Are single-peaked tuning curves tuned for speed rather than accuracy?

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Abstract

According to the efficient coding hypothesis, sensory neurons are adapted to provide maximal information about the environment, given some biophysical constraints. In early visual areas, stimulus-induced modulations of neural activity (or tunings) are predominantly single-peaked. However, periodic tuning, as exhibited by grid cells, has been linked to a significant increase in decoding performance. Does this imply that the tuning curves in early visual areas are sub-optimal? We argue that the time scale at which neurons encode information is imperative to understanding the advantages of single-peaked and periodic tuning curves. Here, we show that the possibility of catastrophic (large) errors creates a trade-off between decoding time and decoding ability. We investigate how decoding time and stimulus dimensionality affect the optimal shape of tuning curves for removing catastrophic errors. In particular, we focus on the spatial periods of the tuning curves for a class of circular tuning curves. We show an overall trend for minimal decoding time to increase with increasing Fisher information, implying a trade-off between accuracy and speed. This trade-off is reinforced whenever the stimulus dimensionality is high, or there is ongoing activity. Thus, given constraints on processing speed, we present normative arguments for the existence of the single-peaked tuning organization observed in early visual areas.

Data availability

Code has been made publicly available on Github (https://github.com/movitzle/Short_Decoding_Time)

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Author details

Movitz Lenninger

Division of Information Science and Engineering, KTH Royal Institute of Technology, Stockholm, Sweden

For correspondence
movitzle@kth.se

Competing interests
The authors declare that no competing interests exist.
Mikael Skoglund

Division of Information Science and Engineering, KTH Royal Institute of Technology, Stockholm, Sweden

Competing interests
The authors declare that no competing interests exist.
Pawel Andrzej Herman

Division of Computational Science and Technology, KTH Royal Institute of Technology, Stockholm, Sweden

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0001-6553-823X
Arvind Kumar

Division of Computational Science and Technology, KTH Royal Institute of Technology, Stockholm, Sweden

For correspondence
arvkumar@kth.se

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-8044-9195

Funding

Digital Futures

Movitz Lenninger
Mikael Skoglund
Pawel Andrzej Herman
Arvind Kumar

Vetenskapsrådet

Arvind Kumar

Institute of Advanced Studies Fellowship, University of Strasbourg, France

Arvind Kumar

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.