Lectures (Alphabetical Order)

• Speaker:
  Andreas Lüthi
• Position:
  riedrich Miescher Institute for Biomedical Research, Basel, Switzerland
• Title:
  Roles of GABAergic inhibition in neuronal networks of fear
• Abstract:

  Classical fear conditioning is one of the most powerful models for studying the neuronal substrates of associative learning and for investigating how plasticity in defined neuronal circuits causes behavioral changes. In animals and humans, the amygdala is a key brain structure within a larger neuronal network mediating the acquisition, expression and extinction of fear memories. In unraveling the substrates of fear conditioning and extinction, the major focus has been the study of excitatory elements. However, interneurons are critical components of neuronal networks and inhibition plays an important role in shaping spatio-temporal patterns of network activity. My presentation will summarize recent progress in understanding how defined local inhibitory circuits contribute to the acquisition and expression of fear and extinction memories by multiple mechanisms and at multiple levels both in the amygdala and in other brain areas. The talk aims at illustrating how the convergence of molecular, electrophysiological and optical approaches has enriched our understanding of the neuronal basis of fear conditioning and of learning and memory in general.

• Speaker:
  Ko Matsui
• Position:
  National Institute for Physiological Sciences / Tohoku University Graduate School of Medicine
• Title:
  Building of brain function through intercellular communication
• Abstract:

  Every scientific endeavor starts with observation. However, observation alone can only lead to analysis of correlation. Experimental perturbation is required to understand the causal relationship between the components that constitute the system under study. The brain is a complex multicellular organ. Our current understanding of its function suggests that communication between these cells underlies the formation of the mind. This is mainly deduced from studies of correlation between cell activity and animal behavior. Recently developed tools enable specific control of cell activity. For example, light-sensitive proteins found in microorganisms, such as channelrhodopsin-2, can now be genetically expressed in mammalian brain cells which allow experimenters to optically control cell activity at will. In this talk, I will introduce various methods that I applied to study communication between neuron-to-neuron, neuron-to-glia, and glia-to-neuron. In particular, I will introduce a recently established method, the Knockin-mediated ENhanced Gene Expression by improved tetracycline-controlled gene induction (KENGE-tet) method, which succeeded in generating a repertoire of transgenic mice expressing a highly light-sensitive channelrhodopsin-2 mutant at sufficient levels to stimulate multiple cell types. In addition to neurons, manipulation of the activity of "non-excitable" glial cells in vivo has also proved possible. A recent report using KENGE-tet shows that selective optogenetic stimulation of glia can lead to release of glutamate as gliotransmitter, induce synaptic plasticity, and accelerate cerebellar modulated motor learning. This finding suggests that glia also participates in information processing in the brain, a function once thought to be solely mediated by neuronal activity. These reports demonstrate the use of optogenetic tools for exploring the causal relationship between brain activity and mind.

• References:
  (Recommended)
  1. Budisantoso T, Harada H, Kamasawa N, Fukazawa Y, Shigemoto R, Matsui K* (2013) Evaluation of glutamate concentration transient in the synaptic cleft of the rat calyx of Held. Journal of Physiology 591: 219-239. （* corresponding author ）
  2. Budisantoso T†, Matsui K†*, Kamasawa N, Fukazawa Y, Shigemoto R (2012) Mechanisms underlying signal filtering at a multi-synapse contact. Journal of Neuroscience, 32: 2357-2376. († equal contribution, * corresponding author )
  3. Matsui K, Jahr CE (2003) Ectopic release of synaptic vesicles. Neuron, 40: 1173-1183.
  4. Tanaka KF*, Matsui K*, Sasaki T, Sano H, Sugio S, Fan K, Hen R, Nakai J, Yanagawa Y, Hasuwa H, Okabe M, Deisseroth K, Ikenaka K, Yamanaka A (2012) Expanding the repertoire of optogenetically targeted cells with an enhanced gene expression system. Cell Reports, 2: 397-406. (* equal contribution )
  5. Sasaki T, Beppu K, Tanaka KF, Fukazawa Y, Shigemoto R, Matsui K* (2012) Application of an optogenetic byway for perturbing neuronal activity via glial photostimulation. Proc Natl Acad Sci USA, 109: 20720-20725. （* corresponding author ）
  (Optional)
  6. Tarusawa E, Matsui K*, Budisantoso T, Moln?r E, Watanabe M, Matsui M, Fukazawa Y*, Shigemoto R (2009) Input-specific intrasynaptic arrangements of ionotropic glutamate receptors and their impact on postsynaptic responses. Journal of Neuroscience, 29: 12896-12908. (* corresponding authors )
  7. Matsui K, Jahr CE (2006) Exocytosis unbound. Current Opinion in Neurobiology, 16: 305-311.
  8. Matsui K*, Jahr CE, Rubio ME (2005) High concentration rapid transients of glutamate mediate neural-glial communication via ectopic release. Journal of Neuroscience, 25: 7538-7547. （* corresponding author ）
  9. Matsui K, Jahr CE (2004) Differential control of synaptic and ectopic vesicular release of glutamate. Journal of Neuroscience, 24: 8932-8939.

• Speaker:
  Masanori Matsuzaki
• Position:
  National Institute for Basic Biology
• Title:
  Spatial and temporal dynamics of function clusters of neurons in the mouse motor cortex during a voluntary movement
• Abstract:

  Two-photon imaging is a powerful tool used to examine molecular and cellular functions in living tissues. In particular, calcium imaging can quantitatively measure neuronal activity i.e. action potential firing. Two-photon calcium imaging can detect the multicellular activity of neuronal circuits in the brain at the single cell level while animals perform behavioral tasks. I will review the general mechanisms and methodologies for two-photon calcium imaging in awake behaving mice. Recently, we conducted two-photon calcium imaging of mouse layer 2/3 motor cortex during a self-initiated lever-pull task. In the imaging session after 8-9-day training, head-restrained mice had to pull a lever for ~600 ms to receive a water drop, and then had to wait for >3 s to pull it again. We found two types of task-related cells in the mice: cells whose peak activities occurred during lever pulls and cells whose peak activities occurred after the end of lever pulls. I will describe the spatiotemporal dynamics of the task-related neurons during the voluntary movement.

• References:
  (Recommended)
  1. Hira R, Ohkubo F, Ozawa K, Isomura Y, Kitamura K, Kano M, Kasai H, Matsuzaki M. (2013) Spatiotemporal dynamics of functional clusters of neurons in the mouse motor cortex during a voluntary movement. J Neurosci 33: 1377-1390.
  2. Dombeck DA, Graziano MS, Tank DW. (2009) Functional clustering of neurons in motor cortex determined by cellular resolution imaging in awake behaving mice. J Neurosci 29: 13751-13760.
  3. Isomura Y, Harukuni R, Takekawa T, Aizawa H, Fukai T (2009) Microcircuitry coordination of cortical motor information in self-initiation of voluntary movements. Nat Neurosci 12: 1586-1593.
     (Optional)
  4. Dombeck DA, Khabbaz AN, Collman F, Adelman TL, Tank DW (2007) Imaging large-scale neural activity with cellular resolution in awake, mobile mice. Neuron 56: 43-57.
  5. Wessberg J, Stambaugh CR, Kralik JD, Beck PD, Laubach M, Chapin JK, Kim J, Biggs J, Srinivasan MA, Nicolelis MAL (2000) Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408: 361-365.

• Speaker:
  Taro Toyoizumi
• Position:
  RIKEN BSI
• Title:
  Toward a unifying theory of cortical plasticity
• Abstract:

  Synaptic plasticity is thought to be the underlying mechanism for learning and memory. It also plays a major role in the development of cortical circuits. In this talk, I introduce a theoretical attempt to integrate many experimental findings on cortical plasticity in order to extract computational principles underlying the self-organization of cortical circuits. In the first part, I review optimization approaches to neuronal plasticity to explain how learning rules derived from theoretical principles share features with experimental observations. In the second part, I present my recent attempt to model activity dependent plasticity in V1 during early development. I explain how inhibitory maturation in V1 and computational learning rules together explain the equalization of the two eyes in kittens and distinct influences of experience at different developmental stages in mice.

• References:
  (Recommended)
  1. T. Toyoizumi and K. D. Miller, J. Neuroscience 29, 6514-6525 (2009). "Equalization of ocular dominance columns induced by an activity-dependent learning rule and the maturation of inhibition"
  2. T. Toyoizumi, J.-P. Pfister, K. Aihara and W. Gerstner, Proc. Natl. Acad. Sci. USA 102, 5239-5244 (2005). "Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission"
     (Optional)
  3. T. Toyoizumi and L. F. Abbott, Physical Review E 84, 051908 (2011). "Beyond the edge of chaos: Amplification and temporal integration by recurrent networks in the chaotic regime"
  4. T. Toyoizumi, J.-P. Pfister, K. Aihara and W. Gerstner, Neural Computation 19, 639-671 (2007). "Optimality Model of Unsupervised Spike-Timing-Dependent Plasticity: Synaptic Memory and Weight Distribution"
  5. J. Gjorgjieva, T. Toyoizumi and S. J. Eglen, PLoS Computational Biology 5, e1000618 (2009). "Burst-time-dependent plasticity robustly guides ON/OFF segregation in the lateral geniculate nucleus"

• Speaker:
  Ben Seymour
• Position:
  National Institute of Information and Communications Technology
• Title:
  Reinforcement learning and the human brain
• 
  

• Speaker:
  Jeff Wickens
• Position:
  Okinawa Institute of Science and Technology
• Title:
  The basal ganglia, dopamine and acetylcholine: substrates of reinforcement and learning
• Abstract:

  The basal ganglia constitute a major brain center for learning on the basis of positive reinforcement. The neuromodulators, dopamine and acetylcholine, play a central role in basal ganglia operations. In this lecture I will discuss the cellular and circuit mechanisms underlying reinforcement learning in the striatum, the major input nucleus of the basal ganglia1. The specific effects on learning of localized lesions of the brain have established the striatum as a crucial center for reinforcement learning.2-4 The striatum contains mechanisms that link sensory, cognitive, and motor information from the cerebral cortex and thalamus with reward signals transmitted by midbrain dopamine neurons.5, 6 The cortical and thalamic afferent fibers make glutamatergic synapses on the principal neurons of the striatum, the spiny projections neurons, which are also the output neurons. This anatomy provides a matrix of potential input-output connections in the striatum from which to select particular connections by activity-dependent plasticity of the corticostriatal synapses. I will review research that aims to determine the rules governing plasticity of these corticostriatal synapses. We know that dopamine plays a key role in these rules7, 8. The striatum receives a dense dopaminergic input from neurons located in the midbrain ventral tegmental area and the substantia nigra pars compacta. Unlike classical neurotransmitters that relay specific communication between single neurons, dopamine is a neuromodulator carrying globally important information to large swaths of neural tissue. Dopamine-dependent plasticity is a potential cellular mechanism underlying reinforcement learning in the striatum. We previously showed that corticostriatal synapses exhibit dopamine dependent plasticity according to a “three factor rule” for synaptic modification. In particular, a conjunction of presynaptic cortical input and postsynaptic striatal output results in long-term potentiation when associated with dopamine inputs, but long-term depression in the absence of dopamine.9, 10 Thus, dopamine may facilitate selection of particular pathways among the matrix of corticostriatal input-output possibilities. In addition to dopamine, acetylcholine is present in high concentrations in the striatum due to intrinsic cholinergic interneurons. The cholinergic contribution to learning is incompletely understood but probably of major significance. Cholinergic interneurons acquire responses to cues in a dopamine dependent manner.11 Conversely, cholinergic interneurons modulate dopamine release12, 13 and dopamine dependent synaptic plasticity14 in striatal neurons. They play a role that is complementary and distinct to that of dopamine neurons.15 For example, although cholinergic interneurons show pauses at the time of dopamine neuron bursts, dopamine neurons differentiate between cues indicating different reward probabilities while cholinergic neurons do not.16 We do not have a good understanding of the role of acetylcholine in plasticity and learning in the striatum at present, but I will review some of the neurobiology of the cholinergic interneurons in the hope this may provide clues for future research.

• References:
  (Recommended)
  1. Wickens, J.R. (1997). Basal ganglia: Structure and computations [Invited Review]. Network: Computation in Neural Systems 8: 77-109.
  2. Packard, M.G., and Knowlton, B.J. (2002). Learning and memory functions of the basal ganglia. Annu Rev Neurosci 25: 563-593.
  3. Balleine, B.W., Delgado, M.R., and Hikosaka, O. (2007). The role of the dorsal striatum in reward and decision-making. J Neurosci 27: 8161-8165.
  4. Schultz, W., Dayan, P., and Montague, P.R. (1997). A neural substrate of prediction and reward. Science 275: 1593-1599.
  5. Glimcher, P.W. (2012). Understanding dopamine and reinforcement learning: the dopamine reward prediction error hypothesis. Proc Natl Acad Sci U S A 108 Suppl 3: 15647-15654.
  6. Reynolds, J.N., Hyland, B.I., and Wickens, J.R. (2001). A cellular mechanism of reward-related learning. Nature 413: 67-70.
  7. Aosaki, T., Graybiel, A.M., and Kimura, M. (1994). Effect of the nigrostriatal dopamine system on acquired neural responses in the striatum of behaving monkeys. Science 265: 412-415.
  8. Morris, G., Arkadir, D., Nevet, A., Vaadia, E., and Bergman, H. (2004). Coincident but distinct messages of midbrain dopamine and striatal tonically active neurons. Neuron 43: 133-143.
     (Optional)
  9. Yin, H.H., and Knowlton, B.J. (2006). The role of the basal ganglia in habit formation. Nat Rev Neurosci 7: 464-476.
  10. Aggarwal, M., and Wickens, J.R. (2012). A role for phasic dopamine neuron firing in habit learning. Neuron 72: 892-894.
  11. Reynolds, J.N., and Wickens, J.R. (2002). Dopamine-dependent plasticity of corticostriatal synapses. Neural Netw 15: 507-521.
  12. Wickens, J.R., Begg, A.J., and Arbuthnott, G.W. (1996). Dopamine reverses the depression of rat cortico-striatal synapses which normally follows high frequency stimulation of cortex in vitro. Neuroscience 70: 1-5.
  13. Cachope, R., Mateo, Y., Mathur, B.N., Irving, J., Wang, H.L., Morales, M., Lovinger, D.M., and Cheer, J.F. (2012). Selective activation of cholinergic interneurons enhances accumbal phasic dopamine release: setting the tone for reward processing. Cell Rep 2: 33-41.
  14. Threlfell, S., Lalic, T., Platt, N.J., Jennings, K.A., Deisseroth, K., and Cragg, S.J. (2012). Striatal dopamine release is triggered by synchronized activity in cholinergic interneurons. Neuron 75: 58-64.
  15. Wang, Z., Kai, L., Day, M., Ronesi, J., Yin, H.H., Ding, J., Tkatch, T., Lovinger, D.M., and Surmeier, D.J. (2006). Dopaminergic control of corticostriatal long-term synaptic depression in medium spiny neurons is mediated by cholinergic interneurons. Neuron 50: 443-452.
  16. Cragg, S.J. (2006). Meaningful silences: how dopamine listens to the ACh pause. Trends Neurosci 29: 125-131.