Cerebellar Golgi cells in the rat: receptive fields and timing of responses to facial stimulation

Weak common parallel fibre synapses explain the loose synchrony observed between rat cerebellar golgi cells

1. In anaesthetized rats, pairs of cerebellar Golgi cells fired synchronously at rest, provided they were aligned along the parallel fibre axis. The observed synchrony was much less precise, however, than that which would be expected to result from common, monosynaptic parallel fibre excitation. 2. To explain this discrepancy, the precision and frequency of spike synchronization (i.e. the width and area of the central peak on the spike train cross-correlogram) were computed in a generic model for varying input, synaptic and neuronal parameters. 3. Correlation peaks between model neurons became broader, and peak area smaller, when the number of afferents increased and each single synapse decreased proportionally in strength. Peak width was inversely proportional to firing rate, but independent of the percentage of shared afferents. Peak area, in contrast, scaled with the percentage of shared afferents but was almost firing rate independent. 4. Broad correlation peaks between pairs of model neurons resulted from the loose spike timing between single model neurons and their afferents. This loose timing reflected a need for long-term synaptic integration to fire the neurons. Model neurons could accomplish this through firing rate adaptation mediated by a Ca2+-activated K+ channel. 5. We conclude that loose synchrony may be entirely explained by shared input from monosynaptic, non-synchronized afferents. The inverse relationship between peak width and firing rate allowed us to distinguish common parallel fibre input from firing rate covariance as a primary cause of loose synchrony between cerebellar Golgi cells in anaesthetized rats.

Miniature carrier with six independently moveable electrodes for recording of multiple single-units in the cerebellar cortex of awake rats

Ensemble recording in cerebellar cortex of awake rats presents unique methodological challenges not encountered when recording from the cerebral cortex or from deep brain structures with more homogeneous cell populations. Compared to the cerebral cortex, removal of dura over the cerebellum evokes pronounced swelling, and insertion of multiple closely spaced electrodes in the cerebellar cortex causes considerable dimpling (Welsh JP, Schwartz C. Multielectrode recording from the cerebellum. In: Nicolelis MAL, editor. Methods for Neural Ensemble Recordings, CRC Methods in Neuroscience Series. Boca Raton, FL: CRC Press LLC, 1999, pp. 79-100). Also, a repetitious and well-defined neural circuit characterizes the cerebellar cortex across its entire surface. With conventional multi-electrode methods, such as chronically implanted bundles or arrays of microwires, the risk of disrupting the cerebellar cytoarchitecture is high. In most conventional multi-electrode systems, electrodes have rather low impedance and cannot be moved independently after implantation. These limitations make proper unit isolation, necessary to identify each of the recorded cerebellar units, very difficult. We designed a lightweight (14 g), miniature (base plate: 19 x 23 mm; total height: 16 mm) multi-electrode system to allow for the chronic implantation of six independently moveable sharp electrodes with high impedance, in the cerebellar cortex. The six electrodes are arranged in a 2 x 3 matrix (inter-electrode distance: 0.6 mm). At any time after the implantation the vertical position of each individual electrode can be adjusted by screwing spring-loaded electrode heads up or down. The system preserves the integrity of the cerebellar cytoarchitecture, and enables easy isolation and identification of individual cerebellar units in awake, freely moving rats.

Using realistic models to study synaptic integration in cerebellar Purkinje cells

This review presents an approach to modeling which we call "experiments in computo". The use of realistic models makes it possible to generate new predictions that can be confirmed experimentally. Several examples are given of how this approach has improved our understanding of synaptic integration by the Purkinje cell active dendrite. The computer model was constructed to replicate neuronal behavior which has no direct relevance to synaptic integration: it was tuned to reproduce the response of Purkinje cells to current injection in vitro, which consists of a high frequency, regular rhythm of somatic Na+ spikes, interrupted by spontaneous dendritic Ca2+ spikes. The in vivo firing behavior of Purkinje cells is quite different as it consists of highly irregular simple spike firing only, without spontaneous dendritic Ca2+ spikes. The computer model predicted-that the Purkinje cell needs to receive a continuous background inhibitory synaptic drive in addition to the excitation by parallel fibers to obtain this typical in vivo firing. This prediction was confirmed by blocking inhibition during in vivo intracellular recordings. More recently, we demonstrated that the net inhibitory drive to the Purkinje cell dendrite has to be larger than the excitatory synaptic drive. Inhibition hyperpolarizes the dendrite compared to the soma, making it act as a current sink during most of the spiking cycle. These predictions have been confirmed with the dynamic clamp method in the cerebellar slice preparation. Synchronous focal excitatory input by parallel fiber leads in the model to activation of voltage-gated Ca2+ channels which amplify the somatic response by a variable amount. The variability of this graded amplification is due both to position of the input, effectively canceling the cable attenuation, and to the effect of preceding background input. Differences between the model and experimental results in this aspect can be explained by the relative hyperpolarized state of Purkinje cells in the in vitro experimental preparation. These studies led to a new theory about the function of long-term depression in the cerebellum which can explain recent experimental results. In conclusion, our modeling approach generated predictions which contradicted prevalent ideas on how the cerebellum, or neurons in general, works and led to experiments which otherwise would not have been carried out.