
Firing rate adaptation within a continuous attractor neural network causes the neural activity bump to oscillate around the external input, accounting for theta phase precession and procession of individual neurons.


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Hippocampal place cells in freely moving rodents display both theta phase precession and procession, which is thought to play important roles in cognition, but the neural mechanism for producing theta phase shift remains largely unknown. Here we show that firing rate adaptation within a continuous attractor neural network causes the neural activity bump to oscillate around the external input, resembling theta sweeps of decoded position during locomotion. These forward and backward sweeps naturally account for theta phase precession and procession of individual neurons, respectively. By tuning the adaptation strength, our model explains the difference between “bimodal cells” showing interleaved phase precession and procession, and “unimodal cells” in which phase precession predominates. Our model also explains the constant cycling of theta sweeps along different arms in a T-maze environment, the speed modulation of place cells’ firing frequency, and the continued phase shift after transient silencing of the hippocampus. We hope that this study will aid an understanding of the neural mechanism supporting theta phase coding in the brain.

### Competing Interest Statement

The authors have declared no competing interest.

Firing rate adaptation affords place cell theta sweeps, phase precession and procession

firing-rate-adaptation-in-continuous-attractor-neural-networks-explains-theta-phase-shift-of-hippocampal-place-cells


Firing Rate Adaptation in Continuous Attractor Neural Networks Explains Theta Phase Shift of Hippocampal Place Cells
