ 톡해 κ°€μ†Œμ„±μ„ λ³΄μ΄λŠ”λ° 이 ν₯λΆ„μ„±μ˜ κ°€μ†Œμ„±μ€ λŒ€μ‚¬μ„± κΈ€λ£¨νƒ€λ©”μ΄νŠΈ 수용체, PKC 그리고 CaMKII와 같은 μ‹œλƒ…μŠ€ μž₯κΈ° μ €ν•˜λ₯Ό μ•ΌκΈ°ν•˜λŠ” 세포 λ‚΄ μ‹ ν˜Έμ „λ‹¬κΈ°μ „μ„ ν•„μš”λ‘œ ν•œλ‹€. μ΄λŸ¬ν•œ μ‹€ν—˜κ²°κ³Όλ₯Ό 톡해 μ‹œλƒ…μŠ€ μž₯κΈ°μ €ν•˜κ°€ λ°œμƒν•  λ•Œ μ†Œλ‡Œ 퍼킨지 μ„Έν¬μ˜ λ‚΄μž¬μ  ν₯λΆ„μ„± μ—­μ‹œ 같이 κ°μ†Œν•˜μ—¬ μ†Œλ‡Œ μš΄λ™ μ‹œ μ†Œλ‡Œ ν”Όμ§ˆμ˜ 좜λ ₯이 크게 κ°μ†Œν•¨μ„ μ˜ˆμƒν•  수 μžˆλ‹€. μ‹€μ œλ‘œ μ†Œλ‡Œ 퍼킨지 μ„Έν¬μ˜ μ‹ κ²½κ°€μ†Œμ„±μ„ μœ λ„ν•œ ν›„ ν‰ν–‰μ„¬μœ λ₯Ό μžκ·Ήν•˜μ—¬ λ‚˜νƒ€λ‚˜λŠ” 퍼킨지 μ„Έν¬μ˜ ν™œλ™ μ „μ•• λ°œμƒ λΉˆλ„λ₯Ό μΈ‘μ •ν•΄ λ³Έ κ²°κ³Ό, μ‹œλƒ…μŠ€ μž₯κΈ°μ €ν•˜μ™€ ν₯λΆ„μ„±μ˜ μž₯κΈ°μ €ν•˜κ°€ ν•¨κ»˜ λ°œμƒν–ˆμ„ λ•Œμ—λ§Œ μ†Œλ‡Œ 퍼킨지 μ„Έν¬μ˜ 좜λ ₯이 μœ μ˜λ―Έν•˜κ²Œ κ°μ†Œν•˜λŠ” 것을 κ΄€μ°°ν•˜μ˜€λ‹€. 특히 퍼킨지 μ„Έν¬μ˜ ν™œλ™-의쑴적 ν₯λΆ„μ„±μ˜ κ°€μ†Œμ„±μ€ μ‹œλƒ…μŠ€ κ°€μ†Œμ„±κ³Ό λ§ˆμ°¬κ°€μ§€λ‘œ νŠΉμ • μˆ˜μƒλŒκΈ° 가지 특이적으둜 λ°œμƒν•¨μ„ κ΄€μ°°ν•˜μ˜€λ‹€. 이λ₯Ό 톡해 퍼킨지 μ„Έν¬μ˜ μ‹œλƒ…μŠ€ κ°€μ†Œμ„±κ³Ό ν₯λΆ„μ„± κ°€μ†Œμ„±μ˜ 유기적인 연합을 톡해 μ†Œλ‡Œ 퍼킨지 μ„Έν¬μ˜ 좜λ ₯μ‹ ν˜Έκ°€ μ‘°μ ˆλ˜μ–΄ μ†Œλ‡Œ-μš΄λ™ν•™μŠ΅μ„ μ‘°μ ˆν•¨μ„ μ•Œ 수 μžˆλ‹€. 결둠적으둜 λ³Έ λ°•μ‚¬ν•™μœ„ λ…Όλ¬Έμ˜ 연ꡬ결과듀은 μ†Œλ‡Œ 퍼킨지 μ„Έν¬μ˜ 좜λ ₯은 퍼킨지 μ„Έν¬μ˜ μ‹œλƒ…μŠ€ κ°€μ†Œμ„± ν˜Ήμ€ ν₯λΆ„μ„±μ˜ 쑰절과 λΉ„μ„ ν˜•κ΄€κ³„λ₯Ό 보이며 μ΄λŸ¬ν•œ μ‹œλƒ…μŠ€ κ°€μ†Œμ„±κ³Ό λ‚΄μž¬μ  κ°€μ†Œμ„±μ˜ μ‹œλ„ˆμ§€λŠ” μ†Œλ‡Œ 정보 μ €μž₯ λŠ₯λ ₯을 κ·ΉλŒ€ν™”ν•˜μ—¬ μ†Œλ‡Œ κΈ°λŠ₯ 쑰절 및 정보저μž₯에 μ€‘μš”ν•œ 역할을 λ‹΄λ‹Ήν•˜κ³  μžˆμŒμ„ μ œμ‹œν•œλ‹€.Preface Abstract General introduction Chapter 1. Summary of the previous literatures and further implication for physiological significance of the intrinsic plasticity in the cerebellar Purkinje cells Summary. 1.1 Ion channels and spiking activity of the cerebellar Purkinje cells 1.1.1 Voltage-gated Na+ channels 1.1.2 Voltage-gated K+ channels and Ca2+-activated K+ channels 1.2 Activity-dependent plasticity of intrinsic excitability through ion channel modulation 1.2.1 Activity-dependent plasticity of intrinsic. excitability through ion channel 1.2.2 Possible mechanisms for LTD-IE. 1.2.3 Upside down: to what extent does bidirectional intrinsic plasticity in. the cerebellar dependent-motor learning do? 1.3 The further implication of intrinsic plasticity in the memory circuits. Chapter 2. Type 1 metabotropic glutamate receptor mediates homeostatic control of intrinsic excitability through hyperpolarization-activated current in cerebellar Purkinje cells Introduction Material and Method Results 2.1 Chronic activity-deprivation reduces intrinsic excitability of the cerebellar. Purkinje cells 35 2.2 Homeostatic intrinsic plasticity of the cerebellar Purkinje cells is mediated activity-dependent modulation of Ih 2.3 Homeostatic intrinsic plasticity of the cerebellar Purkinje cells requires agonist-independent action of mGluR1 2.4 Homeostatic intrinsic plasticity of the cerebellar Purkinje cells is mediated. PKA activity Discussion Chapter 3. Long-Term Depression of Intrinsic Excitability Accompanied by Synaptic Depression in Cerebellar Purkinje Cells Introduction Material and Method Results 3.1 LTD of intrinsic excitability of PC accompanied by PF-PC LTD 3.2 LTD-IE has different developing kinetics from synaptic LTD 3.3 LTD-IE was not reversed by subsequent LTP-IE induction 3.4 The number of recruited synapses were not correlated to the magnitude of the neuronal 3.5 Information processing after LTD induction LTD-IE was not. reversed by subsequent LTP-IE induction 3.6 LTD-IE required the Ca2+-signal but not depended on the Ca2+-activated K+ channels Discussion Chapter 4. Synergies between synaptic depression and intrinsic plasticity of the cerebellar Purkinje cells determining the Purkinje cell output Introduction Material and Method Restuls 4.1 Timing rules of intrinsic plasticity of floccular PCs 87 4.2 Intrinsic plasticity shares intracellular signaling for PF-PC LTD 4.3 Conditioned PF branches contributing to robust reduction of spike output of the PCs 4.4 Sufficient changes in spiking output require both of plasticity, synaptic and. intrinsic plasticity 4.5 Supralinearity of spiking output coordination after induction of PC plasticity Discussion Bibliography Abstract in Korean AcknowledgementDocto
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