Data-driven reduction of dendritic morphologies with preserved dendro-somatic responses

Dendrites shape information flow in neurons. Yet, there is little consensus on the level of spatial complexity at which they operate. Through carefully chosen parameter fits, solvable in the least-squares sense, we obtain accurate reduced compartmental models at any level of complexity. We show that (back-propagating) action potentials, Ca2+ spikes, and N-methyl-D-aspartate spikes can all be reproduced with few compartments. We also investigate whether afferent spatial connectivity motifs admit simplification by ablating targeted branches and grouping affected synapses onto the next proximal dendrite. We find that voltage in the remaining branches is reproduced if temporal conductance fluctuations stay below a limit that depends on the average difference in input resistance between the ablated branches and the next proximal dendrite. Furthermore, our methodology fits reduced models directly from experimental data, without requiring morphological reconstructions. We provide software that automatizes the simplification, eliminating a common hurdle toward including dendritic computations in network models.

Data-driven reduction of dendritic morphologies with preserved dendro-somatic responses

Dendrites shape information flow in neurons. Yet, there is little consensus on the level of spatial complexity at which they operate. We present a flexible and fast method to obtain simplified neuron models at any level of complexity. Through carefully chosen parameter fits, solvable in the least squares sense, we obtain optimal reduced compartmental models. We show that (back-propagating) action potentials, calcium-spikes and NMDA-spikes can all be reproduced with few compartments. We also investigate whether afferent spatial connectivity motifs admit simplification by ablating targeted branches and grouping the affected synapses onto the next proximal dendrite. We find that voltage in the remaining branches is reproduced if temporal conductance fluctuations stay below a limit that depends on the average difference in input impedance between the ablated branches and the next proximal dendrite. Further, our methodology fits reduced models directly from experimental data, without requiring morphological reconstructions. We provide a software toolbox that automatizes the simplification, eliminating a common hurdle towards including dendritic computations in network models.

Spatial Asymmetry in the Mechanosensory Phenotypes of the C. elegans DEG/ENaC Gene mec-10

DEG/ENaC channels have been broadly implicated in mechanosensory transduction, yet many questions remain about how these proteins contribute to complexes that sense mechanical stimuli. In C. elegans, two DEG/ENaC channel subunits are thought to contribute to a gentle touch transduction complex: MEC-4, which is essential for gentle touch sensation, and MEC-10, whose importance is less well defined. By characterizing a mec-10 deletion mutant, we have found that MEC-10 is important, but not essential, for gentle touch responses in the body touch neurons ALM, PLM, and PVM. Surprisingly, the requirement for MEC-10 in ALM and PLM is spatially asymmetric; mec-10 animals show significant behavioral and physiological responses to stimulation at the distal end of touch neuron dendrites, but respond poorly to stimuli applied near the neuronal cell body. The subcellular distribution of a rescuing MEC-10::GFP translational fusion was found to be restricted to the neuronal cell body and proximal dendrite, consistent with the hypothesis that MEC-10 protein is asymmetrically distributed within the touch neuron process. These results suggest that MEC-10 may contribute to only a subset of gentle touch mechanosensory complexes found preferentially at the proximal dendrite.