The misleading manifold?
The current debate (decoding vs causal relevance)
and a toy example I gave in the thread below
got me thinking about a related issue: how decoding may reflect structure more than function.
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These, and other, studies show that you can decode task-related signals from many brain areas.
But wouldn't we need causal manipulations to conclude that the brain "uses" them?
For example, maybe we can decode equally well from two areas. But, only one impacts behaviour when inactivated.
If we start from this circuit:
Input -> neuron_1 -> output
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⋮
↓
neuron_n
And record neurons 1 to n simultaneously (where n could be very large).
We can obtain a matrix of neural activity (neurons x time).
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A common approach to analysing this data would be to apply PCA (or another technique).
Yielding a matrix of population activity (d x time). Where d < the number of neurons.
A common interpretation of this would be that "the brain uses a low dimensional manifold to link this input-output".
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However,
In this case, from the anatomy (circuit diagram above), we know that only neuron_1 is involved in the computation (transforming the input to the output).
And the manifold we observe is misleading.
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In this case wouldn't we get that d=1 though and that would be the right conclusion in some sense? Unless we added noise perhaps?
Sep 8, 2025 15:02I think by adding a mixture of noise, delays etc, we may not end up at d=1.
But even if we did, many studies would describe these "population dynamics" as a line attractor.
Rather than just a single neuron acting on it's own.
