Cerebellum as a neuronal machine

Christopher H. Yeo

Professor of Behavioural Neuroscience
Tel: +44 (0)20 7679 7377
E-mail: c.yeo@ucl.ac.uk

Current Lab. members:

  • Dr. Peter Gilbert
  • Michael Longley
  • Maria Andrs Alonso
  • Megan Sayyad
  • Peter Trigg

Collaborators:

  • Prof. Paul Dean
  • Dr. John Porrill
  • Dr. Nathan Lepora, University of Sheffield
  • Dr. Steve Edgley, University of Cambridge
  • Prof. Germund Hesslow, University of Lund
  • Prof. Richard Hawkes, University of Calgary
 
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Prof. Christopher Yeo obtained his BSc (1973) and PhD (1977) in Zoology and Comparative Physiology at Queen Mary College, University of London. He joined the MRC Unit on Neural Mechanisms of Behaviour at University College London (1977) as an MRC scientist and later as a senior scientist (1987). He was appointed as Reader in Behavioural Neuroscience (1995) and then Professor of Behavioural Neuroscience (2003) in the Dept. Anatomy and Developmental Biology at UCL. He is an Editor for Behavioral Brain Research and for NeuroReport.

Research

In analysing how the brain controls behaviour, an ideal approach is to characterise the activity of a real network of neurons with an essential role in the control of an identified behaviour. In selecting such a network for investigation, the cerebellum stands out. Its influence upon behaviour is ubiquitous. Long seen as an important regulator of reflex and voluntary movements, it is now recognized that the cerebellum is critically important in many aspects of sensorimotor control and learning. Recent evidence that cerebellar damage may be associated with dyslexia, autism and disturbances of time estimation have led to suggestions that the cerebellum is also important for cognitive function. Three special properties recommend the cerebellar neural network for investigation: First, the cerebellum has prodigious computational power – it contains more than half of all the neurons in the brain.

Second, because of its special architecture, the size of this neuronal population does not deter analysis. Cerebellar neurons are arranged in a regularly repeating, geometrical array to form a large set of regularly repeating microcircuits.

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The anatomical and physiological similarity of these microcircuits suggests a consistent type of information processing - the cerebellar algorithm.

Third, the microcircuits are mapped in an orderly fashion within the cerebellum. Each receives an appropriate set of sensory inputs but applies the computational result solely to a specific output region. For many cerebellar microcircuits this output is, ultimately, to a small set of motoneurons that control an individual muscle or group of synergist muscles. These unique features of the cerebellar architecture enable a very special approach to the analysis of behavioural control. A specific target movement can be selected and the properties of the cerebellar neurons contained only in those microcircuits that directly control that movement can be analysed. Since the cerebellum is specifically implicated in learning, a target movement that can be shown to undergo modification through learning can be analysed. This strategy enables us to investigate how, with specified inputs and outputs, local synaptic change in an identified set of cerebellar microcircuits can generate a globally intelligent behaviour.

A leading candidate behaviour for investigation is classical conditioning of the rabbit eyeblink/nictitating membrane response (NMR). Lesion and reversible inactivation studies have revealed its dependence upon the cerebellum but have not revealed how the essential neural plasticity is distributed across and within the cerebellar cortical and nuclear circuitry. Our work in defining how the cerebellum generates this behaviour is at several levels of analysis. We are working to:

  1. Describe how plasticity essential for eyeblink/NMR conditioning is partitioned between cortical and nuclear levels.
  2. Define whether learning-related cerebellar plasticity has properties similar to previously identified in vitro forms.
  3. Analyse how the relevant cerebellar Purkinje cells and nuclear neurons behave during reflex and learned eyeblinks and describe how their activities are related.
  4. Determine how Purkinje cell simple and complex spikes change their behaviour as learning proceeds.
  5. Use information from the empirical work to make descriptive, and then computational, models of cerebellar action that will explain how local synaptic learning rules result in overall intelligent behaviour.

Full list of publications with PDF links

Selected publications:

  • Kellett DO, Fukunaga I, Chen-Kubota E, Dean P, Yeo CH (2010) Memory consolidation in the cerebellar cortex. PLoS One 5: e11737
  • Lepora N, Porrill J, Yeo CH, Dean P (2010) Sensory prediction or motor control? Application of Marr-Albus type models of cerebellar function to classical conditioning. Front. Comp. Neurosci. 4:140
  • Mostofi A, Holtzman T, Grout AS, Yeo CH, Edgley SA (2010) Electrophysiological localisation of eyeblink-related microzones in rabbit cerebellar cortex. J Neurosci 30: 8920-8934
  • Lepora NF, Porrill J, Yeo CH, Evinger C, Dean P (2009) Recruitment in retractor bulbi muscle during eyeblink conditioning: EMG analysis and common-drive model. J Neurophysiol 102: 2498-2513
  • Lepora NF, Mavritsaki E, Porrill J, Yeo CH, Evinger C, Dean P (2007) Evidence from retractor bulbi EMG for linearized motor control of conditioned nictitating membrane responses. J Neurophysiol 98: 2074 –2088, 2007.
  • Fukunaga I, Yeo CH, Batchelor AM (2007) Potent and specific action of the mGlu1 antagonists YM-298198 and JNJ16259685 on synaptic transmission in rat cerebellar slices. British Journal of Pharmacology 151: 870-876.
  • Fukunaga I, Yeo CH, Batchelor AM (2007) The mGlu1 antagonist CPCCOEt enhances the climbing fibre response in Purkinje neurones independently of glutamate receptors. Neuropharmacology 52: 450-458.
  • Mavritsaki E, Lepora N, Porrill J, Yeo CH, Dean P (2006) Response linearity determined by recruitment strategy in detailed model of nictitating membrane control. Biol Cybern 96: 39-57
  • De Zeeuw CI, Yeo CH (2005) Time and tide in cerebellar memory formation. Curr Opinion Neurobiol 15:667-74
  • Cooke S, Attwell PJE, Yeo CH (2004) Temporal properties of cerebellar-dependent memory consolidation. J Neurosci 24:2934 –2941
  • Yeo CH (2004) Memory and the cerebellum. Current Neurology and Neuroscience. 4:87-89
  • Rogelj B, Hartmann CEA, Yeo CH, Hunt SP, Giese KP (2003) Contextual conditioning regulates the expression of brain-specific non-coding RNAs in hippocampus. Eur J Neurosci. 18:3089-96.
  • Attwell PJE, Ivarsson M, Millar L, Yeo CH (2002) Cerebellar mechanisms in eyeblink conditioning. Ann New York Acad Sci 978: 79-92
  • Attwell PJE, Cooke S, Yeo CH (2002) Cerebellar function in consolidation of a motor memory. Neuron 34:1011-1020
  • Sanchez M, Sillitoe RV, Attwell PJE, Ivarsson M, Rahman S, Yeo CH and Hawkes R (2002) Compartmentalization of the rabbit cerebellum revealed by zebrin II immunohistochemistry. J Comp Neurol 444:159-173
  • Attwell PJE, Rahman S, Yeo CH (2001) Acquisition of eyeblink conditioning is critically dependent upon normal function in cerebellar cortical lobule HVI. J Neurosci 21:5715-5722
  • Attwell PJE, Rahman S, Ivarsson M, Yeo CH (1999) Cerebellar cortical AMPA/kainate receptor blockade prevents performance of classically conditioned nictitating membrane responses. J. Neurosci 19:RC45
  • Yeo CH, Hesslow G (1998) Cerebellum and conditioned reflexes. Trends in Cognitive Science 2:322-337
  • Hesslow G, Yeo CH (1998) Cerebellum and learning - a complex problem. Science 280:1817-1818