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 銅谷賢治等編 (2002) 脳の情報表現,ニューロン・ネットワーク・数理モデル. 朝倉書店, pp.131-145.

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* DeAngelis GC, Ohzawa I, Freeman RD. Receptive-field dynamics in the central visual pathways. Trends Neurosci. 1995 Oct;18(10):451-8. Review. PMID: 8545912

Nicolelis, M.A.L. Brain-machine interfaces to restore motor functions and probe neural circuits. Nature Reviews Neuroscience.4.417-422 (2003).

我々は絶えず覚えるべき情報を取捨選択している。おもしろいことに、全く同じ情報に遭遇しても、受け手側の状態（体調、気分、過去の経験）により、情報が重要性に対する判断が異なる場合がある。記憶すべき情報は、どこでどのようにしてゲーティングされているのであろうか。新規記憶の形成に重要な部位である海馬を研究対象にして得られたこれまでの生理学実験データーを紹介し、記憶情報の選択メカニズムを実験的にどこまで検証できるのか、また理論的アプローチをどのように取り入れていくべきかを討論したい。

講義テキスト

* 関野祐子、白尾智明「海馬内興奮伝播のゲート機構」　特集：記憶研究最近の進歩、神経研究の進歩　45巻　283-286 (2001)
*Saji M., Kobayashi S., Ohono K., Sekino Y. Interruption of supramammillo-hippocampal afferents prevents the genesis and spread of limbic seizures in the hippocampus via a disinhibition mechanism. Neurosci. 97: 437-445 (2000)
*Ochiishi T., Saitoh Y., Yukawa A., Saji M., Ren Y., Shirao T., Miyamoto H., Nakata H., Sekino Y. High level of adenosine A1 receptor-like immunoreactivity in the CA2/CA3a region of the adult rat hippocampus. Neurosci. 93:955-967(1999)
*Matsuoka Y., Okazaki M., Tanaka K., Katamura Y., Ohta S., Sekino Y., Taniguchi T. Endogenous Adenosine Protects CA1 Neurons from Kainic Acid-Induced Neuronal Cell Loss in the Rat Hippocampus. Eur.J. Neurosci. 11:3617-3625 (1999)
*Sekino Y., Obata K., Tanifuji, M. Mizuno, M. and Murayama J. Delayed signalpropagation via CA2 in rat hippocampal slices revealed by optical recording. J. Neurophysiol. 78, 1662-1668 (1997)

小脳皮質の構成ニューロンとそれらが形成する神経回路を説明します。各ニューロンと各シナプスのはたらきに関して実験的解析手法を紹介しながら、これまでに得られた知見を概説し、小脳皮質の神経回路全体の作動原理に関する考察と議論の材料を提供したいと思います。

* Cerebellum. Llinas R.R. and Walton K.D. The synaptic organization of the
brain. Ed. by Shepherd G.M. Oxford University Press,1998.

lectureHirano.pdf

大脳基底核は大脳皮質のほとんどから入力を受けますが、その最大の入力部 位は線条体です。そこから、淡蒼球の外節、視床下核などを経て出力の核である黒 質網様部、淡蒼球の内節から視床、そして再び皮質へと情報が戻されます。この講 義ではその皮質ー基底核連関がどのように動いているのかについてその概略を紹介 し、次に線条体における局所回路についての最近のデータについてお話します。

*Alexander, G.E. and Crutcher, M.D., "Functional architechture of basal ganglia circuits: neural substrates of parallel processing", Trends Neurosci 13, 266-271, 1990.
*Chesselet, M-F and Delfs, J.M., "Basal ganglia and movement disorders: an update" Trends Neurosci 19, 417-422, 1996.
*Bergman, H., Feingold, A., Nini, A., Raz, A., Slovin, H., Abeles, M. and Vaardia, E. "Physiological aspects of information processing in the basal ganglia of normal and parkinsonian primates" Trends Neurosci 21, 32-38, 1998.
*Bevan, M.D., Magill, P.J., Terman, D., Bolam, J.P., Wilson, C.J., "Move to the rhythm: oscillations in the subthalamic nucleus-external globus pallidus network." Trends Neurosci 25, 525-531, 2002.
*Aosaki, T., Graybiel, A.M., Kimura, M., "Effects of the nigrostriatal dopamine system on acquired neural responses in the striatum of behaving monkeys." Science 265, 412-415, 1994.
*Graybiel, A.M., Aosaki, T., Flaherty, A.W., Kimura, M. "The basal ganglia and adaptive motor control" Science 265, 1826-1831, 1994.
*Cicchettti, F., Prensa, L., Wu, Y., Parent, A. "Chemical anatomy of striatal interneurons in normal individuals and in patients with Huntington's disease" Brain Res Rev 34, 80-101, 2000.
*Suzuki, T., Miura, M., Nishimura, K. and Aosaki, T. "Dopamine-dependent synaptic plasticity in the striatal cholinergic interneurons" J Neurosci 21, 6492-6501, 2001.

 Self-organization of neural networks is a central question in neuroscience. How does the brain achieve and maintain activity states at which information processing is possible? In mammals, the cortex is responsible for information processing that ultimately leads to adaptive behavior in an ever changing environment. To achieve this task, the cortex is greatly helped by the basal ganglia that reside as a collection of highly diverse nuclei in the forebrain. My group focuses on the neuronal dynamics in cortical networks and the processing of cortical inputs in the striatum, the first stage of the basal ganglia. More specifically, we aim at the understanding of critically self-organized network states in cortex and its resultant ﾔUpﾕ and ﾔDownﾕ state dynamics in the striatum.

Neuronal networks exist at many different levels and come in various sizes, so where shall we start? For sure we know that knowledge of individual neuron properties is insufficient to predict the number of activity patterns such networks can generate. The task is even more challenging when considering networks between different brain structures such as the cortex and the striatum. Thus, we designed experimental systems in vitro under defined tissue culture conditions that allowed for precisely targeted in vitro reconstruction of cortex - basal ganglia networks using organotypic cultures. In these in vitro networks, the cortical culture consists of up to 50,000 neurons with all major layers and cell classes preserved. Similarly, the cultures from basal ganglia nuclei, e.g. the striatum, contain tens of thousands of neurons with all major cell classes integrated into the ongoing network dynamics. These evolved in vitro systems have already been proven to acquire some of the most complex network states so far described in vivo such as cortical ~40 Hz oscillations [1] and striatal Up and Down states [2]. They have been instrumental in discovering new network states e.g. a central pacemaker of the basal ganglia comprised of subthalamic nucleus and globus pallidus [3].

We use primarily electrophysiological and computational methods to study these neuronal networks. Specific questions relate to the intra- and internuclei generation of network activities, their stability conditions, the transfer of activity patterns to target nuclei, and the dynamic characteristics of single neuron processing under such network activity. Our recent work has focused on the discovery of a self-organized critical network state in cortex [4], dendritic processing in striatal neurons during Up-states [5], and synaptic transmission between spiny projection neurons [6].

Our experimental strategies and conceptual approaches establish conditions that allow for a detailed classification of normal and abnormal neuronal activity patterns that have been observed in human neurological diseases and corresponding animal models (i.e. Parkinson's disease, Huntington's Chorea, Alzheimerﾕs disease). Insights into critically self-organized network states will provide essential information for our understanding these neurological disorders and normal brain function.

1 Plenz, D. and Kitai, S. T. (1996) Generation of high frequency oscillations in cortical circuits of somatosensory cortex cultures. J. Neurophysiol. 76, 4001-4005
2 Plenz, D. and Kitai, S. T. (1998) 'Up' and 'down' states in striatal medium spiny neurons simultaneously recorded with spontaneous activity in fast-spiking interneurons studied in cortex-striatum-substantia nigra organotypic cultures. J. Neurosci. 18, 266-283
3 Plenz, D. and Kitai, S. T. (1999) A basal ganglia pacemaker formed by the subthalamic nucleus and external globus pallidus [see comments]. Nature 400, 677-682
4 Beggs, J.M. and Plenz, D. (2003) A branching process underlies self-organized critical behavior in isolated cortical networks (submitted).
5 Kerr, J.N. and Plenz, D. (2002) Dendritic calcium encodes striatal neuron output during Up-states. J. Neurosci. 22, 1499-1512
6 Czubayko, U. and Plenz, D. (2002) Fast synaptic transmission between striatal spiny projection neurons. Proc. Natl. Acad. Sci. U. S. A 99, 15764-15769

synfire chainの基本的なアイディアを解説した後，Alex Reyesがスライス標本 で行ったsynfire chain生成の実験を紹介する．そのあと，それを解析する確率過程の手法を紹介する

*Diesmann M, Gewaltig MO, Aertsen A. Stable propagation of synchronous spiking in cortical neural networks. Nature. 1999 Dec 2;402(6761):529-33.
*Cateau H, Fukai T. A stochastic method to predict the consequence of arbitrary forms of spike-timing-dependent plasticity. Neural Comput. 2003 Mar;15(3):597-620.