116,537 research outputs found

    Intrinsic plasticity complements long-term potentiation in parallel fiber input gain control in cerebellar Purkinje cells

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    Synaptic gain control and information storage in neural networks are mediated by alterations in synaptic transmission, such as in long-term potentiation (LTP). Here,weshowusingboth in vitroandin vivo recordingsfromthe rat cerebellum that tetanization protocols for the induction of LTP at parallel fiber (PF)-to-Purkinje cell synapsescanalsoevokeincreases in intrinsic excitability. Thisformof intrinsic plasticity shares with LTP a requirement for the activation of protein phosphatases 1, 2A, and 2B for induction. Purkinje cell intrinsic plasticity resembles CA1 hippocampal pyramidal cell intrinsic plasticity in that it requires activity of protein kinase A(PKA) and casein kinase 2 (CK2) and is mediated by a downregulation of SK-type calcium-sensitive K conductances. In addition, Purkinje cell intrinsic plasticity similarly results in enhanced spine calcium signaling. However, there are fundamental differences: first, while in the hippocampus increases in excitability result in a higher probability for LTP induction, intrinsic plasticity in Purkinj

    Learning intrinsic excitability in medium spiny neurons

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    We present an unsupervised, local activation-dependent learning rule for intrinsic plasticity (IP) which affects the composition of ion channel conductances for single neurons in a use-dependent way. We use a single-compartment conductance-based model for medium spiny striatal neurons in order to show the effects of parametrization of individual ion channels on the neuronal activation function. We show that parameter changes within the physiological ranges are sufficient to create an ensemble of neurons with significantly different activation functions. We emphasize that the effects of intrinsic neuronal variability on spiking behavior require a distributed mode of synaptic input and can be eliminated by strongly correlated input. We show how variability and adaptivity in ion channel conductances can be utilized to store patterns without an additional contribution by synaptic plasticity (SP). The adaptation of the spike response may result in either "positive" or "negative" pattern learning. However, read-out of stored information depends on a distributed pattern of synaptic activity to let intrinsic variability determine spike response. We briefly discuss the implications of this conditional memory on learning and addiction.Comment: 20 pages, 8 figure

    Relationship Between Learning-Related Synaptic and Intrinsic Plasticity Within Lateral Amygdala

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    A central question in neuroscience is to determine the mechanisms that govern formation, storage and modulation of memories. Determining these mechanisms would allow us to facilitate new memory formation as in the case of aging-related cognitive decline or weaken preexisting pathological memories such as traumatic memories and cue-induced drug craving. Pharmacological and genetic manipulation of intrinsic neuronal excitability has been demonstrated to impact the strength of memory formation, allocation of memories, and modulation of memories through retrieval and reconsolidation-dependent processes. In addition to experimental manipulations of intrinsic excitability, intrinsic plasticity, a change in neuronal intrinsic excitability, can be brought about by behavioral means such as learning. Indeed, learning-related intrinsic plasticity has been observed in many brain structures following acquisition of a variety of learning paradigms. Despite its ubiquitous nature, little is known about the functional significance of learning-induced intrinsic plasticity. Using the well-characterized lateral amygdala-dependent auditory fear conditioning as a behavioral paradigm, the current experiments investigated the time course and relationship between intrinsic and synaptic plasticity. We found that learning-related changes in amygdala intrinsic excitability were transient and were no longer evident 10 days following fear conditioning. We also found that fear learning related synaptic plasticity was evident up to 24hr following fear conditioning but not 4 days later. Finally, we demonstrate that the intrinsic excitability changes are evident in many of the same neurons that are undergoing synaptic facilitation immediately following fear conditioning. These data demonstrate that learning related intrinsic and synaptic changes are transient and co-localized to the same neurons. These data demonstrate that memory encoding neurons are more excitable, thus more likely to capture new memories for a time after the learning event

    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

    ์†Œ๋‡Œ ํผํ‚จ์ง€ ์„ธํฌ ๋‚ด์žฌ์  ํฅ๋ถ„์„ฑ์˜ ํ™œ๋™-์˜์กด์  ์กฐ์ ˆ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ์˜๊ณผํ•™๊ณผ, 2019. 2. ๊น€์ƒ์ •.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.์ƒ๋ช…์ฒด๋Š” ๋Š์ž„์—†์ด ์ฃผ๋ณ€ํ™˜๊ฒฝ์— ๋ฐ˜์‘ํ•˜์—ฌ ํ–‰๋™์„ ์ˆ˜์ •ํ•˜๋ฉฐ ์ด๋Ÿฌํ•œ ์ ์‘์€ ๋ณ€ํ™”ํ•˜๋Š” ํ™˜๊ฒฝ์—์„œ ์ƒ์กด์— ํ•„์ˆ˜์ ์ด๋‹ค. ์†Œ๋‡Œ-์šด๋™ ํ•™์Šต์€ ๋Œ€ํ‘œ์ ์ธ ์ ์‘ ํ–‰๋™์˜ ์˜ˆ์ด๋‹ค. ๋‹ค์–‘ํ•œ ๊ฐ๊ฐ ์‹ ํ˜ธ๋“ค์ด ์†Œ๋‡Œ๋กœ ์ „๋‹ฌ๋˜์–ด ์ฒ˜๋ฆฌ๋œ ํ›„ ์†Œ๋‡Œ ์ถœ๋ ฅ์„ ํ†ตํ•ด ์šด๋™ ํ˜‘์‘์ด ์ด๋ฃจ์–ด์ง„๋‹ค. ์ด๋Ÿฌํ•œ ์†Œ๋‡Œ-์šด๋™ ํ•™์Šต ๋ฐ ์†Œ๋‡Œ ๊ธฐ๋Šฅ ์กฐ์ ˆ์˜ ์„ธํฌ ์ƒ๋ฆฌํ•™์  ๊ธฐ์ „์œผ๋กœ ์†Œ๋‡Œ ํผํ‚จ์ง€ ์„ธํฌ์˜ ์‹œ๋ƒ…์Šค ์žฅ๊ธฐ์ €ํ•˜๊ฐ€ ์˜ค๋žซ๋™์•ˆ ์ฃผ๋ชฉ๋ฐ›์•˜๋‹ค. ํผํ‚จ์ง€ ์„ธํฌ์˜ ์‹œ๋ƒ…์Šค ์žฅ๊ธฐ์ €ํ•˜๊ฐ€ ๋‚˜ํƒ€๋‚˜์ง€ ์•Š๋Š” ์œ ์ „์ž ๋ณ€ํ˜• ๋™๋ฌผ ๋ชจ๋ธ๋“ค์—์„œ ์†Œ๋‡Œ-์šด๋™ ํ•™์Šต์ด ์ •์ƒ์ ์œผ๋กœ ์ผ์–ด๋‚˜์ง€ ์•Š๋Š” ํ˜„์ƒ์ด ๊ด€์ฐฐ๋˜์—ˆ๊ธฐ ๋•Œ๋ฌธ์— ์‹œ๋ƒ…์Šค ์žฅ๊ธฐ์ €ํ•˜ ์ด๋ก ์€ ์˜ค๋žœ ์‹œ๊ฐ„ ์†Œ๋‡Œ-์šด๋™ ํ•™์Šต์˜ ๊ธฐ์ „์œผ๋กœ ์ง€์ง€ ๋ฐ›์•˜๋‹ค. ํ•˜์ง€๋งŒ ์ตœ๊ทผ 10๋…„ ๋™์•ˆ์˜ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋Š” ์‹œ๋ƒ…์Šค ์žฅ๊ธฐ์ €ํ•˜๋งŒ์œผ๋กœ ์†Œ๋‡Œ-์šด๋™ ํ•™์Šต ๋ฐ ๊ธฐ๋Šฅ ์กฐ์ ˆ์„ ์„ค๋ช…ํ•  ์ˆ˜ ์—†๋‹ค๊ณ  ๋ฐ˜๋ฐ•ํ•œ๋‹ค. ํŠนํžˆ ์†Œ๋‡Œ ํผํ‚จ์ง€ ์„ธํฌ๋Š” ์†Œ๋‡Œ ํ”ผ์งˆ๋กœ ์ „๋‹ฌ๋œ ๊ฐ๊ฐ์‹ ํ˜ธ๋ฅผ ์ฒ˜๋ฆฌํ•˜์—ฌ ์ถœ๋ ฅ์„ ๋‹ด๋‹นํ•˜๋Š” ์œ ์ผํ•œ ์‹ ๊ฒฝ์„ธํฌ์ด๋ฏ€๋กœ ์šด๋™ ํ•™์Šต ์ƒํ™ฉ์—์„œ ์†Œ๋‡Œ์˜ ์ถœ๋ ฅ์ด ์–ด๋–ป๊ฒŒ ์กฐ์ ˆ๋˜๋Š”์ง€๋ฅผ ์ดํ•ดํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๊ฒŒ ์ธ์‹๋˜์—ˆ๋‹ค. ๊ฐ๊ฐ ์‹ ํ˜ธ๊ฐ€ ์‹ ๊ฒฝ ํšŒ๋กœ ๋‚ด์—์„œ ์ „๋‹ฌ๋  ๋•Œ ํ™œ๋™ ์ „์••์˜ ํ˜•ํƒœ๋กœ ์ „๋‹ฌ๋˜๊ธฐ ๋•Œ๋ฌธ์— ํ™œ๋™ ์ „์••์˜ ๋ฐœ์ƒ ๋นˆ๋„ ๋ฐ ํŒจํ„ด ์กฐ์ ˆ ์–‘์ƒ์— ๋Œ€ํ•œ ์ดํ•ด๋Š” ์†Œ๋‡Œ ์šด๋™ ํ•™์Šต์˜ ๊ธฐ์ „์„ ๋ฐํžˆ๋Š” ๋ฐ์— ์ค‘์š”ํ•˜๋‹ค. ๋ณธ ๋ฐ•์‚ฌํ•™์œ„ ๋…ผ๋ฌธ์—์„œ๋Š” ๋จผ์ € ์†Œ๋‡Œ ํผํ‚จ์ง€ ์„ธํฌ์˜ ๋‚ด์žฌ์  ํฅ๋ถ„์„ฑ์„ ์กฐ์ ˆํ•˜๋Š” ์—ฌ๋Ÿฌ๊ฐ€์ง€ ์ด์˜จ ํ†ต๋กœ๋“ค์˜ ํŠน์„ฑ์— ๋Œ€ํ•ด ์ •๋ฆฌํ•˜๊ณ  ๋” ๋‚˜์•„๊ฐ€ ๋‚ด์žฌ์  ํฅ๋ถ„์„ฑ ๊ฐ€์†Œ์„ฑ์˜ ๊ธฐ์ „ ๋ฐ ์ƒ๋ฆฌํ•™์  ์˜์˜๋ฅผ ์ œ์‹œํ•˜์˜€๋‹ค. ์†Œ๋‡Œ ํผํ‚จ์ง€ ์„ธํฌ์˜ ํฅ๋ถ„์„ฑ์€ ํ™œ๋™-์˜์กด์  ๊ฐ€์†Œ์„ฑ์„ ๋ณด์ด๋Š”๋ฐ, ์‹œ๋ƒ…์Šค์˜ ํ™œ๋™์ด ์•„๋‹Œ ์†Œ๋‡Œ ํšŒ๋กœ ํ™œ๋™์„ฑ์˜ ์žฅ๊ธฐ์ ์ธ ๋ณ€ํ™”์— ๋Œ€์‘ํ•˜์—ฌ ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ๋‹ค. ์†Œ๋‡Œ ํšŒ๋กœ์˜ ํ™œ๋™์„ 2์ผ ๊ฐ„์˜ tetrodotoxin (TTX, 1ยตM) ์ฒ˜๋ฆฌ๋ฅผ ํ†ตํ•ด ์ €ํ•ดํ•˜์˜€์„ ๋•Œ ๊ณผ๋ถ„๊ทน์— ์˜ํ•ด ๋ฐœ์ƒํ•˜๋Š” ๋‚ดํ–ฅ์ „๋ฅ˜ (Ih) ์ฆ๊ฐ€๋ฅผ ํ†ตํ•œ ์†Œ๋‡Œ ํผํ‚จ์ง€ ์„ธํฌ์˜ ํฅ๋ถ„์„ฑ์ด ๊ฐ์†Œ๋˜๋Š” ๊ฒƒ์„ ์ „๊ธฐ์ƒ๋ฆฌํ•™์  ๊ธฐ๋ก์„ ํ†ตํ•ด ๊ด€์ฐฐํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ์žฅ๊ธฐ์ ์ธ ์†Œ๋‡Œ ํšŒ๋กœ์˜ ํ™œ๋™์„ฑ ๋ณ€ํ™”์— ์˜ํ•œ ํผํ‚จ์ง€ ์„ธํฌ์˜ ๋‚ด์žฌ์  ํฅ๋ถ„์„ฑ ๊ฐ์†Œ์˜ ์„ธํฌ์ƒ๋ฆฌํ•™์  ๊ธฐ์ „์œผ๋กœ์„œ ๋Œ€์‚ฌ์„ฑ ๊ธ€๋ฃจํƒ€๋ฉ”์ดํŠธ ์ˆ˜์šฉ์ฒด์˜ ๊ธธํ•ญ์ œ-๋น„์˜์กด์ ์ธ ํ™œ๋™์„ฑ ์ฆ๊ฐ€ ๋ฐ ๊ทธ๋กœ ์ธํ•œ PKA์˜ ์ฆ๊ฐ€์— ์˜ํ•ด ๋ฐœ์ƒํ•จ์„ ์ƒํ™”ํ•™ ๋ฐ ์ „๊ธฐ์ƒ๋ฆฌํ•™์  ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ๊ทœ๋ช…ํ•˜์˜€๋‹ค. ์ด์ฒ˜๋Ÿผ ์†Œ๋‡Œ ํผํ‚จ์ง€ ์„ธํฌ์˜ ๋‚ด์žฌ์  ํฅ๋ถ„์„ฑ์€ ์†Œ๋‡Œ ํšŒ๋กœ ๋‚ด์—์„œ ์—ญ๋™์ ์œผ๋กœ ์กฐ์ ˆ๋˜์–ด ์†Œ๋‡Œ ๊ธฐ๋Šฅ์„ ์กฐ์ ˆํ•œ๋‹ค. ๋” ๋‚˜์•„๊ฐ€ ํผํ‚จ์ง€ ์„ธํฌ์˜ ํฅ๋ถ„์„ฑ ์กฐ์ ˆ๊ณผ ์†Œ๋‡Œ-๊ธฐ์–ตํ˜•์„ฑ๊ณผ์˜ ๊ด€๊ณ„์„ฑ์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด ์†Œ๋‡Œ-ํ•™์Šต์˜ ์„ธํฌ์ƒ๋ฆฌํ•™์  ๊ธฐ์ „์œผ๋กœ ์•Œ๋ ค์ ธ์žˆ๋Š” ํผํ‚จ์ง€ ์„ธํฌ ์‹œ๋ƒ…์Šค ์žฅ๊ธฐ์ €ํ•˜ ์œ ๋„ ํ›„ ํฅ๋ถ„์„ฑ์˜ ๋ณ€ํ™”๋ฅผ ๊ด€์ฐฐํ•˜์˜€๋‹ค. ํฅ๋ฏธ๋กญ๊ฒŒ๋„ ํผํ‚จ์ง€ ์„ธํฌ์˜ ๋‚ด์žฌ์  ํฅ๋ถ„์„ฑ ์—ญ์‹œ ์‹œ๋ƒ…์Šค ๊ฐ€์†Œ์„ฑ๊ณผ ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ ํ‰ํ–‰์„ฌ์œ ์™€ ๋“ฑ๋ฐ˜์„ฌ์œ ์˜ ํ™œ์„ฑ์„ ํ†ตํ•ด ๊ฐ€์†Œ์„ฑ์„ ๋ณด์ด๋Š”๋ฐ ์ด ํฅ๋ถ„์„ฑ์˜ ๊ฐ€์†Œ์„ฑ์€ ๋Œ€์‚ฌ์„ฑ ๊ธ€๋ฃจํƒ€๋ฉ”์ดํŠธ ์ˆ˜์šฉ์ฒด, PKC ๊ทธ๋ฆฌ๊ณ  CaMKII์™€ ๊ฐ™์€ ์‹œ๋ƒ…์Šค ์žฅ๊ธฐ ์ €ํ•˜๋ฅผ ์•ผ๊ธฐํ•˜๋Š” ์„ธํฌ ๋‚ด ์‹ ํ˜ธ์ „๋‹ฌ๊ธฐ์ „์„ ํ•„์š”๋กœ ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์‹คํ—˜๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ์‹œ๋ƒ…์Šค ์žฅ๊ธฐ์ €ํ•˜๊ฐ€ ๋ฐœ์ƒํ•  ๋•Œ ์†Œ๋‡Œ ํผํ‚จ์ง€ ์„ธํฌ์˜ ๋‚ด์žฌ์  ํฅ๋ถ„์„ฑ ์—ญ์‹œ ๊ฐ™์ด ๊ฐ์†Œํ•˜์—ฌ ์†Œ๋‡Œ ์šด๋™ ์‹œ ์†Œ๋‡Œ ํ”ผ์งˆ์˜ ์ถœ๋ ฅ์ด ํฌ๊ฒŒ ๊ฐ์†Œํ•จ์„ ์˜ˆ์ƒํ•  ์ˆ˜ ์žˆ๋‹ค. ์‹ค์ œ๋กœ ์†Œ๋‡Œ ํผํ‚จ์ง€ ์„ธํฌ์˜ ์‹ ๊ฒฝ๊ฐ€์†Œ์„ฑ์„ ์œ ๋„ํ•œ ํ›„ ํ‰ํ–‰์„ฌ์œ ๋ฅผ ์ž๊ทนํ•˜์—ฌ ๋‚˜ํƒ€๋‚˜๋Š” ํผํ‚จ์ง€ ์„ธํฌ์˜ ํ™œ๋™ ์ „์•• ๋ฐœ์ƒ ๋นˆ๋„๋ฅผ ์ธก์ •ํ•ด ๋ณธ ๊ฒฐ๊ณผ, ์‹œ๋ƒ…์Šค ์žฅ๊ธฐ์ €ํ•˜์™€ ํฅ๋ถ„์„ฑ์˜ ์žฅ๊ธฐ์ €ํ•˜๊ฐ€ ํ•จ๊ป˜ ๋ฐœ์ƒํ–ˆ์„ ๋•Œ์—๋งŒ ์†Œ๋‡Œ ํผํ‚จ์ง€ ์„ธํฌ์˜ ์ถœ๋ ฅ์ด ์œ ์˜๋ฏธํ•˜๊ฒŒ ๊ฐ์†Œํ•˜๋Š” ๊ฒƒ์„ ๊ด€์ฐฐํ•˜์˜€๋‹ค. ํŠนํžˆ ํผํ‚จ์ง€ ์„ธํฌ์˜ ํ™œ๋™-์˜์กด์  ํฅ๋ถ„์„ฑ์˜ ๊ฐ€์†Œ์„ฑ์€ ์‹œ๋ƒ…์Šค ๊ฐ€์†Œ์„ฑ๊ณผ ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ ํŠน์ • ์ˆ˜์ƒ๋Œ๊ธฐ ๊ฐ€์ง€ ํŠน์ด์ ์œผ๋กœ ๋ฐœ์ƒํ•จ์„ ๊ด€์ฐฐํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ํผํ‚จ์ง€ ์„ธํฌ์˜ ์‹œ๋ƒ…์Šค ๊ฐ€์†Œ์„ฑ๊ณผ ํฅ๋ถ„์„ฑ ๊ฐ€์†Œ์„ฑ์˜ ์œ ๊ธฐ์ ์ธ ์—ฐํ•ฉ์„ ํ†ตํ•ด ์†Œ๋‡Œ ํผํ‚จ์ง€ ์„ธํฌ์˜ ์ถœ๋ ฅ์‹ ํ˜ธ๊ฐ€ ์กฐ์ ˆ๋˜์–ด ์†Œ๋‡Œ-์šด๋™ํ•™์Šต์„ ์กฐ์ ˆํ•จ์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ๋ณธ ๋ฐ•์‚ฌํ•™์œ„ ๋…ผ๋ฌธ์˜ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋“ค์€ ์†Œ๋‡Œ ํผํ‚จ์ง€ ์„ธํฌ์˜ ์ถœ๋ ฅ์€ ํผํ‚จ์ง€ ์„ธํฌ์˜ ์‹œ๋ƒ…์Šค ๊ฐ€์†Œ์„ฑ ํ˜น์€ ํฅ๋ถ„์„ฑ์˜ ์กฐ์ ˆ๊ณผ ๋น„์„ ํ˜•๊ด€๊ณ„๋ฅผ ๋ณด์ด๋ฉฐ ์ด๋Ÿฌํ•œ ์‹œ๋ƒ…์Šค ๊ฐ€์†Œ์„ฑ๊ณผ ๋‚ด์žฌ์  ๊ฐ€์†Œ์„ฑ์˜ ์‹œ๋„ˆ์ง€๋Š” ์†Œ๋‡Œ ์ •๋ณด ์ €์žฅ ๋Šฅ๋ ฅ์„ ๊ทน๋Œ€ํ™”ํ•˜์—ฌ ์†Œ๋‡Œ ๊ธฐ๋Šฅ ์กฐ์ ˆ ๋ฐ ์ •๋ณด์ €์žฅ์— ์ค‘์š”ํ•œ ์—ญํ• ์„ ๋‹ด๋‹นํ•˜๊ณ  ์žˆ์Œ์„ ์ œ์‹œํ•œ๋‹ค.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

    Biphasic Somatic A-Type K+ Channel Downregulation Mediates Intrinsic Plasticity in Hippocampal CA1 Pyramidal Neurons

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    Since its original description, the induction of synaptic long-term potentiation (LTP) has been known to be accompanied by a lasting increase in the intrinsic excitability (intrinsic plasticity) of hippocampal neurons. Recent evidence shows that dendritic excitability can be enhanced by an activity-dependent decrease in the activity of A-type K+ channels. In the present manuscript, we examined the role of A-type K+ channels in regulating intrinsic excitability of CA1 pyramidal neurons of the hippocampus after synapse-specific LTP induction. In electrophysiological recordings we found that LTP induced a potentiation of excitability which was accompanied by a two-phased change in A-type K+ channel activity recorded in nucleated patches from organotypic slices of rat hippocampus. Induction of LTP resulted in an immediate but short lasting hyperpolarization of the voltage-dependence of steady-state A-type K+ channel inactivation along with a progressive, long-lasting decrease in peak A-current density. Blocking clathrin-mediated endocytosis prevented the A-current decrease and most measures of intrinsic plasticity. These results suggest that two temporally distinct but overlapping mechanisms of A-channel downregulation together contribute to the plasticity of intrinsic excitability. Finally we show that intrinsic plasticity resulted in a global enhancement of EPSP-spike coupling

    Control over stress induces plasticity of individual prefrontal cortical neurons: A conductance-based neural simulation

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    Behavioral control over stressful stimuli induces resilience to future conditions when control is lacking. The medial prefrontal cortex(mPFC) is a critically important brain region required for plasticity of stress resilience. We found that control over stress induces plasticity of the intrinsic voltage-gated conductances of pyramidal neurons in the PFC. To gain insight into the underlying biophysical mechanisms of this plasticity we used the conductance- based neural simulation software tool, NEURON, to model the increase in membrane excitability associated with resilience to stress. A ball and stick multicompartment conductance-based model was used to realistically fit passive and active data traces from prototypical pyramidal neurons in neurons in rats with control over tail shock stress and those lacking control. The results indicate that the plasticity of membrane excitability associated with control over stress can be attributed to an increase in Na+ and Ca2+ T-type conductances and an increase in the leak conductance. Using simulated dendritic synaptic inputs we observed an increase in excitatory postsynaptic summation and amplification resulting in elevated action potential output. This realistic simulation suggests that control over stress enhances the output of the PFC and offers specific testable hypotheses to guide future electrophysiological mechanistic studies in animal models of resilience and vulnerability to stress

    Microglial subtypes: diversity within the microglial community

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    Microglia are brain-resident macrophages forming the first active immune barrier in the central nervous system. They fulfill multiple functions across development and adulthood and under disease conditions. Current understanding revolves around microglia acquiring distinct phenotypes upon exposure to extrinsic cues in their environment. However, emerging evidence suggests that microglia display differences in their functions that are not exclusively driven by their milieu, rather by the unique properties these cells possess. This microglial intrinsic heterogeneity has been largely overlooked, favoring the prevailing view that microglia are a single-cell type endowed with spectacular plasticity, allowing them to acquire multiple phenotypes and thereby fulfill their numerous functions in health and disease. Here, we review the evidence that microglia might form a community of cells in which each member (or "subtype") displays intrinsic properties and performs unique functions. Distinctive features and functional implications of several microglial subtypes are considered, across contexts of health and disease. Finally, we suggest that microglial subtype categorization shall be based on function and we propose ways for studying them. Hence, we advocate that plasticity (reaction states) and diversity (subtypes) should both be considered when studying the multitasking microglia.Espaรฑa, Ministerio de Ciencia, Innovaciรณn y Universidades FEDER y UE RTI2018-098645-B-10
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