Paper:
Spike Frequency Adaptation Implements Anticipative Tracking in Continuous Attractor Neural Networks
To extract motion information, the brain needs to compensate for time delays that are ubiquitous in neural signal transmission and processing. Here we propose a simple yet effective mechanism to implement anticipative tracking in neural systems. The proposed mechanism utilizes the property of spike-frequency adaptation (SFA), a feature widely observed in neuronal responses. We employ continuous attractor neural networks (CANNs) as the model to describe the tracking behaviors in neural systems. Incorporating SFA, a CANN exhibits intrinsic mobility, manifested by the ability of the CANN to support self-sustained travelling waves. In tracking a moving stimulus, the interplay between the external drive and the intrinsic mobility of the network determines the tracking performance. Interestingly, we find that the regime of anticipation effectively coincides with the regime where the intrinsic speed of the travelling wave exceeds that of the external drive. Depending on the SFA amplitudes, the network can achieve either perfect tracking, with zero-lag to the input, or perfect anticipative tracking, with a constant leading time to the input. Our model successfully reproduces experimentally observed anticipative tracking behaviors, and sheds light on our understanding of how the brain processes motion information in a timely manner.

Review:
Summary:The authors present a continuous attractor neural model which implements (anticipative) tracking. The authors show that spike frequency adaptation (SFA) can induce traveling waves under certain conditions. Interestingly, they show that the effects induced by SFA are similar to those that can be obtained by introducing asymmetric coupling between neurons as in [14], with the advantage that this method does not depend on hard-wired connections. The bulk of the paper is a theoretical analysis of a simplified model and simulations with the complete model for verification. The model can sustain traveling waves and reproduces both perfect tracking and anticipatory tracking. Comments to the authors:The paper is well written and interesting - especially the link between the model of [14] and the demonstration that SFA reproduces a similar effect.  I am not an expert in this field, so I find it hard to judge how novel this work is - for instance, you refer to [12], saying that it was already known that a continuous attractor network with SFA can exhibit traveling waves, but in the abstract you state that you propose the SFA mechanism to implement tracking. I think it would be good to clearly delineate what your new contributions are in the introduction.Are the time constants used in the model (e.g. at figure 2B) realistic? Could you comment on this? A factor 60 difference between \tau and \tau_v seems large to me.How well does the model reproduce experimental results? Such comparison would make the paper more interesting.Minor issues:l. 53. unpredictable flash: I don't think this has to do with the predictability of the flash - it can flash at regular intervals. The point is that the flashing stimulus does not move. l. 53-55. Despite ...; rephrase -> e.g., Although it is clear that the brain ..., it remains unclear ... .l. 59-60. moving speed of an object -> speed of a moving objectl. 81. anticipative time -> anticipative timesl 100, 2 ~ 5 ms \rho is -> 2 ~ 5 ms, \rho is the neural density, and l 150, dynamical property -> dynamical propertiesl 151, of adding the -> of addingfig 2 B, units of v_int?l 208, traveling wave -> traveling wavesl 215, the simulation -> simulationl 215, that at -> that inl 216, the Gaussian shape -> Gaussian shapel. 232, is dominated -> are dominatedl. 258, effect on -> effect ofl. 304-306, which gives to (twice) -> gives rise to?l. 320-321, SFA holds these appealing -> exhibits these properties.l. 354, Prefect -> Perfectl. 360, the condition that ... -> the approximationl. 361, We carry -> We carriedl. 265, of stimulus speed -> of stimulus speeds throughout the Ms.: holding a traveling wave -> contains a traveling wave / can exhibit traveling waves?