ausblenden:
Schlagwörter:
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Zusammenfassung:
Soma location, dendrite morphology and synaptic innervation are key determinants of neuronal function. Unfortunately, conventional functional measurements of sensory-evoked activity in vivo yield limited structural information. In particular, when trying to infer mechanistic principles that underlie perception and behavior, interpretations from functional recordings of individual or small groups of neurons often remain ambiguous without detailed knowledge of the underlying network structures.
I will present a novel reverse engineering approach that allows investigating sensory-evoked signal flow through individual and ensembles of neurons within the context of their surrounding neural networks. To do so, spontaneous and sensory-evoked activity patterns are recorded from individual neurons in vivo. In addition, the complete 3D dendrite and axon projection patterns of such in vivo characterized neurons are reconstructed using a custom-designed semiautomatic tracing pipeline. Our semiautomatic tracing system rivals manual tracings from human experts in both accuracy and completeness. Specifically, the combination of these in vivo filling and reconstruction approaches recovered total axonal lengths far greater than previously observed. For example, intracortical axonal lengths of most pyramidal neurons in rodent sensory cortex exceed 10 centimeters per neuron. The axon of an individual thalamocortical neuron can even reach a length of up to 25 centimeters.
Finally, the hence reconstructed neurons are integrated into an anatomically realistic cortex model, which is based on high-resolution reconstructions of the cortex geometry, large-scale neuron counts and structural overlap between axons and dendrites. The model allows estimating the number and cell type-specific subcellular distribution of synapses. As a result, each neuron can be described by a rich set of parameters that allows investigating structure-function relationships and simulation experiments at single neuron and network levels.
