176 research outputs found

    Information processing in dissociated neuronal cultures of rat hippocampal neurons

    Get PDF
    One of the major aims of Systems Neuroscience is to understand how the nervous system transforms sensory inputs into appropriate motor reactions. In very simple cases sensory neurons are immediately coupled to motoneurons and the entire transformation becomes a simple reflex, in which a noxious signal is immediately transformed into an escape reaction. However, in the most complex behaviours, the nervous system seems to analyse in detail the sensory inputs and is performing some kind of information processing (IP). IP takes place at many different levels of the nervous system: from the peripheral nervous system, where sensory stimuli are detected and converted into electrical pulses, to the central nervous system, where features of sensory stimuli are extracted, perception takes place and actions and motions are coordinated. Moreover, understanding the basic computational properties of the nervous system, besides being at the core of Neuroscience, also arouses great interest even in the field of Neuroengineering and in the field of Computer Science. In fact, being able to decode the neural activity can lead to the development of a new generation of neuroprosthetic devices aimed, for example, at restoring motor functions in severely paralysed patients (Chapin, 2004). On the other side, the development of Artificial Neural Networks (ANNs) (Marr, 1982; Rumelhart & McClelland, 1988; Herz et al., 1981; Hopfield, 1982; Minsky & Papert, 1988) has already proved that the study of biological neural networks may lead to the development and to the design of new computing algorithms and devices. All nervous systems are based on the same elements, the neurons, which are computing devices which, compared to silicon components, are much slower and much less reliable. How are nervous systems of all living species able to survive being based on slow and poorly reliable components? This obvious and na\uefve question is equivalent to characterizing IP in a more quantitative way. In order to study IP and to capture the basic computational properties of the nervous system, two major questions seem to arise. Firstly, which is the fundamental unit of information processing: 2 single neurons or neuronal ensembles? Secondly, how is information encoded in the neuronal firing? These questions - in my view - summarize the problem of the neural code. The subject of my PhD research was to study information processing in dissociated neuronal cultures of rat hippocampal neurons. These cultures, with random connections, provide a more general view of neuronal networks and assemblies, not depending on the circuitry of a neuronal network in vivo, and allow a more detailed and careful experimental investigation. In order to record the activity of a large ensemble of neurons, these neurons were cultured on multielectrode arrays (MEAs) and multi-site stimulation was used to activate different neurons and pathways of the network. In this way, it was possible to vary the properties of the stimulus applied under a controlled extracellular environment. Given this experimental system, my investigation had two major approaches. On one side, I focused my studies on the problem of the neural code, where I studied in particular information processing at the single neuron level and at an ensemble level, investigating also putative neural coding mechanisms. On the other side, I tried to explore the possibility of using biological neurons as computing elements in a task commonly solved by conventional silicon devices: image processing and pattern recognition. The results reported in the first two chapters of my thesis have been published in two separate articles. The third chapter of my thesis represents an article in preparation

    Excitatory postsynaptic potentials in rat neocortical neurons in vitro. III. Effects of a quinoxalinedione non-NMDA receptor antagonist

    Get PDF
    1. Intracellular microelectrodes were used to obtain recordings from neurons in layer II/III of rat frontal cortex. A bipolar electrode positioned in layer IV of the neocortex was used to evoke postsynaptic potentials. Graded series of stimulation were employed to selectively activate different classes of postsynaptic responses. The sensitivity of postsynaptic potentials and iontophoretically applied neurotransmitters to the non-N-methyl-D-asparate (NMDA) antagonist 6-cyano-7-nitroquinoxaline-2,3-dione (CNQX) was examined. 2. As reported previously, low-intensity electrical stimulation of cortical layer IV evoked short-latency early excitatory postsynaptic potentials (eEPSPs) in layer II/III neurons. CNQX reversibly antagonized eEPSPs in a dose-dependent manner. Stimulation at intensities just subthreshold for activation of inhibitory postsynaptic potentials (IPSPs) produced long-latency (10 to 40-ms) EPSPs (late EPSPs or 1EPSPs). CNQX was effective in blocking 1EPSPs. 3. With the use of stimulus intensities at or just below threshold for evoking an action potential, complex synaptic potentials consisting of EPSP-IPSP sequences were observed. Both early, Cl(-)-dependent and late, K(+)-dependent IPSPs were reduced by CNQX. This effect was reversible on washing. This disinhibition could lead to enhanced excitability in the presence of CNQX. 4. Iontophoretic application of quisqualate produced a membrane depolarization with superimposed action potentials, whereas NMDA depolarized the membrane potential and evoked bursts of action potentials. At concentrations up to 5 microM, CNQX selectively antagonized quisqualate responses. NMDA responses were reduced by 10 microM CNQX. D-Serine (0.5-2 mM), an agonist at the glycine regulatory site on the NMDA receptor, reversed the CNQX depression of NMDA responses

    ํ”์  ๊ณตํฌ ์กฐ๊ฑดํ™”์™€ ๋งฅ๋ฝ ๊ณตํฌ ๋ถ„๋ณ„์˜ ์‹œ๋ƒ…์Šค ๊ธฐ์ „

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ์˜๊ณผํ•™๊ณผ, 2023. 2. ์ด์„ํ˜ธ.Learning the association between aversive events and environmental stimuli is essential for survival. Fear conditioning, a basic form of associative learning, is one of the widely studied paradigms in behavioral psychology. Fear conditioning is divided into cued and contextual fear conditioning (CFC). Depending on the absence or presence of a time interval between conditioned stimulus (CS) and unconditioned stimulus (US), cued fear conditioning is further subdivided into delayed fear conditioning (DFC) and traced fear conditioning (TFC), respectively. Amygdala is a center for associative memory formation and expression of fear response in all forms of fear conditioning. Whereas the amygdala is sufficient for DFC, CFC and TFC requires contributions of other cortical regions such as the hippocampus, prefrontal cortex (PFC) and retrosplenial cortex. Thus, CFC and TFC provide invaluable opportunities for studying cortical functions. Most previous studies have focused on the neuronal correlates of fear conditioning, but it has not previously been investigated whether synapse specific dysfunction of plasticity has any effects on the behavior. In this thesis, I challenged this issue by investigating the behavioral consequences of dysfunction of plasticity at layer 2/3 (L2/3) to layer 5 (L5) pyramidal cell synapses in the prelimbic (PL) area of the medial prefrontal cortex (mPFC) and perforant pathway (PP) synapses in the hippocampal CA3 and in rodents. Sustained increased activity in PFC during trace interval in TFC is essential for acquisition of trace fear memory. However, the neurophysiological mechanisms underlying increased activity are poorly understood. Post-tetanic potentiation (PTP) was proposed as short-term plasticity that might mediate the generation of sustained increased activity during working memory. I examined the neurobiological mechanism of PTP in the PL, and tested whether PTP plays a role in sustained increased activity and TFC. Using optogenetic stimulation, I stimulated afferent fibers to L5 pyramidal neurons (PNs) in cell-type and layer-specific manner. I found that PTP was induced in the L5 corticopontine (Cpn) PNs at the synapses made by L2/3 PNs and L5 commissural (COM) PNs, but not in the L5 COM PNs. While PTP at both synapse types onto Cpn cells was inhibited by protein kinase C inhibitor, tetraphenylphosphonium (TPP), a mitochondrial Na/Ca exchanger blocker, suppressed PTP only at L2/3-to-Cpn synapses. Studying the effect of TPP infusion into the PL area on TFC, I found that TPP did not affect the trace memory formation, but reduced the maintenance of the fear memory during fear memory extinction test. In vivo recordings revealed that c.a. 20% of PL-PNs exhibited sustained increased activity after the cessation of CS. The TPP infusion abolished such post-CS sustained increased activity during both conditioning and extinction training. In vivo calcium imaging of COM and CPn neurons during the tone test revealed that L5 CPn and COM neurons showed different proportions of activity patterns. The largest proportion of CPn cells, but not COM cells, belonged to a delay cell type that exhibited sustained increased activity from CS onset to the expected US timing. These results imply that PTP at L2/3-Cpn synapses is required for post-CS sustained increased activity during trace fear conditioning, and plays a role in maintenance of trace fear memory. The hippocampus is important for consolidation and retrieval of contextual fear. The network process that underlies formation of distinct ensembles representing two similar contexts is called pattern separation and is one of the important functions of the hippocampus. However, the network mechanisms underlying pattern separation of neuronal ensembles in CA3 is largely unknown. Kv1.2 expression in rodent CA3 pyramidal cells (CA3-PCs) is polarized to distal apical dendrites, and its downregulation specifically enhances dendritic responses to PP synaptic inputs. It has been previously shown that haploinsufficiency of Kv1.2 (Kcna2+/-) in CA3-PCs, but not Kv1.1 (Kcna1+/-), lowers the threshold for long-term potentiation (LTP) at PP-CA3 synapses, and that the Kcna2+/- mice are normal in discrimination of distinct contexts but impaired in discrimination of similar but slightly distinct contexts. I examined pattern separation of neuronal ensembles in CA3 and dentate gyrus (DG) that represent two similar contexts using in situ hybridization of immediate early genes: Homer1a and Arc. The size and overlap of CA3 ensembles activated by the first visit to the similar contexts were not different between wildtype and Kcna2+/- mice, but these ensemble parameters diverged over training days between genotypes, suggesting that abnormal plastic changes at PP-CA3 synapses of Kcna2+/- mice is responsible for the impaired pattern separation. Unlike CA3, DG ensembles were not different between two genotype mice. The DG ensembles were already separated on the first day, and their overlap did not further evolve. Eventually, the Kcna2+/- mice exhibited larger CA3 ensemble size and overlap upon retrieval of two contexts, compared to wildtype or Kcna1+/- mice. These results suggest that sparse LTP at PP-CA3 synapse probably supervised by mossy fiber inputs is essential for gradual pattern separation in CA3.ํ™˜๊ฒฝ์  ์ž๊ทน๊ณผ ํ˜์˜ค์Šค๋Ÿฌ์šด ์‚ฌ๊ฑด์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ํ•™์Šตํ•˜๋Š” ๊ฒƒ์€ ์ƒ์กด์— ํ•„์ˆ˜์ ์ธ ์š”์†Œ๋‹ค. ๊ณตํฌ ์กฐ๊ฑดํ™” (fear conditioning)๋Š” ์ƒ๊ด€ ํ•™์Šต์˜ ๊ธฐ์ดˆ์ ์ธ ํ˜•ํƒœ๋กœ ํ–‰๋™ ์‹ฌ๋ฆฌํ•™์—์„œ ๋งŽ์ด ์ด์šฉ๋˜๋Š” ํŒจ๋Ÿฌ๋‹ค์ž„ ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ๊ณตํฌ ์กฐ๊ฑดํ™”๋Š” ๋ฌธ๋งฅ ๊ณตํฌ ์กฐ๊ฑดํ™” (contextual fear conditioning; CFC)์™€ ๋‹จ์„œ ๊ณตํฌ ์กฐ๊ฑดํ™”๋กœ ๋‚˜๋‰œ๋‹ค. ๋˜ํ•œ ๋‹จ์„œ ๊ณตํฌ ์กฐ๊ฑดํ™”๋Š” ์กฐ๊ฑด ์ž๊ทน (conditioned stimulus; CS)๊ณผ ๋ฌด์กฐ๊ฑด ์ž๊ทน (unconditioned stimulus; US) ์‚ฌ์ด์˜ ์งง์€ ์‹œ๊ฐ„ ๊ฐ„๊ฒฉ์˜ ์œ ๋ฌด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ฐ๊ฐ ์ง€์—ฐ ๊ณตํฌ ์กฐ๊ฑดํ™” (delayed fear conditioning; DFC)๊ณผ ํ”์  ๊ณตํฌ ์กฐ๊ฑดํ™” (trace fear conditioning; TFC)๋กœ ์„ธ๋ถ„ํ™”๋œ๋‹ค. ํŽธ๋„์ฒด๋Š” ๋ชจ๋“  ์ข…๋ฅ˜์˜ ๊ณตํฌ ์กฐ๊ฑดํ™”์—์„œ ์ƒ๊ด€ ๊ธฐ์–ต์˜ ํ˜•์„ฑ๊ณผ ๊ณตํฌ ๋ฐ˜์‘์˜ ๋ฐœํ˜„์— ํ•ต์‹ฌ์ ์ธ ์—ญํ• ์„ ํ•œ๋‹ค. DFC์—์„œ๋Š” ๊ณตํฌ ๊ธฐ์–ต์˜ ํ•™์Šต ๋ฐ ๋ฐœํ˜„์ด ํŽธ๋„์ฒด๋งŒ์œผ๋กœ ์ถฉ๋ถ„ํ•œ๋ฐ ๋ฐ˜ํ•ด, TFC์™€ CFC์—์„œ๋Š” ํ•ด๋งˆ, ์ „์ „๋‘์—ฝ ๊ทธ๋ฆฌ๊ณ  ํŒฝ๋Œ€ํ›„ํ”ผ์งˆ (retrosplenial cortex)๊ณผ ๊ฐ™์€ ๋‹ค๋ฅธ ํ”ผ์งˆ ์˜์—ญ์˜ ๊ธฐ์—ฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ๋”ฐ๋ผ์„œ CFC์™€ TFC๋Š” ํ”ผ์งˆ ์˜์—ญ์˜ ๊ธฐ๋Šฅ์„ ์—ฐ๊ตฌํ•˜๋Š”๋ฐ ํฐ ๋„์›€์„ ์ค€๋‹ค. ์ง€๊ธˆ๊นŒ์ง€ ๋Œ€๋ถ€๋ถ„์˜ ์—ฐ๊ตฌ๋Š” ๊ณตํฌ ์กฐ๊ฑดํ™”์™€ ์‹ ๊ฒฝ๋ง ํ™œ์„ฑ์˜ ์ƒ๊ด€๊ด€๊ณ„์— ์ดˆ์ ์ด ๋งž์ถฐ์ ธ ์žˆ์—ˆ๊ธฐ์— ์‹œ๋ƒ…์Šค ํŠน์ด์  ๊ฐ€์†Œ์„ฑ์˜ ๊ธฐ๋Šฅ์žฅ์• ๊ฐ€ ํ–‰๋™์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์€ ์—ฐ๊ตฌ๋Š” ๋ฏธ์ง„ํ•˜๋‹ค. ๋‚˜๋Š” ๋‚ด์ธก ์ „์ „๋‘์—ฝ (medial prefrontal cortex; PFC) ๋ณ€์—ฐ๊ณ„์ „ (prelimbic; PL) ๋ถ€์œ„์˜ ๋ ˆ์ด์–ด 2/3 (L2/3)์™€ ๋ ˆ์ด์–ด 5 (L5)์˜ ํ”ผ๋ผ๋ฏธ๋“œ ์„ธํฌ (pyramidal neuron; PN) ์‚ฌ์ด์˜ ์‹œ๋ƒ…์Šค์™€ ์ฒœ๊ณต๊ฒฝ๋กœ (perforant pathway; PP)์™€ ํ•ด๋งˆ CA3์˜ ์‹œ๋ƒ…์Šค์—์„œ์˜ ๊ฐ€์†Œ์„ฑ ์žฅ์• ์™€ ํ–‰๋™ ์–‘์ƒ์˜ ๊ด€๊ณ„์— ๋Œ€ํ•ด ์—ฐ๊ตฌํ•˜์˜€๋‹ค. TFC์˜ ํ”์  ๊ธฐ๊ฐ„ (trace interval) ๋™์•ˆ PFC ์‹ ๊ฒฝ์„ธํฌ์˜ ์ง€์†์ ์œผ๋กœ ์ฆ๊ฐ€๋œ ํ™œ์„ฑ์€ ํ”์  ๊ณตํฌ ๊ธฐ์–ต์˜ ์Šต๋“์— ํ•„์š”ํ•˜๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ PFC์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ์ง€์†์ ์œผ๋กœ ์ฆ๊ฐ€๋œ ํ™œ์„ฑ์˜ ์‹ ๊ฒฝ์ƒ๋ฆฌํ•™์ ์ธ ๋ฉ”์ปค๋‹ˆ์ฆ˜์˜ ์ดํ•ด๋„๋Š” ๋‚ฎ๋‹ค. ๋‹จ๊ธฐ๊ฐ€์†Œ์„ฑ์˜ ์ข…๋ฅ˜ ์ค‘ ํ•˜๋‚˜ ๊ฐ•์ถ•ํ›„ ๊ฐ•ํ™” (post-tetanic potentiation; PTP)๋Š” ์ž‘์—… ๊ธฐ์–ต ๋™์•ˆ ์ง€์†์ ์œผ๋กœ ์ฆ๊ฐ€ํ•˜๋Š” ํ™œ์„ฑ์„ ์ค‘๊ฐœํ•  ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋œ๋‹ค. ๋”ฐ๋ผ์„œ ๋‚˜๋Š” PL์—์„œ ๋ฐœ์ƒํ•˜๋Š” PTP์˜ ์‹ ๊ฒฝ์ƒ๋ฌผํ•™์  ๋ฉ”์ปค๋‹ˆ์ฆ˜๊ณผ PTP๊ฐ€ TFC์™€ ์ง€์†์ ์œผ๋กœ ์ฆ๊ฐ€๋œ ํ™œ์„ฑ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”์ง€์— ๋Œ€ํ•ด์„œ ์—ฐ๊ตฌํ–ˆ๋‹ค. ๊ด‘์œ ์ „ํ•™์„ ํ†ตํ•ด ๋‚˜๋Š” L5 PN์˜ ๋“ค์‹ ๊ฒฝ์„ฌ์œ (afferent fiber)๋ฅผ ์„ธํฌ ํƒ€์ž… ๊ทธ๋ฆฌ๊ณ  ์ธต ํŠน์ด์ ์œผ๋กœ ์ž๊ทนํ–ˆ๋‹ค. ๊ทธ ๊ฒฐ๊ณผ L2/3 PN๊ณผ L5์˜ ๋งž๊ต์ฐจ (commissural; COM) PN์ด L5 ํ”ผ์งˆ๋‡Œ๊ตํˆฌ์‚ฌ (corticopontine; CPn) PN๊ณผ ์—ฐ๊ฒฐ๋˜๋Š” ์‹œ๋ƒ…์Šค์—์„œ PTP๊ฐ€ ์œ ๋„๋˜๊ณ  L5 COM๊ณผ ์—ฐ๊ฒฐ๋˜๋Š” ์‹œ๋ƒ…์Šค์—์„œ๋Š” ์œ ๋„๋˜์ง€ ์•Š๋Š” ๊ฒƒ์„ ๋ฐœ๊ฒฌํ–ˆ๋‹ค. ๋‘๊ฐœ์˜ ์‹œ๋ƒ…์Šค์—์„œ ์œ ๋„๋˜๋Š” PTP๋Š” protein kinase C (PKC) ์–ต์ œ์ œ๋กœ ๋ชจ๋‘ ์–ต์ œ๋˜์—ˆ์ง€๋งŒ, ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„ Na/Ca exchanger (mNCX) ์ฐจ๋‹จ์ œ์ธ tetraphenylphosphonium (TPP)๋Š” L2/3-CPn ์‹œ๋ƒ…์Šค์—์„œ ์œ ๋„๋˜๋Š” PTP๋งŒ์„ ์–ต์ œํ–ˆ๋‹ค. TPP๋ฅผ PL ๋ถ€์œ„์— ์ฃผ์ž…ํ•œ ํ›„ TFC๋ฅผ ์ˆ˜ํ–‰ํ•œ ๊ฒฐ๊ณผ, ํ”์  ๊ณตํฌ ์†Œ๊ฑฐ ์‹คํ—˜์—์„œ TPP๋Š” ํ”์  ๊ณตํฌ ํ˜•์„ฑ์—๋Š” ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ์•Š์•˜์ง€๋งŒ ๊ณตํฌ ๊ธฐ์–ต์„ ์งง๊ฒŒ ์œ ์ง€์‹œํ‚ค๋Š” ๊ฒƒ์„ ๋ฐํ˜”๋‹ค. ๋˜ํ•œ, ์ƒ์ฒด ๋‚ด ๊ธฐ๋ก ์‹คํ—˜์—์„œ TFC๋™์•ˆ CS ์ดํ›„์— ์ง€์†์ ์œผ๋กœ ์ฆ๊ฐ€ํ•œ ํ™œ์„ฑ์„ ๊ฐ€์ง€๋Š” PL-PNs์ด ์•ฝ 20% ์ธ ๊ฒƒ์„ ๋ฐํ˜”๋‹ค. TPP์˜ ์ฃผ์ž…์€ ๊ณตํฌ ์กฐ๊ฑดํ™” ์‹คํ—˜๊ณผ ๊ณตํฌ ๊ธฐ์–ต ์†Œ๊ฑฐ ์‹คํ—˜์—์„œ CS ์ดํ›„์— ๋‚˜ํƒ€๋‚˜๋Š” ์ง€์†์ ์ธ ํ™œ์„ฑ ์ฆ๊ฐ€๋ฅผ ์–ต์ œํ–ˆ๋‹ค. COM๊ณผ CPn ์„ธํฌ ํŠน์ด์  ์ƒ์ฒด๋‚ด ์นผ์Š˜ ์ด๋ฏธ์ง• ์‹คํ—˜์—์„œ ๋‘ ์„ธํฌ๋Š” ๋‹ค๋ฅธ ๋น„์œจ์˜ ํ™œ์„ฑ ์–‘์ƒ์„ ๋‚˜ํƒ€๋ƒˆ๋‹ค. COM๊ณผ ๋‹ค๋ฅด๊ฒŒ CPn์€ CS์˜ ์‹œ์ž‘์ ์—์„œ ์˜ˆ์ƒ๋˜๋Š” US ์‹œ๊ฐ„๊นŒ์ง€ ์ง€์†์ ์œผ๋กœ ์ฆ๊ฐ€ํ•œ ํ™œ์„ฑ์„ ๋ณด์ด๋Š” Delay ์„ธํฌ ํƒ€์ž…์˜ ๋น„์œจ์ด ํ›จ์”ฌ ๋งŽ์•˜๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋“ค์€ L2/3-CPn ์‹œ๋ƒ…์Šค์—์„œ ๋ฐœ์ƒํ•˜๋Š” PTP๊ฐ€ TFC์—์„œ CS ์ดํ›„์— ์ง€์†์ ์œผ๋กœ ์ฆ๊ฐ€ํ•œ ํ™œ์„ฑ์— ํ•„์š”ํ•˜๋ฉฐ, ํ”์  ๊ณตํฌ ๊ธฐ์–ต ์œ ์ง€์— ์ค‘์š”ํ•˜๋‹ค๋Š” ๊ฒƒ์„ ์‹œ์‚ฌํ•œ๋‹ค. ํ•ด๋งˆ๋Š” ๋งฅ๋ฝ ๊ณตํฌ์˜ ํ˜•์„ฑ, ๊ฐ•ํ™” ๊ทธ๋ฆฌ๊ณ  ๊ฒ€์ƒ‰์— ์ค‘์š”ํ•˜๋‹ค. ๋‘ ๊ฐœ์˜ ์œ ์‚ฌํ•œ ๋งฅ๋ฝ์„ ๋‚˜ํƒ€๋‚ด๋Š” ์•™์ƒ๋ธ” ํ˜•์„ฑ์˜ ๊ธฐ์ดˆ๊ฐ€ ๋˜๋Š” ๋„คํŠธ์›Œํฌ ํ”„๋กœ์„ธ์Šค๋ฅผ ํŒจํ„ด ๋ถ„๋ฆฌ (pattern separation)๋ผ๊ณ  ํ•˜๋ฉฐ ํ•ด๋งˆ์˜ ์ค‘์š”ํ•œ ๊ธฐ๋Šฅ ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ CA3์—์„œ ์‹ ๊ฒฝ ์•™์ƒ๋ธ”์˜ ํŒจํ„ด ๋ถ„๋ฆฌ์˜ ๊ธฐ๋ณธ์ด ๋˜๋Š” ๋„คํŠธ์›Œํฌ ๋ฉ”์ปค๋‹ˆ์ฆ˜์€ ๊ฑฐ์˜ ์•Œ๋ ค์ ธ ์žˆ์ง€ ์•Š๋‹ค. CA3 ํ”ผ๋ผ๋ฏธ๋“œ ์„ธํฌ (CA3-PN)์—์„œ Kv1.2 ๋ฐœํ˜„์€ ๋ง๋‹จ ์ •์  ์ˆ˜์ƒ๋Œ๊ธฐ (distal apical dendrite)๋กœ ์ง‘์ค‘๋˜์–ด ์žˆ๊ณ , ๊ทธ๊ฒƒ์˜ ํ•˜ํ–ฅ ์กฐ์ ˆ (downregulation)์€ PP ์‹œ๋ƒ…์Šค ์ž…๋ ฅ์— ๋Œ€ํ•œ ์ˆ˜์ง€์ƒ (dendritic) ๋ฐ˜์‘์„ ํ–ฅ์ƒ์‹œํ‚จ๋‹ค. CA3-PN์—์„œ Kv1.2์˜ ๋ฐ˜์ˆ˜์ฒด ๋ถ€์กฑ ๊ฐœ์ฒด (Kcna2+/-)๋Š” PP-CA3 ์‹œ๋ƒ…์Šค์—์„œ ์žฅ๊ธฐ ๊ฐ•ํ™” (Long-term potentiation; LTP)์˜ ์ž„๊ณ„๊ฐ’ (threshold)์„ ๋‚ฎ์ถ˜๋‹ค. Kcna2+/- ๊ฐœ์ฒด๋Š” ํ™•์‹คํžˆ ๊ตฌ๋ณ„๋˜๋Š” ์ƒํ™ฉ์„ ์‹๋ณ„ํ•˜๋Š” ํ–‰๋™์€ ์ •์ƒ์ด์ง€๋งŒ ์•ฝ๊ฐ„์˜ ์ฐจ์ด๊ฐ€ ์žˆ๋Š” ์œ ์‚ฌํ•œ ์ƒํ™ฉ์„ ์‹๋ณ„ํ•˜๋Š” ํ–‰๋™์—๋Š” ์žฅ์• ๋ฅผ ๋ณด์ด๋Š” ๊ฒƒ์„ ์ด์ „ ์—ฐ๊ตฌ์—์„œ ๋ฐํ˜”๋‹ค. ๋‚˜๋Š” ์ดˆ๊ธฐ ๋ฐœํ˜„ ์œ ์ „์ž (Homer1a, Arc)์™€ ์ ˆํŽธ์ƒ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ํ˜•์„ฑ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ๋‘ ๊ฐ€์ง€ ์œ ์‚ฌํ•œ ์ปจํ…์ŠคํŠธ์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” CA3 ๋ฐ ์น˜์•„์ด๋ž‘ (Dentate gyrus; DG)์˜ ์‹ ๊ฒฝ ์•™์ƒ๋ธ”์˜ ํŒจํ„ด ๋ถ„๋ฆฌ๋ฅผ ์กฐ์‚ฌํ–ˆ๋‹ค. ๋น„์Šทํ•œ ๋ฌธ๋งฅ์— ์ฒ˜์Œ ๋…ธ์ถœ๋˜์—ˆ์„ ๋•Œ ํ™œ์„ฑํ™”๋˜๋Š” CA3 ์•™์ƒ๋ธ”์˜ ํฌ๊ธฐ์™€ ๊ต์ง‘ํ•ฉ์€ ์•ผ์ƒํ˜• (wildtype; WT)๊ณผ Kcna2+/- ๋งˆ์šฐ์Šค ๊ฐ„์— ์ฐจ์ด๊ฐ€ ์—†์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋‘ ๊ทธ๋ฃน๊ฐ„์˜ ์•™์ƒ๋ธ” ํฌ๊ธฐ์™€ ๊ต์ง‘ํ•ฉ์€ ์‹คํ—˜์ด ์ง„ํ–‰๋ ์ˆ˜๋ก ์ ์ฐจ ์ฐจ์ด๋ฅผ ๋ณด์˜€๋‹ค. ์ด๋Š” Kcna2+/- ๋งˆ์šฐ์Šค์˜ PP-CA3 ์‹œ๋ƒ…์Šค์—์„œ ๋น„์ •์ƒ์ ์ธ ๊ฐ€์†Œ์„ฑ ๋ณ€ํ™”๊ฐ€ ๋‚˜ํƒ€๋‚˜๋ฉฐ ๊ฒฐ๊ณผ์ ์œผ๋กœ WT๊ณผ ๋‹ค๋ฅด๊ฒŒ ๋น„์ •์ƒ์ ์ธ ํŒจํ„ด ๋ถ„๋ฆฌ๋ฅผ ์•ผ๊ธฐํ•œ๋‹ค๋Š” ๊ฒƒ์„ ์ œ์•ˆํ•œ๋‹ค. ์ด์™€ ๋‹ค๋ฅด๊ฒŒ DG์˜ ์•™์ƒ๋ธ”์€ ๋‘ ๊ทธ๋ฃน๊ฐ„์˜ ์ฐจ์ด๊ฐ€ ์—†์—ˆ๋‹ค. DG ์•™์ƒ๋ธ”์€ ์‹คํ—˜ ์ฒซ๋‚ ๋ถ€ํ„ฐ ์ด๋ฏธ ๋ถ„๋ฆฌ๋˜์–ด ์žˆ์—ˆ์œผ๋ฉฐ, ์•™์ƒ๋ธ”์˜ ๊ต์ง‘ํ•ฉ์€ ์‹คํ—˜์ด ์ง„ํ–‰๋˜์–ด๋„ ๋ณ€ํ™”ํ•˜์ง€ ์•Š์•˜๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ Kcna2+/- ๋งˆ์šฐ์Šค๋Š” WT๊ณผ Kcna1+/- ๋งˆ์šฐ์Šค์— ๋น„ํ•ด์„œ, CA3 ์•™์ƒ๋ธ”์˜ ํฌ๊ธฐ์™€ ๋‘๊ฐœ์˜ ๋ฌธ๋งฅ์— ๋Œ€ํ•œ ์•™์ƒ๋ธ”์˜ ๊ต์ง‘ํ•ฉ์ด ํฌ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ์ด๋ผ ์„ฌ์œ  (mossy fiber)์˜ ์ž…๋ ฅ์— ์˜ํ•ด ์ง€๋„๋˜๋Š” PP-CA3 ์‹œ๋ƒ…์Šค์˜ ํฌ์†Œํ•œ LTP๊ฐ€ CA3์˜ ์ ์ง„์  ํŒจํ„ด ๋ถ„๋ฆฌ์— ํ•„์ˆ˜์ ์ž„์„ ์‹œ์‚ฌํ•œ๋‹ค.Chapter 1. Role of post-tetanic potentiation in persistent activity of medial prefrontal neuron during trace fear conditioning and retrieval 1 Introduction 2 Materials and Methods 14 Results 34 Discussion 112 Chapter 2. Gradual pattern separation in CA3 associated with contextual discrimination learning is impaired by Kv1.2 insufficiency 134 Introduction 135 Materials and Methods 140 Results 148 Discussion 172 References 180 Abstract in Korean 200๋ฐ•

    Cellular and circuit mechanisms of anti-NMDA receptor autoimmune encephalitis

    Get PDF

    MUSCARINIC MODULATION OF BASOLATERAL AMYGDALA

    Get PDF
    The basolateral amygdala (BL) receives a dense cholinergic innervation from the basal forebrain. Despite the importance of muscarinic acetylcholine receptors (mAChRs) in fear learning, consolidation, and extinction, there have been no studies that have systematically investigated the functional role of mAChRs in regulating emotional processing in the BL. To address this critical knowledge gap we combined brain slice whole-cell recording, optogenetics, and immunohistochemistry to determine how muscarine, acting on mAChRs, regulates neuronal oscillations, synaptic transmission and plasticity in the BL. Neurons in the BL oscillate rhythmically during emotional processing, which are thought to be important to integrate sensory inputs, allow binding of information from different brain areas and facilitate synaptic plasticity in target downstream structures. We found that muscarine induced theta frequency rhythmic inhibitory postsynaptic potentials (IPSPs) in BL pyramidal neuron (PN). These IPSPs synchronized PN firing at theta frequencies. Recordings from neurochemically-identified interneurons revealed that muscarine selectively depolarized parvalbumin (PV)-containing, fast firing, but not PV, regular firing or somatostatin (SOM)-containing interneurons. This depolarization was mediated by M3 mAChRs. Dual cell recordings from connected interneuron-PN pair indicated that action potentials in fast firing, but not regular firing interneurons were strongly correlated with large IPSCs in BL PNs. Furthermore, selective blockade of M3, but not M1 mAChRs suppressed the rhythmic IPSCs in BL PNs. These findings suggest that muscarine induces rhythmic IPSCs in PNs by selectively depolarizing PV, fast firing interneurons through M3 mAChRs. Furthermore, we found that rhythmic IPSCs were highly synchronized between PNs throughout the BL. The BL receives extensive glutamatergic inputs from multiple brain regions and recurrent collaterals as well. They are important for fear learning and extinction, which are tightly regulated by local GABAergic inhibition. We found that mAChRs activation suppressed external glutamatergic inputs in a frequency dependent and pathway specific manner but kept recurrent glutamatergic transmission intact. In addition, muscarine disinhibited BL PNs by attenuating feedforward and GABAergic inhibition. In agreement with these observations, long term potentiation (LTP) induction was facilitated in the BL by mAChRs activation. Taken together, we provided mechanisms for cholinergic induction of thetaoscillations and facilitation of LTP in the BL

    The interaction between neuronal networks and gene networks

    Get PDF
    Periods of strong electrical activity can initiate neuronal plasticity leading to long-lasting changes of network properties. A key event in the modification of the synaptic connectivity after neuronal activity is the activation of new gene transcription. Moreover, calcium (Ca2+) influx is crucial for transducing synaptic activity into gene expression through the activation of many signalling pathways. In our work we are interested in studying changes in electrical activity and in gene expression profile at the network level. In particular, we want to understand the interplay between neuronal and gene networks to clarify how electrical activity can alter the gene expression profile and how gene expression profile can modify the electrical and functional properties of neuronal networks. Thus, we investigated the neuronal network at three different levels: gene transcription profile, electrical activity and Ca2+ dynamics. Blockage of GABAA receptor by pharmacological inhibitors such as gabazine or bicuculline triggers synchronous bursts of spikes initiating neuronal plasticity. We have used this model of chemically-induced neuronal plasticity to investigate the modifications that occur at different network levels in rat hippocampal cultures. By combining multielectrode extracellular recordings and calcium imaging with DNA microarrays, we were able to study the concomitant changes of the gene expression profile, network electrical activity and Ca2+ concentration. First, we have investigated the time course of the electrical activity and the molecular events triggered by gabazine treatment. The analysis of the electrical activity revealed three main phases during gabazine-induced neuronal plasticity: an early component of synchronization (E-Sync) that appeared immediately after the termination of the treatment persisted for 3 hours and was blocked by inhibitors of the MAPK/ERK pathway; a late component (L-Sync) -from 6 to 24 hours- that was blocked by inhibitors of the transcription. And, an intermediate phase, from 3 to 6 hours after the treatment, in which the evoke response was maximally potentiated. Moreover, gabazine exposure initiated significant changes of gene expression; the genomic analysis identified three clusters of genes that displayed a characteristic temporal profile. An early rise of transcription factors (Cluster 1), which were maximally up-regulated at 1.5 hours. More than 200 genes, many of which known to be involved in LTP were maximally up-regulated in the following 2-3 hours (Cluster 2) and then were down-regulated at 24 hours. Among these genes, we have found several genes coding for K+ channels and the HNC1 channels. Finally, genes involved in cellular homeostasis were up-regulated at longer time (Cluster 3). Therefore, this approach allows relating changes of electrical properties occurring during neuronal plasticity to specific molecular events. Second, we have investigated which sources of Ca2+ entry were involved in mediating the new gene transcription activated in response to bursting activity. Using Ca2+ imaging, a detailed characterization of Ca2+ contributions was performed to allow investigating which sources of Ca2+ entry could be relevant to induce gene transcription. At the same time, changes of gene expression were specifically investigated blocking NMDA receptors and L-, N- and P/Q-type VGCCs. Therefore, the analysis of the Ca2+ contribution and gene expression changes revealed that the NMDA receptors and the VGCCs specifically induced different groups of genes. Thus, the combination of genome-wide analysis, MEA technology and calcium imaging offers an attractive strategy to study the molecular events underlying long-term synaptic modification

    Long-Term Activity-Dependent Plasticity of Action Potential Propagation Delay and Amplitude in Cortical Networks

    Get PDF
    Background: The precise temporal control of neuronal action potentials is essential for regulating many brain functions. From the viewpoint of a neuron, the specific timings of afferent input from the action potentials of its synaptic partners determines whether or not and when that neuron will fire its own action potential. Tuning such input would provide a powerful mechanism to adjust neuron function and in turn, that of the brain. However, axonal plasticity of action potential timing is counter to conventional notions of stable propagation and to the dominant theories of activity-dependent plasticity focusing on synaptic efficacies. Methodology/Principal Findings: Here we show the occurrence of activity-dependent plasticity of action potentia
    • โ€ฆ
    corecore