There’s a lot of competition in cognitive science about which metaphor to use for the brain. This has been going on for centuries and keeping pace with technological advancement.
In this article This post is a summary/update of this paper from Chris Eliasmith and is based off this blog comment , I would like to justify that they’re all wrong. That all metaphors are wrong when trying to explain the brain and it’s better to just see the brain for what it is, based off the empirical observations we have from experiments. As evidence for this, I’ll talk about Spaun, the world’s largest functioning brain model able to solve IQ puzzles and transfer knowledge between tasks. Spaun was enabled by the Neural Engineering Framework (NEF) and the Semantic Pointer Architecture (SPA). These technologies were used to transcend brain-metaphors, as well uniting research from previously dissimilar brain-metaphors.
The three main brain metaphors in use today are Some people would argue that Bayesian explanations of the brain are one of the newer paradigms. Whether Bayesian inference is happening in the brain and how it’s compatible with the NEF is the topic of another blog post. However, I believe saying the brain is a Bayesian machine falls to the same metaphoric pitfalls described in this post. Indeed, I would even go as far to say that it’s a subset of Symbolicism. :
Symbolicism The brain thinks with symbols like a computer and neurons are pointless implementation details, see ACT-R.
Dynamicism The brain is a dynamic system that we should describe with differential equations like a Watt Governor, also neurons should still be ignored.
Connectionism Everyone should be paying attention to neurons. The brain is neurons and connection weights.
Using the NEF and SPA, we’re able to use components from all the previously mentioned paradigms to create a new paradigm. This is analogous to the way waves and particles were combined to understand light in quantum physics.
Symbolicism We’re able to represent and manipulate vectors (multi-dimensional values) in neurons, which can be interpreted as symbols.
Dynamicism We’re also able to construct dynamical systems by feeding a neural network back into itself.
Connectionism All the “computations” or “information transformations” in our model are based on biologically plausible neurons.
This means we can take all the cool aspects from each of these systems and make cool things. Spaun uses symbols to solve the IQ puzzle I mentioned before, while dynamic systems is a more accurate description of it’s arm control and Connectionist Convolutional Neural Networks form its vision system.
However, you may have noticed I haven’t used any metaphors for NEF and SPA. That’s because there are none. Not to say its hard to explain. It isn’t. It’s just doesn’t fit into a specific metaphor. The NEF and SPA are just spiking neurons representing vectors manipulated via… Anyways, you end up just saying that the mind is a mind, the brain is a brain and a rose is a rose.
I believe this adhesion to trying to understand what computations the brain can do and then exploring it’s capabilities from there, gives the NEF and SPA it’s power for unification and explanation. It’s also why I’ve decided to commit two years of my life to it.