Absence of functional FMRP causes Fragile X syndrome. Abnormalities in synaptic processes in the cerebral cortex and hippocampus contribute to cognitive deficits in Fragile X patients. So far, the potential roles of cerebellar deficits have not been investigated. Here, we demonstrate that both global and Purkinje cell-specific knockouts of Fmr1 show deficits in classical delay eye-blink conditioning in that the percentage of conditioned responses as well as their peak amplitude and peak velocity are reduced. Purkinje cells of these mice show elongated spines and enhanced LTD induction at the parallel fiber synapses that innervate these spines. Moreover, Fragile X patients display the same cerebellar deficits in eye-blink conditioning as the mutant mice. These data indicate that a lack of FMRP leads to cerebellar deficits at both the cellular and behavioral levels and raise the possibility that cerebellar dysfunctions can contribute to motor learning deficits in Fragile X patients.

This volume includes papers originally presented at the 11th annual Computational Neuroscience Meeting (CNS 02) held in July 2002 at the Congress Plaza Hotel and Convention Center in Chicago, Illinois, USA. The CNS meetings bring together computational neuroscientists representing many different fields and backgrounds as well as many different experimental preparations and theoretical approaches. The papers published here range from pure experimental neurobiology, to neuro-ethology, mathematics, physics, and engineering. In all cases, the research described is focused on understanding how nervous systems compute. The actual subjects of the research include a highly diverse number of preparations, modeling approaches and analysis techniques. Accordingly, this volume reflects the breadth and depth of current research in computational neuroscience taking place throughout the world.

There is significant interest amongst neuroscientists in sharing neuroscience data and analytical tools. The exchange of neuroscience data and tools between groups affords the opportunity to differently re-analyze previously collected data, encourage new neuroscience interpretations and foster otherwise uninitiated collaborations, and provide a framework for the further development of theoretically based models of brain function. Data sharing will ultimately reduce experimental and analytical error. Many small Internet accessible database initiatives have been developed and specialized analytical software and modeling tools are distributed within different fields of neuroscience. However, in addition large-scale international collaborations are required which involve new mechanisms of coordination and funding. Provided sufficient government support is given to such international initiatives, sharing of neuroscience data and tools can play a pivotal role in human brain research and lead to innovations in neuroscience, informatics and treatment of brain disorders. These innovations will enable application of theoretical modeling techniques to enhance our understanding of the integrative aspects of neuroscience. This article, authored by a multinational working group on neuroinformatics established by the Organization for Economic Co-operation and Development (OECD), articulates some of the challenges and lessons learned to date in efforts to achieve international collaborative neuroscience.

Authors S.-I. Amari, F. Beltrame, J.G. Bjaalie, T. Dalkara, E. De Schutter, G.F. Egan*, N.H. Goddard, C. Gonzalez, S. Grillner, A. Herz, K.-P. Hoffmann, I. Jaaskelainen, S.H. Koslow, S.-Y. Lee, L. Matthiessen, P.L. Miller, F.M. da Silva, M. Novak, V. Ravindranath, R. Ritz, U. Ruotsalainen, V. Sebestra, S. Subramaniam, A.W. Toga, S. Usui, J. van Pelt, P. Verschure, D. Willshaw, A. Wrobel, T. Yiyuan

Cerebellar granule cells constitute the largest neurone population of the brain. Their axons run as parallel fibres along the coronal axis, and the one-dimensional spread of excitation that is expected to result from this arrangement is a key assumption of theories of cerebellar function. In many studies using various techniques, however, it was not possible to evoke such a beam-like propagation of excitation with natural stimuli.We recorded, in Crus 1 and 11 of anaesthetised rats, pairs of Golgi cells aligned along the parallel fibre axis and synchronising spontaneously. Each pair was subjected to two stimulation protocols: punctate and semi-continuous. Local punctate facial stimulation evoked distinct fast and late responses of variable strength and latency (fast: 4.0-10.2 ms; late: 13.6-22.7 ms). Semi-continuous stimulation with a brush increased the firing rate, and modified the precision and phase of synchronisation. Differences between a pair in response strength and phase to brush stimulation correlated strongly with the difference in latency to punctate stimulation.These observations were reproduced in a model of the granular layer. The stimulus activated a central patch of mossy fibres, and Golgi cells received short- and long-range excitation from mossy and parallel fibres, respectively. The strength and latency of the punctate response of a model Golgi cell were found to vary with its position, reflecting a systematic change in the contribution of mossy and parallel fibres to its excitation with distance from the activated patch. During brush stimulation, model Golgi cells inside the patch fired more precisely synchronised, whereas the other Golgi cells responded with a lag proportional to their distance from the patch, thereby reproducing the experimentally observed changes in synchronisation.Taken together with the previously reported large receptive fields of Golgi cells and with their spontaneous synchronisation, the variable, position-dependent latency of evoked Golgi cell responses indicates a beam-like spread of excitation along the parallel fibres in rat cerebellar cortex. (C) 2002 IBRO. Published by Elsevier Science Ltd. All rights reserved.

The granular layer of the cerebellar cortex consists of densely packed neuronal cells, classified into granule cells and large interneurons. In this study, we provide a comparative survey of large granular layer interneurons in the adult rat cerebellum based on both morphological and neurochemical criteria. To this end, double immunofluorescence histochemistry was performed by combining antibodies against the cytoplasmic antigen Rat-303, calretinin, the metabotropic glutamate receptor mGluR2 and somatostatin. Based on Rat-303/calretinin double immunohistochemistry, three distinct populations of large granular layer interneurons could be discerned: cells immunopositive for Rat-303, calretinin or both. Rat-303 or calretinin single-labeled cells represented Golgi cells and unipolar brush cells, respectively. Rat-303/calretinin double-labeled cells located just underneath the Purkinje cell layer represented Lugaro cells. Morphometrical analysis distinguished two populations of Rat-303-positive Golgi cells according to their location: vermis versus hemisphere. Immunostaining for the metabotropic glutamate receptor mGluR2 combined with Rat-303 or calretinin revealed that the majority of Golgi cells (about 90%) appeared to be mGluR2 positive. Lugaro cells were mGluR2 negative. In addition, a limited population of large polymorphous interneurons in the depth of the granular layer with morphological features resembling Golgi cells also displayed Rat-303/calretinin immunoreactivity and were mGluR2 negative. Double immunohistochemistry for Rat-303 and somatostatin revealed three populations of labeled cells in the depth of the granular layer. Besides double-labeled Golgi cells, Rat-303 or somatostatin single-labeled cells were present. Based on mGluR2/somatostatin and calretinin/somatostatin double immunostainings, Rat-303 single-labeled cells were found to correspond to Rat-303/calretinin-positive, mGluR2-negative Golgi-like cells, while the identity of somatostatin single-labeled cells remained unclear. The data presented in this article elaborate previous reports on the morphological and neurochemical differentiation of large interneurons in the rat cerebellar granular layer. In addition, they indicate that the current classification of these cells into Golgi cells, Lugaro cells and unipolar brush cells does not describe the observed neurochemical heterogeneity.

We demonstrate, by means of computer simulations of a realistic cerebellar circuit model, the existence of an optimal axonal length (connection radius) for establishing long-range synchronous oscillations. Beyond the optimal axonal length, conduction delays hamper synchronization. The conduction delays also codetermine the oscillation frequency, making synchrony frequency-dependent. Large conduction delays and also doublet firing may stabilize low-frequency oscillations.

Blood oxygenation level dependent contrast (BOLD) functional MRI responses at 7T were observed in the cerebellum of alpha-chloralose anaesthetised rats in response to innocuous electrical stimulation of a forepaw or hindpaw. The responses were imaged in both coronal and sagittal slices which allowed for a clear delineation and localisation of the observed activations. We demonstrate the validity of our fMRI protocol by imaging the responses in somatosensory cortex to the same stimuli and by showing a high level of reproducibility of the cerebellar responses. Widespread bilateral activations were found with mainly a patchy and medio-lateral band organization, more pronounced ipsilaterally. There was no overlap between the cerebellar activations caused by forepaw or hindpaw stimulation. Most remarkable was the overall horizontal organization of these responses: for both stimulation paradigms the patches and bands of activation were roughly positioned in either a cranial or caudal plane running antero-posteriorly through the whole cerebellum. This is the first fMRI study in the cerebellum of the rat. We relate our findings to the known projection patterns found with other techniques and to human fMRI studies. The horizontal organization found wasn't observed before in other studies using other techniques.

Abstract: A monoclonal antibody (mAb), termed UIA/ NEU/I/G1 (G1), that reacted with the astroglial marker glial fibrillary acidic protein (GFAP), or α‐albumin, is described. It was directed against a structural determinant of GFAP. The G1 mAb could be used for quantitative determination of GFAP in two‐site radiometric assays and for histoimmunological demonstration of GFAP. The G1 mAb reacted with the GFAP from rat as well as from man. The presence of several different molecular weight forms of GFAP in aqueous and detergent extracts from human brain was shown with the G1 mAb. The possible meaning of these forms is discussed.

Over the last years it has become increasingly clear that many neurons possess dendritic calcium channels. The cerebellar Purkinje cell is the classic example (Llinás and Sugimori 1980), but pyramidal cells in the cortex (Amitai et al. 1993) and in the hippocampus (Jaffe et al. 1994) also have high densities of calcium channels in their dendrite. The role of such channels in dendritic processing is not entirely clear. Spencer and Kandel (1961) first proposed that voltage-dependent dendritic channels could amplify large synaptic inputs, presumably resulting in a larger somatic response. Recently, Markram and Sakmann (1994) demonstrated in cortical pyramidal cells that single (subthreshold) EPSPs can cause activation of dendritic calcium channels. Similar results where reported for Purkinje cells (Eilers et al. 1995). We have previously used a detailed compartmental model of the cerebellar Purkinje cell (De Schutter and Bower 1994a) to study synaptic integration in an active dendrite In this model, dendritic calcium channels amplify small, synchronous synaptic inputs (De Schutter and Bower 1994c). Distal inputs get amplified more than proximal ones, resulting in a similar amplitude of the somatic response. We showed that the presence of continuous background synaptic inputs, i.e. small subthreshold excitatory inputs and inhibitory inputs, is essential to get amplification. These background inputs depolarize the Purkinje cell dendrite, bringing it closer to the activation threshold of its main calcium channel. Further study demonstrated that the background inputs themselves interact also with the dendritic calcium channels. We used the in vivo state of the Purkinje cell model, where background asynchronous excitatory and inhibitory inputs would cause the model to fire at a normal rhythm of about 65 Hz (De Schutter and Bower 1994b), but removed the firing mechanism by making the soma passive (De Schutter and Bower 1994c). This allowed us to study the background input induced fluctuations of the somatic membrane potential in a model with active dendrite compared to a totally passive model. To examine effects on somatic EPSP amplitude we sometimes synchronously activated 200 excitatory synapses, equally distributed over the dendrite. The membrane potential was more depolarized in the active dendrite model

Many physical systems of interest to scientists and engineers can be modeled using a partial differential equation extended along the dimensions of time and space. These equations are typically nonlinear with real-valued parameters that control the classes of behaviors that the model is able to produce. Unfortunately, these control parameters are often difficult to measure in the physical system. Consequently, the first task in developing a model is usually to search for appropriate parameter values. In a high dimensional system, this task potentially requires a prohibitive number of evaluations and it may be impossible or inappropriate to select a unique solution. We have applied evolutionary algorithms (EAs) to the problem of parameter selection in models of biologically realistic neurons. Our objective was not to find the "best" solution, but rather we sought to produce the manifold of high fitness solutions that best accounts for biological variability. The search space was high dimensional (> 100) and each function evaluation required from one minute to several hours of CPU time on high performance computers. Using this model and our goals as an example, we will: 1) review the problem from the neuroscience perspective; 2) discuss high performance computing aspects of the problem; 3) examine the suitability of EAs for the efficient optimization of this class of problems; and 4) describe and justify the specific EA implementation used to solve this problem.