Abstract
Place cells in the mammalian hippocampus signal self-location with sparse spatially stable firing fields. Based on observation of place cell activity it is possible to accurately decode an animal’s location. The precision of this decoding sets a lower bound for the amount of information that the hippocampal population conveys about the location of the animal. In this work we use a novel recurrent neural network (RNN) decoder to infer the location of freely moving rats from single unit hippocampal recordings. RNNs are biologically plausible models of neural circuits that learn to incorporate relevant temporal context without the need to make complicated assumptions about the use of prior information to predict the current state. When decoding animal position from spike counts in 1D and 2D-environments, we show that the RNN consistently outperforms a standard Bayesian approach with either flat priors or with memory. In addition, we also conducted a set of sensitivity analysis on the RNN decoder to determine which neurons and sections of firing fields were the most influential. We found that the application of RNNs to neural data allowed flexible integration of temporal context, yielding improved accuracy relative to the more commonly used Bayesian approaches and opens new avenues for exploration of the neural code.
Highlights
Place cells, pyramidal neurons found in CA1 and CA3 of the mammalian hippocampus [1,2,3,4], exhibit spatially constrained receptive fields, referred to as place fields
When decoding animal position from spike counts in 1D and 2D-environments, we show that the recurrent neural network (RNN) consistently outperforms a standard Bayesian approach with either flat priors or with memory
We found that a machine learning approach using recurrent neural networks (RNNs) allowed us to predict the rodents’ true positions more accurately than a standard Bayesian method with flat priors as well as a Bayesian approach with memory
Summary
Pyramidal neurons found in CA1 and CA3 of the mammalian hippocampus [1,2,3,4], exhibit spatially constrained receptive fields, referred to as place fields. Upon exposure to a novel enclosure the firing correlates of place cells rapidly ‘remap’; place fields change their firing rate and relative position, forming a distinct representation for the new space [10,11,12]. For these reasons place cells are widely held to provide the neural basis of self-location, signalling the position of an animal relative to its environment and being a necessary element for the control of spatial behaviours, such as navigation, and the retention of spatial memories [2]. Hippocampal activity provides considerable information about an animal’s self-location the representation is dynamic: accumulating changes and sometimes encoding other variables both spatial and non-spatial
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
