Neural activity in the dorsal medial superior temporal area of monkeys represents retinal error during adaptive motor learning

Ephrin-mediated dendrite-dendrite repulsion regulates compartment-specific targeting of dendrites in the central nervous system

Neurons often forms synaptic contacts at specific subcellular domains to differentially regulate the activity of target neurons. However, how dendrites are targeted to specific subcellular domains of axons is rarely studied. Here we use Drosophila mushroom body out neurons (MBONs) and local dopaminergic neurons (DANs) as a model system to study how dendrites and axons are targeted to specific subcellular domains (compartments) of mushroom body axonal lobes to form synaptic contacts. We found that Ephrin-mediated dendrite-dendrite repulsion between neighboring compartments restricts the projection of MBON dendrites to their specific compartments and prevents the formation of ectopic synaptic connections with DAN axons in neighboring compartments. Meanwhile, DAN neurons in a subset of compartments may also depend on their partner MBONs for projecting their axons to a specific compartment and cover the same territory as their partner MBON dendrites. Our work reveals that compartment-specific targeting of MBON dendrites and DAN axons is regulated in part by a combination of dendrite-dendrite repulsion between neighboring compartments and dendrite-axon interactions within the same compartment.

50 Years Since the Marr, Ito, and Albus Models of the Cerebellum

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.