Golgi cells (GoCs) are the primary inhibitory interneurons of the granular layer of the cerebellum. Their inhibition of granule cells is central to operate the relay of excitatory inputs to the cerebellar cortex. Parallel fibers (PFs) establish synapses to the GoCs in the molecular layer; these synapses contain AMPA, N-methyl-D-aspartate (NMDA), and mostly group II metabotropic glutamate receptors. Long-term changes in the efficacy of synaptic transmission at the PF-GoC synapse have not been described previously. We used whole cell patch-clamp recordings of GoCs in acute rat cerebellar slices to study synaptic plasticity. We report that high-frequency burst stimulation of PFs, using a current-clamp or voltage-clamp induction protocol, gave rise to long-term depression (LTD) at the PF-GoC synapse. This form of LTD was not associated with persistent changes of paired-pulse ratio, suggesting a postsynaptic origin. Furthermore, LTD induction was not dependent on activation of NMDA receptors. PF-GoC LTD does require activation of specifically group II metabotropic glutamate receptors and of protein kinase A.

The phase-response curve (PRC), relating the phase shift of an oscillator to external perturbation, is an important tool to study neurons and their population behavior. It can be experimentally estimated by measuring the phase changes caused by probe stimuli. These stimuli, usually short pulses or continuous noise, have a much wider frequency spectrum than that of neuronal dynamics. This makes the experimental data high dimensional while the number of data samples tends to be small. Current PRC estimation methods have not been optimized for efficiently discovering the relevant degrees of freedom from such data. We propose a systematic and efficient approach based on a recently developed signal processing theory called compressive sensing (CS). CS is a framework for recovering sparsely constructed signals from undersampled data and is suitable for extracting information about the PRC from finite but high-dimensional experimental measurements. We illustrate how the CS algorithm can be translated into an estimation scheme and demonstrate that our CS method can produce good estimates of the PRCs with simulated and experimental data, especially when the data size is so small that simple approaches such as naive averaging fail. The tradeoffs between degrees of freedom vs. goodness-of-fit were systematically analyzed, which help us to understand better what part of the data has the most predictive power. Our results illustrate that finite sizes of neuroscientific data in general compounded by large dimensionality can hamper studies of the neural code and suggest that CS is a good tool for overcoming this challenge.