bims-climfi Biomed News
on Cerebellar cortical circuitry
Issue of 2020‒07‒05
two papers selected by
Jun Maruta
Mount Sinai Health System


  1. Cerebellum. 2020 Jul 02.
    Lippiello P, Hoxha E, Tempia F, Miniaci MC.
      G-protein-coupled inwardly rectifying potassium (GIRK) channels contribute to the resting membrane potential of many neurons and play an important role in controlling neuronal excitability. Although previous studies have revealed a high expression of GIRK subunits in the cerebellum, their functional role has never been clearly described. Using patch-clamp recordings in mice cerebellar slices, we examined the properties of the GIRK currents in Purkinje cells (PCs) and investigated the effects of a selective agonist of GIRK1-containing channels, ML297 (ML), on PC firing and synaptic plasticity. We demonstrated that GIRK channel activation decreases the PC excitability by inhibiting both sodium and calcium spikes and, in addition, modulates the complex spike response evoked by climbing fiber stimulation. Our results indicate that GIRK channels have also a marked effect on synaptic plasticity of the parallel fiber-PC synapse, as the application of ML297 increased the expression of LTP while preventing LTD. We, therefore, propose that the recruitment of GIRK channels represents a crucial mechanism by which neuromodulators can control synaptic strength and membrane conductance for proper refinement of the neural network involved in memory storage and higher cognitive functions.
    Keywords:  Cerebellum; GIRK; Neuromodulation; Purkinje cell; Synaptic plasticity
    DOI:  https://doi.org/10.1007/s12311-020-01158-y
  2. Neuroscience. 2020 Jun 26. pii: S0306-4522(20)30396-1. [Epub ahead of print]
    Kawato M, Ohmae S, Hoang H, Sanger T.
      Fifty years have passed since David Marr, Masao Ito, and James Albus proposed seminal models of cerebellar functions. These models share the essential concept that parallel-fiber-Purkinje-cell synapses undergo plastic changes, guided by climbing-fiber activities during sensorimotor learning. However, they differ in several important respects, including holistic versus complementary roles of the cerebellum, pattern recognition versus control as computational objectives, potentiation versus depression of synaptic plasticity, teaching signals versus error signals transmitted by climbing-fibers, sparse expansion coding by granule cells, and cerebellar internal models. In this review, we evaluate different features of the three models based on recent computational and experimental studies. While acknowledging that the three models have greatly advanced our understanding of cerebellar control mechanisms in eye movements and classical conditioning, we propose a new direction for computational frameworks of the cerebellum, that is, hierarchical reinforcement learning with multiple internal models.
    DOI:  https://doi.org/10.1016/j.neuroscience.2020.06.019