Not much on my mind this morning. Spent a long time this weekend helping my girlfriend with her masters dissertation research, delving into marketing and psychology journals. Pretty appalled by the quality of papers in that field. Often seem to do all the right analyses and statistical tests and I think I even saw corrections for multiple comparisons at times. But then they don't include a single plot! Bizarre. Am I seriously supposed to go through and actually read the results section? Read the numbers and the p values and test statistics? Ridiculous. To each their own I guess. The actually bad part was mostly the methods. Often terribly written and badly done. Brings back memories from my undergrad in reading really bad psychology papers - the marketing stuff seems to fall right in that corner of the field that gives psychology such a bad rep. i.e. I wonder how much of this stuff is replicable

We're currently redesigning the systems and theoretical neuroscience course taught at the Gatsby. It's a course designed for both students from the Gatsby (from maths/physics/computer science backgrounds) and students from the SWC (from biology backgrounds) to take together. The structure says it all: two lectures per week, one in theory and one in biology. How do we do this well? To start thinking about this I lined up all the topics one would want to cover in a "foundations of theoretical neuroscience" class and then thought about what systems/biology topics fit along side, e.g. coding - sensory systems, optimal control - motor systems, networks - ?, ... It's not so easy. But the hardest part is designing the biology lectures in a certain way so that they compliment the theory (I am starting to sound quite theory-biased, not sure if that's a good thing :s). I absolutely hate classic "intro to visual system" lectures where they go through the classical picture of the visual system that you get from a textbook without really delving into detail. But maybe that's necessary to be able to go any further? I'm not sure. I think the key thing is to take the Marrian approach and always start from "what is the problem this system is trying to solve?" and then "What would you expect a system built to solve this look like?" to "What does it actually look like?" but now with an emphasis on the computational problem. But this obviously gives a highly incomplete picture, since there are many many important things observed experimentally for which we have no idea what they are for. Can't leave those out: this is the "bottom-up" side of theory, whereby we try to come up with a theory to explain an observation (as opposed to "top-down" theory where you specify a computation and try to come up with a theory for how a brain-like thing could do it, e.g. supervised learning -> backprop -> Tim Lillicrap's research). You could just tack these on to the end. I don't think this would be the worst idea in the world. Once you've already set up our investigation of the visual system as looking for ways in which the brain solves some problem, that already gives you some perspective and a framework within which to think about what these new puzzling observations mean. Another task is convincing someone to build their lecture this way - it's a lot more work than your typical intro to ___ system. Also, this is a very top-down (maybe theory-centric) way of thinking about how to teach neuroscience. I think it's the right way, but does everyone else?

The reason this just popped into my head is because one area that I realized was totally underrepresented was cognitive psychology. I think the classic macrostructure in systems courses is (sensory systems)-(motor systems)-(cognitive and learning systems). Indeed, this is mainly how we have divided it. Shouldn't cognitive psychology have a place in that last section? It's not entirely clear, which I think is very sad. Usually that last section consists of reinforcement learning, conditioning, memory and decision making (in the sense of e.g. random dot stereograms), with their biological counterparts in reward systems, neuromodulators, hippocampus, LIP stuff. What about language? What about reasoning? There is loads of research out there on these higher level truly "cognitive" phenomena. But unfortunately we have no way of mapping these to biology. In my opinion none of that research is anywhere close to, mainly because they are phenomena unique to human beings (in some sense making them the most important to study) so we can't do calcium imaging or ephys - just fMRI or EEG from time to time. That said, there are a lot of classic results and interesting patterns in the data. Just because we can't relate it to brains, it doesn't necessarily mean we shouldn't include it in a systems course. Or does it? There is something to be said here about what systems neuroscience students should know. But I think there is also something deeper to be said about the direction of such research. How far can we take such investigations without grounding them in biology? There is a reason psychology is one of the fields suffering the most from the replication crisis...