Hamker, F. H. | bioRxiv Science

bioRxiv2018

Spatial updating of attention across eye movements: A neuro-computational approach

While scanning our environment, the retinal image changes with every saccade. Nevertheless, the visual system anticipates where an attended target will be next and attention is updated to the new location. Recently, two different types of perisaccadic attentional updates were discovered: Predictive remapping of attention before saccade onset (Rolfs, Jonikaitis, Deubel, & Cavanagh, 2011) as well as lingering of attention after saccade (Golomb, Chun, & Mazer, 2008; Golomb, Pulido, Albrecht, Chun, & Mazer, 2010). We here propose a neurocomputational model located in LIP based on a previous model of perisaccadic space perception (Ziesche & Hamker, 2011, 2014). Our model can account for both types of updating of attention at a neural systems level. The lingering effect originates from the late updating of the proprioceptive eye position signal and the remapping from the early corollary discharge signal. We put these results in relationship to predictive remapping of receptive fields and show that both phenomena arise from the same simple, recurrent neural circuit. Thus, together with the previously published results, the model provides a comprehensive framework to discuss multiple experimental observations that occur around saccades.

neuroscience↗

bioRxiv2018

A statistical note on the detection of autoassociative dynamics in hippocampal place-cell sequences

The sequential activity of hippocampal place cells observed during sleep and awake resting is widely viewed as a neural correlate of memory processes. While recent work has advanced our understanding of the content represented during rest-related place-cell sequences, the nature of hippocampal population dynamics during sequential activity remains poorly understood. A recent experimental study has reported that place-cell sequences show a pattern of step-like movement, reminiscent of transitions between discrete attractor states (Pfeiffer and Foster, 2015). By contrast, previous theoretical models predict that the spatiotemporal structure of place-cell sequences should reflect the disribution of place fields, typically observed to be spatially smooth.\n\nMotivated by this discrepancy between models and experimental data, we performed a quantitative comparison between these results and the spike trains generated by a network model for the generation of place-cell sequences (Gonner et al., 2017). Although the model is based on continuous attractor network dynamics, we observed that the movement of sequential place representations was phase-locked to the population oscillation, highly similar to the experimental data interpreted as evidence for discrete attractor dynamics. To resolve this potential contradiction, we performed a detailed analysis of the methodology used to identify discrete attractor dynamics. Our results show that a previous approach to step size decoding is prone to a decoding artefact. We propose a modified approach to estimate step sizes which may help to characterize the underlying circuit dynamics in vivo.

neuroscience↗