The extent and strength of electrical coupling between inferior olivary neurons is heterogeneous

Hoge GJ, Davidson KG, Yasumura T, Castillo PE, Rash JE, Pereda AE. The extent and strength of electrical coupling between inferior olivary neurons is heterogeneous. J Neurophysiol 105: 1089-1101, 2011. First published December 22, 2010; doi: 10.1152/jn.00789.2010.-Gap junctions constitute the only form of synaptic communication between neurons in the inferior olive (IO), which gives rise to the climbing fibers innervating the cerebellar cortex. Although its exact functional role remains undetermined, electrical coupling was shown to be necessary for the transient formation of functional compartments of IO neurons and to underlie the precise timing of climbing fibers required for cerebellar learning. So far, most functional considerations assume the existence of a network of permanently and homogeneously coupled IO neurons. Contrasting this notion, our results indicate that coupling within the IO is highly variable. By combining tracer-coupling analysis and paired electrophysiological recordings, we found that individual IO neurons could be coupled to a highly variable number of neighboring neurons. Furthermore, a given neuron could be coupled at remarkably different strengths with each of its partners. Freeze-fracture analysis of IO glomeruli revealed the close proximity of glutamatergic postsynaptic densities to connexin 36-containing gap junctions, at distances comparable to separations between chemical transmitting domains and gap junctions in goldfish mixed contacts, where electrical coupling was shown to be modulated by the activity of glutamatergic synapses. On the basis of structural and molecular similarities with goldfish mixed synapses, we speculate that, rather than being hard-wired, variations in coupling could result from glomerulus-specific long-term modulation of gap junctions. This striking heterogeneity of coupling might act to finely influence the synchronization of IO neurons, adding an unexpected degree of complexity to olivary networks.