Morality binds and blinds. People derive moral claims from emotional and intuitive notions. It can feel good and moral to do amoral things. Objective morality has to be tied to evidence what really is human wellbeing; not to moral intuitions that are adaptions to the benefit of ones ingroup; or post hoc thought experiments about knowledge.

Thanks for the post, I really liked the article overall. Nice general summary of the ideas. I agree with torekp. I also think that the term consciousness is too broad. Wanting to have a theory of consciousness is like wanting to have a "theory of disease". The overall term is too general and "consciousness" can mean many different things. This dilutes the conversation. We need to sharpen our semantic markers and not to rely on intuitive or prescientific ideas.Terms that do not "carve nature well at its joints" will lead our inquiry astray from the beginning.

When talking about consciousness one can mean for example:

-vigilance/wakefulness

-attention: focusing mental resources on specific information

-primary consciousness: having any form of subjective experience

-conscious access: how the attended information reaches awareness and becomes reportable to others

-phenomenal awareness/qualia

-sense of self/I

Neuroscience is needed to determine if our concepts are accurate (enough) in the first place. It can be that the "easy problem" is hard and the "hard problem" seems hard only because it engages ill posed intuitions.

Networks of the Brain by Olaf Sporns certainly doesn't cover all of computational neuroscience, but is a good accessible introduction to using the tools of network theory to gain a better understanding of brain function at many different levels.

From the cover text of How to Build a Brain it seems the main focus is on the architecture of SPAWN, and I suspect it does not actually give a proper introduction to other areas of computational neuroscience. That said, I wouldn't be surprised if it is the most enjoyable book to read on the topic, that you can find. I have read Computational Neuroscience by Hanspeter Mallot, which is very short, weird and not very good. I'm currently about halfway through Theoretical Neuroscience by Dayan and Abbott. My impression is, it might be decent for people with a strong physics/math background, it's OK if you have some prior knowledge about the topics (e.g. having visited a lecture) and rather bad otherwise.

Edit: My prof told me about Information Theory, Inference and Learning Algorithms (legal free online version), which is, as the title implies more about information theory and learning algorithms (so more mathy), but from the perspective of neuroscience, so it's missing a lot of the typical topics of computational neuroscience. I have just started reading it, but so far it seems really well written (4.35 rating on goodreads), and it also contains exercises and reflection questions.

Found this free online course if someone else is interested: https://www.coursera.org/course/compneuro

How to build a brain by Chris Eliasmith is one possibility.

Not particularly math-y, but people around me loved "how the mind works" from Steven Pinker.

So thinking probabilities existing as "things itself" taken to the extreme could lead one to the conclusion that one cant say much for example about single-case probabilities.

Thinking probabilities can exists in the territory leads to no such conclusion. Thinking probabilities exist only in the territory may lead to such a conclusion, but that is a strawman of the points that are being made.

It would be insane to deny that frequencies exist, or that they can be represented by a formal system derived from the Kolmogorov (or Cox) axioms.

It would also be insane to deny that beliefs exist, or that they can be represented by a formal system derived from the Kolmogorov (or Cox) axioms.

I think this confusion would all go away if people stopped worrying about the semantic meaning of the word "probability" and just specified whether they are talking about frequency or belief. It puzzles me when people insist that the formal system can only be isomorphic to one thing, and it is truly bizarre when they take sides in a holy war over which of those things it "really" represents. A rational decision maker genuinely needs both the concept of frequency and the concept of belief.

For instance, an agent may need to reason about the proportion (frequency) P of Everett branches in which he survives if he makes a decision, and also about how certain he is about his estimate of that probability. Let's say his beliefs about the probability P follow a beta distribution, or any other distribution bounded by 0 and 1. In order to make a decision, he may do something like calculate a new probability Q, which is the expected value of P under his prior. You can interpret Q as the agent's beliefs about the probability of dying, but it also has elements of frequency.

You can make the untestable claim that all Everett branches have the same outcome, and therefore that Q is determined exclusively by your uncertainty about whether you will live or die in all Everett branches. This would be Bayesian fundamentalism. You can also go to the other extreme and argue that Q is determined exclusively by P, and that there is no reason to consider uncertainty. That would be Frequentist fundamentalism. However, there is a spectrum between the two and there is no reason we should only allow the two edge cases to be possible positions. The truth is almost certainly somewhere in between.

Such as?