Understanding the cellular basis of neural computation

MHpurkinje

Prof Michael Häusser
Professor of Neuroscience
Tel: +44 (0)20 7679 6756
Email: m.hausser@ucl.ac.uk
MHimage1 Michael Häusser biography

Neurons are the basic cellular units of the brain, and are connected via synapses to form neural networks. One of the central questions in neuroscience is how particular tasks, or "computations", are implemented by neural networks to generate behaviour, and how patterns of activity are stored during learning. In the past, the prevailing view has been that information processing and storage in neural networks results mainly from properties of synapses and connectivity of neurons within the network. As a consequence, the contribution of single neurons to computation in the brain has long been underestimated. Recent work has shown, however, that the dendritic processes of single neurons, which receive most of the synaptic input, display an extremely rich repertoire of behaviour, and actively integrate their synaptic inputs to define the input-output relation of the neuron (1). Furthermore, the signalling mechanisms which have been discovered in dendrites have suggested new ways in which patterns of network activity could be stored and transmitted (2). These advances have refocused attention on how single neurons contribute to information processing and storage in the brain. The recent development of new experimental and theoretical techniques now offers the promise to link single-cell processing with higher levels of brain function. Our group is interested in understanding the cellular basis of neural computation, focusing on dendritic function and processing of synaptic input in relation to network activity in the intact brain. We are integrating approaches and techniques at different levels of brain function to study the cellular basis of information processing in the cerebellar and cerebral cortex. Our focus is on cerebellar Purkinje cells (3, 4) and cortical layer 5 pyramidal cells (5), which are the principal neurons in their respective networks. Techniques used include direct patch-clamp recordings from neuronal dendrites (figure 1), imaging ionic signals in dendrites and spines with two-photon laser-scanning microscopy (figure 2), and recording from multiple synaptically connected cells (figure 3).

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Figure 1 Simultaneous somatic and dendritic patch-clamp recording from a Purkinje cell in a cerebellar cortex slice. A, infrared differential interference contrast image. B, fluorescence image (Cascade Blue and Lucifer Yellow in somatic and dendritic pipettes, respectively).

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Figure 2 Synaptic connection between an interneuron and a postsynaptic Purkinje cell in the cerebellar cortex. The Purkinje cell was filled with Texas Red, and the interneuron with Lucifer Yellow, and reconstructed using confocal microscopy.

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Figure 3. Simultaneous quadruple patch-clamp recording from layer 5 pyramidal neurons in a cortical brain slice. Neurons were filled with a fluorescent calcium indicator and imaged with 2-photon laser-scanning microscopy. This approach is being used to investigate the location-dependence of synaptic plasticity in pyramidal neuron dendrites.

These techniques are applied in parallel to in-vitro and in-vivo preparations in order to investigate the details of cellular mechanisms while placing them in the context of network activity. The experimental approaches are being complemented by state-of-the-art modelling of cellular signalling and the dynamics of synaptic integration. At each stage of our work, our aim is to measure, using combined imaging and electrophysiological approaches, dendritic signals in vivo triggered by sensory processing, in order to ultimately link cellular mechanisms to behaviour.

Full publication list with PDFs

Selected publications:

  • London, M. & Häusser, M. (2005) Dendritic Computation Annual Review of Neuroscience 28:503 – 532 link
  • Mittmann, W., Koch, U. & Häusser, M. (2005) Feed-forward inhibition shapes the spike output of cerebellar Purkinje neurons Journal of Physiology 563:369-378 link
  • Monsivais, P., Clark, B.A., Roth, A. & Häusser, M (2005) Determinants of action potential propagation in cerebellar Purkinje cell axons. Journal of Neuroscience 25, 464-472. link
  • Clark, B.A., Monsivais, P., Branco, T., London, M., & Häusser, M. (2005) The site of action potential initiation in cerebellar Purkinje neurons. Nature Neuroscience 8:137-139 link
  • Loewenstein, Y., Mahon, S., Chadderton, P., Kitamura, K., Sompolinski, H., Yarom, Y. & Häusser, M (2005) Bistability of cerebellar Purkinje cells modulated by sensory stimulation. Nature Neuroscience 8:202-211 link
  • Chadderton, P., Margrie, T.W., Häusser, M. (2004) Integration of quanta in cerebellar granule cells during sensory processing Nature 428, 856-860 link
  • Häusser, M, & Mel B. (2003) Dendrites: bug or feature? Current Opinion in Neurobiology 13:372-383 link
  • London, M., Schreibman, A., Häusser, M., Larkum, M. & Segev, I (2003) The information efficacy of a synapse Nature Neuroscience 5, 332-40. link
  • Häusser, M., Major, G. & Stuart, G. (2001) Differential shunting of EPSPs by action potentials Science 291, 138-141. link
  • Stuart, G. & Häusser, M. (2001) Dendritic coincidence detection of EPSPs and action potentials Nature Neuroscience 4, 63-71. link
  • Vetter, P., Roth, A. & Häusser, M. (2001) Action potential propagation in dendrites depends on dendritic morphology. Journal of Neurophysiology 85, 926-937 link
  • Wang, S.S.H., Denk, W. & Häusser, M. (2000) Coincidence detection in single dendritic spines mediated by calcium release. Nature Neuroscience 3, 1266-1273 link
  • Hausser M, Spruston N, Stuart GJ. (2000) Diversity and dynamics of dendritic signaling Science 290:739-44. link