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Just because some phenomenon in brain activity correlates with consciousness (or aspects of consciousness), it does not explain how consciousness arises through it.

Neuropsychology must both describe information processing in the brain and do its part in building the abstracted interface to theories of cognition at higher levels of resolution.

Simon implies that human cognition is serial or parallel depending on the level of resolution one looks at it.

There has been extensive research into the phenomenon that is visually guided flight in flies.

Polarization of light is used by flies for long-range orientation wrt. sun.

Biomimetic (neural) robotics can provide feedback to neuroscience.

Although predecessors existed, Bayesian theory became popular in perceptual science in the 1980's and 1990's.

"Constructing a mathematically precise account of the brain has the potential to change our view of how it works."

"In order to understand a device one needs many different kinds of explanations." To understand vision, one needs theories that comply with the knowledge of the common man, the brain scientist, the experimental psychologist and which can be put to practical use.

Marr effectively argues normativity:

"... gone is any explanation in terms of neurons—except as a way of implementing a method. And present is a clear understanding of what is to be computed, how it is to be done, the physical assumptions on which the method is to be based, and some kind of analysis of algorithms that are capable of carrying it out."

It is important to make the distinction between different levels of understanding something (an information processing system) explicit.

Understanding that an abstract, mathematical description of the brain as an information-processing system is part of understanding the brain as a whole, one can rationally study

  • what is being processed,
  • why it is being processed,
  • how it is processed,
  • and whether or not processing it that way is optimal.

Marr speaks of vision as one process, whose task is to generate `a useful description of the world'. However, there is more than one actual goal of vision (though they share similar properties) and thus there are different representations and algorithms being used in the different parts of the brain concerned with these goals.

Normativity is one thing a computer scientist can contribute to neuroscience: explain what the brain should do and how what we find in nature implements that.

Abstraction is one thing a computer scientist can contribute to neuroscience: if you don't want to control a cat, don't use cat hardware (but be sure to use all the inspiration cat hardware can give you for your case).

Nature has had millions of years to optimize the performance of cognitive systems. It is therefore reasonable to assume that they perform optimally wrt. natural tasks and natural conditions.

Bayesian theory provides a framework to determine optimal strategies. Therefore, it makes sense to operate under the assumption that the processes we observe in nature can be understood as implementations of Bayes-optimal strategies.

According to Rucci et al., neuroscientists can use robots to quantitatively test and analyze their theories.

The degree to which neuroscientists can draw conclusions from computational models depends on biological accuracy.

If input to biologically plausible models is too dissimilar to natural input, then that can lead to non-natural behavior of the model.

Sensory noise in robotic experiments validates a model's robustness. It is always realistic (but not necessarily natural).