Dendritic Computation

By now you are well equipped for a successful dive into this module's topic - "Cable theory and dendritic computations ". We are ready to make a conceptual “leap” and to discuss a fascinating topic. Namely, how the anatomical and electrical machinery of neurons endow neurons with computational capabilities. Computing the orientation of line and the direction of motion (in the visual system), or the location or intensity of sound (in the auditory system) and planning a movement for grasping a cup of coffee (in the motor system), are all computations that our brain performs effortlessly. The success of these computations is absolutely critical for our survival. Hubel and Wiesel (Nobel laureates in 1981) showed that nerve cells in the visual cortex of the cat are sensitive to the orientation of lines in the visual world (orientation selectivity). So that when you look at the world around you, you may “use” these cells to identify the angle of a tree (vertical) and of the eyes looking at you (horizontal). But how do nerve cells (and the brain as a whole) perform these computations? In the 1960's Wilfrid Rall regarded neurons as electrically-distributed elements (rather than as a “point” element and, consequently, he developed the “cable theory for dendrites” - highlighting the principles that govern the spread/attenuation of synaptic potentials (the cell’s input) from their dendritic site of origin to the soma/axon (the output) region. We will see that the cable properties of dendrites empower neurons with computational capabilities (e.g., neurons that compute the direction of motion). We will discuss a few early and more recent theoretical ideas on how the “neuronal hardware” – synapses, dendrites, axons and the signals that they carry - may implement elementary computations. We will end by providing a few recent and fantastic technological advances that enabled us, for the first time ever, to validate experimentally some of these theoretical ideas. We hope that you will enjoy this conceptual “leap” from discussing the biophysical properties of neurons to highlighting their computational functions.

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