Abstract
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.
Highlights
Morphological neuron models have been instrumental in neuroscience (Segev and London, 2000)
For instance that N-methyl-D-aspartate (NMDA) channels (MacDonald and Wojtowicz, 1982) produce local dendritic all or none responses (Schiller et al, 2000; Major et al, 2008), or that dendritic Ca2+ spikes mediate coincidence detection between distal inputs and somatic action potentials (APs) (Larkum et al, 1999), have been combined in morphological models to arrive at a consistent picture of dendritic integration: the dendrite is an intricate system of semi-independent subunits (Mel, 1993; Poirazi et al, 2003b; Poirazi et al, 2003a), amenable to dynamic regulation (Poleg-Polsky et al, 2018; Wybo et al, 2019), and able to distinguish specific input patterns (Branco and Hausser, 2010; Laudanski et al, 2014)
We find that effective weight-rescale factors for synapses can be computed if temporal conductance fluctuations stay below a limit that depends on the difference in input resistance between the ablated branch and the proximal dendrite
Summary
Morphological neuron models have been instrumental in neuroscience (Segev and London, 2000). They are highly complex and consist of thousands of coupled compartments, each receiving multiple non-linear currents. The parameters of the models, typically fitted with evolutionary algorithms to electro-physiological recordings (Hay et al, 2011; Almog and Korngreen, 2014; Van Geit et al, 2016), number in the tens of thousands. The single-neuron fitting challenge, where model performance was measured on unseen spike trains, was not won by a biophysical model, but by an abstract spiking model (Gerstner and Naud, 2009; DiLorenzo and Victor, 2013). Many network-level observations can be explained without morphological models (Gerstner et al, 2012)
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