Comment by return0 | Hacker News Reader

Comment by return0 | Hacker News Reader

What a fun approach. It's true that traditional neuroscience methods are too crude to give insight to brain functions and results are often over-interpreted. Neuroscientists know that however [1], and that's why attempts to simulate large circuits like the blue brain project or the simulated cat model produced nothing more than "oscillation statistics". Despite their crudeness, there are cases where circuits (like the amygdala) were causally linked to behavior by early electrophysiology .

There is hope however, as there are now optical and molecular methods that make it possible to observe , activate, and inactivate individual neurons, which allows making causal inferences[2].

1: http://compneuro.uwaterloo.ca/files/publications/eliasmith.2...

2: https://www.sciencedaily.com/releases/2015/05/150528142815.h...

This paper doesn't describe "traditional" methods. The author test analyses used today. The first simulation specifically tests whether inactivating "cells" (components in the microprocessor) can let us make causal inferences. They conclude that it can't. Whether that's a fair conclusion is up for debate, but they are definitely critiquing the most cutting edge techniques in the field

Couple that, however, with the ability to tag the cells participating in an experience, the ability to specifically reactivate them, and the ability to correlate them with other experiences. Their analogy in general has a big flaw, in that we know already that the brain does not have the serial logic of a circuit. The output of the brain is also not predetermined behavior, but rather dynamics between large, sparse populations, which somehow combine in interesting ways. This problem may be interesting to study in a more rigorous theoretical manner, but i don't think their approach may be taken seriously as an indicator of anything.

What "traditional" methods are you referring to? Hubel and Wiesel mapped the functional anatomy of the visual cortex in the 1950s by poking at cat brains. We've learned more since but their original model is still largely correct.

fmri for example

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