Figure 2.

Concept of the inverse approach to elucidate dendritic function. Finding optimized model neurons for a given computational function corresponds to a mapping from function space to structure space. This mapping, from function to morphology, can be one-to-many; the mapping from morphology to function can be many-to-one. The “hypothesis generator” version of the approach starts with a function of general interest, such as input-order detection and proceeds to find a neuronal morphology optimized for that function. This morphology is then compared to the morphologies of real neurons, with similarities hinting at their functions. In the “function confirmation” variant of the approach, the cycle starts with a real neuron and a hypothesis about its computational function. The evolutionary algorithm then finds an optimized model neuron for this function, which in turn can be compared to the real neuron. Yet unmeasured features of the real neuron, such as conductance distributions, can be predicted from the optimized model neuron (neuron reconstruction from Furtak et al., 2007).