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Conductance-based neuronal network models can help us understand how synaptic and cellular mechanisms underlie brain function. However, these complex models are difficult to develop and are inaccessible to most neuroscientists. Moreover, even the most biologically realistic network models disregard many 3D anatomical features of the brain. Here, we describe(More)
To act as computational devices, neurons must perform mathematical operations as they transform synaptic and modulatory input into output firing rate. Experiments and theory indicate that neuronal firing typically represents the sum of synaptic inputs, an additive operation, but multiplication of inputs is essential for many computations. Multiplication by(More)
We review our recent experimental and modeling results on how cerebellar Purkinje cells encode information in their simple spike trains and present a theory of the function of pauses and regular spiking patterns. The regular spiking patterns were discovered in extracellular recordings of simple spikes in awake and anesthetized rodents, where it was shown(More)
Many theories of cerebellar function assume that long-term depression (LTD) of parallel fiber (PF) synapses enables Purkinje cells to learn to recognize PF activity patterns. We have studied the LTD-based recognition of PF patterns in a biophysically realistic Purkinje-cell model. With simple-spike firing as observed in vivo, the presentation of a pattern(More)
We have identified a close homologue of L1 (CHL1) in the mouse. CHL1 comprises an N-terminal signal sequence, six immunoglobulin (Ig)-like domains, 4.5 fibronectin type III (FN)-like repeats, a transmembrane domain and a C-terminal, most likely intracellular domain of approximately 100 amino acids. CHL1 is most similar in its extracellular domain to chicken(More)
Significant inroads have been made to understand cerebellar cortical processing but neural coding at the output stage of the cerebellum in the deep cerebellar nuclei (DCN) remains poorly understood. The DCN are unlikely to just present a relay nucleus because Purkinje cell inhibition has to be turned into an excitatory output signal, and DCN neurons exhibit(More)
Neurons in the cerebellar nuclei (CN) receive inhibitory inputs from Purkinje cells in the cerebellar cortex and provide the major output from the cerebellum, but their computational function is not well understood. It has recently been shown that the spike activity of Purkinje cells is more regular than previously assumed and that this regularity can(More)
It has been suggested that information in the brain is encoded in temporal spike patterns which are decoded by a combination of time delays and coincidence detection. Here, we show how a multi-compartmental model of a cerebellar Purkinje cell can learn to recognise temporal parallel fibre activity patterns by adapting latencies of calcium responses after(More)
The goal of this study was to determine how the ÿt of passive parameters in a compart-mental model varies depending on the precise morphological reconstruction of the neuron. We performed whole-cell recordings of deep cerebellar nucleus neurons in brain slices, reconstructed the neuronal morphologies and converted them into detailed compartmental models. A(More)
It has been suggested that long-term depression (LTD) of parallel "bre (PF) synapses enables a cerebellar Purkinje cell (PC) to learn to recognise PF activity patterns. We investigate the recognition of PF patterns that have been stored by LTD of AMPA receptors in a multi-compartmental PC model with a passive soma. We "nd that a corresponding arti"cial(More)