After a two weeks break:

• Zhang and Rasband (2016) - Cytoskeletal control of axon domain assembly and function. Nice looking review.
• Ko et al (2016) - Serotonin modulates spike probability in the axon initial segment through HCN channels. There was some previous related work by Florence Cotel et al. (2013).
• Bird and Cuntz (2016) - Optimal Current Transfer in Dendrites. The paper includes in the supplementary information a derivation of the extended cable equation with variable diameter. Otherwise I think the approach might be quite interesting for the axon too.
• Srivastava et al (2016) - Motor control by precisely timed spike patterns. The demonstration was made previously by Vladimir Brezina in invertebrate muscles that muscle contraction is highly sensitive to single spikes, but this one is in vertebrates.
• Grunwald and Vitanyi (2010) - Shannon Information and Kolmogorov Complexity. I already knew about Kolmogorov complexity, but I just realized it might be an interesting alternative definition of information to use in neuroscience; in particular you can define the information in a single stimulus (eg image), in contrast with Shannon information (a single stimulus is just one element of a set).
• Jonas and Kording (2016) - Could a neuroscientist understand a microprocessor. None of the epistemological points made here are original, but what I like in this paper is it is a concrete demonstration that the reductionist methods used in systems neuroscience (tuning curves, connectomes etc) are just not going to work. It relates to many texts I have written on this blog, for example: Can we understand the brain by measuring its structure? And the series What is computational neuroscience? It is also related to the point I try to make in my essay Philosophy of the spike, that rate-based descriptions of neural activity are not causal, mechanistic descriptions. Think about having a rate-based view for transistors: you certainly could just as you can with neurons, but that won't get you to any mechanistic level of understanding, just tuning curves and transistor correlates.