Axonal Na⁺Channels Ensure Fast Spike Activation and Back-Propagation in Cerebellar Granule Cells

Abstract

In most neurons, Na+ channels in the axon are complemented by others localized in the soma and dendrites to ensure spike back-propagation. However, cerebellar granule cells are neurons with simplified architecture in which the dendrites are short and unbranched and a single thin ascending axon travels toward the molecular layer before bifurcating into parallel fibers. Here we show that in cerebellar granule cells, Na+ channels are enriched in the axon, especially in the hillock, but almost absent from soma and dendrites. The impact of this channel distribution on neuronal electroresponsiveness was investigated by multi-compartmental modeling. Numerical simulations indicated that granule cells have a compact electrotonic structure allowing excitatory postsynaptic potentials to diffuse with little attenuation from dendrites to axon. The spike arose almost simultaneously along the whole axonal ascending branch and invaded the hillock the activation of which promoted spike back-propagation with marginal delay (<200 micros) and attenuation (<20 mV) into the somato-dendritic compartment. These properties allow granule cells to perform sub-millisecond coincidence detection of pre- and postsynaptic activity and to rapidly activate Purkinje cells contacted by the axonal ascending branch.

Control of cerebellar granule cell output by sensory-evoked Golgi cell inhibition

“…Our understanding of the functional role of GABAergic inhibition of granule cells has been constrained by our limited knowledge of the temporal dynamics and varying contributions of phasic and spillover inhibition during sensory stimulation in vivo. The functional characterization of a classical feed-forward inhibitory circuit in the input layer of the cerebellum in vitro (6,14,56) led to the assumption that Golgi-cell inhibition plays an important role in regulating both the magnitude and precision of granule cell spike output (6,14,18). However, our findings suggest that the primary function of Golgicell inhibition in Crus II is not to enforce high temporal fidelity of sensory responses in granule cells, but instead to ensure sensory response uniformity across granule cells.…”

“…The prevailing view is that, when mossy fibers are activated, granule cells receive both monosynaptic excitation and disynaptic FFI from Golgi cells, providing temporally precise inhibitory input that narrows the window for the temporal summation of discrete mossy fiber inputs (6,14,18). This classical excitation-inhibition sequence forms the basis of a variety of contemporary cerebellar models (7,9,18,19). However, the exact temporal relationship between sensory-evoked excitation and inhibition in granule cells has never been determined in vivo.…”

Sumoylation of Na V 1.2 Channels Mediates the Early Response to Acute Hypoxia in Central Neurons

“…In ambient (21%) O 2 , CGN I Na activated and inactivated rapidly, showing the expected biophysical properties (Diwakar et al, 2009), including a mean peak I Na of À172 ± 20 pA/pF at À20 mV, a halfmaximal activation voltage (V ½ ) of À23 ± 0.5 mV, and a steady-state inactivation midpoint (SSI) of eLife digest Neurons in the brain require a continuous supply of oxygen for normal activity. If the level of oxygen in the brain decreases -for example when a blood vessel becomes blockedneurons begin to die, and permanent brain damage can result.…”

Temporal constrained objects for modelling neuronal dynamics

“…While Golgi neurons inhibit granule neurons via a feed-forward inhibition, the axons of granule cells extend as parallel fibres and excite Purkinje neurons. As in experiments (Diwakar et al, 2009), modelled granule neuron receives on an average four excitatory and four inhibitory connections. In this paper, a small scale granular layer circuitry with Purkinje neurons was modelled with temporal constrained objects (Fig.…”

“…The significantly large number of granule cells in the input layer of the cerebellum distinguishes cerebellum from the rest of the nervous system. The computational significance of these neurons has been a topic of interest and granule cell has received recent attention in computational modelling studies attributed to the numerosity, its electronically compact structure, simpler dendritic arbor and the signal recoding computations that it performs on the inputs that it receive from different brain regions (Diwakar et al, 2009;Solinas, Nieus & D'Angelo, 2010;D'Angelo, 2011). To reconstruct all the computational elements of the cerebellar circuitry and their convergence-divergence ratios, computationally effective methods may be needed to model circuit functions (Markram, 2006).…”