Papers › Disambiguating the role of noise correlations when decoding neural populations together
Disambiguating the role of noise correlations when decoding neural populations together
Hugo Gabriel Eyherabide
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One of the most controversial problems in neural decoding is quantifying the information loss caused by ignoring noise correlations during optimal brain computations. For more than a decade, the measure here called ΔIᴰᴸ has been believed exact. However, we have recently shown that it can exceed the information loss ΔIᴮ caused by optimal decoders constructed ignoring noise correlations. Unfortunately, the different information notions underlying ΔIᴰᴸ and ΔIᴮ, and the putative rigorous information-theoretical derivation of ΔIᴰᴸ, both render unclear whether those findings indicate either flaws in ΔIᴰᴸ or major departures from traditional relations between information and decoding. Here we resolve this paradox and prove that, under certain conditions, observing ΔIᴰᴸ >ΔIᴮ implies that ΔIᴰᴸ is flawed. Motivated by this analysis, we test both measures using neural populations that transmit independent information. Our results show that ΔIᴰᴸ may deem noise correlations more important when decoding the populations together than when decoding them in parallel, whereas the opposite may occur for ΔIᴮ. We trace these phenomena back, for ΔIᴮ, to the choice of tie-breaking rules, and for ΔIᴰᴸ, to unforeseen limitations within its information-theoretical foundations. Our study contributes with better estimates that potentially improve theoretical and experimental inferences currently drawn from ΔIᴰᴸ without noticing that it may constitute an upper bound. On the practical side, our results promote the design of optimal decoding algorithms and neuroprosthetics without recording noise correlations, thereby saving experimental and computational resources.
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