TLDR: Representation of the world doesn't seem to be locked to specific neurons or groups of neurons; over time, they drift around to completely different groups of neurons.
Someone here likened it to how programs can move around in computer memory. If neurons are the "silicon of the brain" or the "PHY" layer, the biggest problem in neuroscience IMHO is that we have virtually no understanding of what is the next layer up, (i.e. the "logical layer"). What is the equivalent of program counters, CPU instructions, etc. in the brain?
The brain is a nonlinear recurrent dynamical system and we are really in the dark as to how to break the dynamics down into understandable subcomponents.
To get an idea of the complexity, this paper by Randall Beer analyzes the possible behaviours of 1 and 2 neuron circuits:
Beer, R. D. (1995). On the Dynamics of Small Continuous-Time Recurrent Neural Networks. Adaptive Behavior, 3(4), 469–509. doi:10.1177/105971239500300405
Obviously this kind of analysis doesn't scale to 10^10 neurons.
I use this all the time (I call it $EXEC instead), and have gotten into the habit of starting all my scripts with EXEC=echo and put it on every command that does any file changes. It has saved my bacon many times.
It doesn't work all the time as the previous poster noted, but it is very low friction which is especially important when writing quick throw-away scripts.
Someone here likened it to how programs can move around in computer memory. If neurons are the "silicon of the brain" or the "PHY" layer, the biggest problem in neuroscience IMHO is that we have virtually no understanding of what is the next layer up, (i.e. the "logical layer"). What is the equivalent of program counters, CPU instructions, etc. in the brain?
The brain is a nonlinear recurrent dynamical system and we are really in the dark as to how to break the dynamics down into understandable subcomponents.
To get an idea of the complexity, this paper by Randall Beer analyzes the possible behaviours of 1 and 2 neuron circuits:
Beer, R. D. (1995). On the Dynamics of Small Continuous-Time Recurrent Neural Networks. Adaptive Behavior, 3(4), 469–509. doi:10.1177/105971239500300405
Obviously this kind of analysis doesn't scale to 10^10 neurons.