Multiple-Timescale Representations of Space: Linking Memory to Navigation

When navigating through space, we must maintain a representation of our position in real time; when recalling a past episode, a memory can come back in a flash. Interestingly, the brain's spatial representation system, including the hippocampus, supports these two distinct timescale functions. How are neural representations of space used in the service of both real-world navigation and internal mnemonic processes? Recent progress has identified sequences of hippocampal place cells, evolving at multiple timescales in accordance with either navigational behaviors or internal oscillations, that underlie these functions. We review experimental findings on experience-dependent modulation of these sequential representations and consider how they link real-world navigation to time-compressed memories. We further discuss recent work suggesting the prevalence of these sequences beyond hippocampus and propose that these multiple-timescale mechanisms may represent a general algorithm for organizing cell assemblies, potentially unifying the dual roles of the spatial representation system in memory and navigation. Expected final online publication date for the Annual Review of Neuroscience, Volume 45 is July 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Does feedback temperature dependence influence the slow mode of the climate response?

Atmosphere-Ocean General Circulation models (AOGCMs) are a necessary tool to understand climate dynamics on centennial timescales for which observations are scarce. We explore to which degree the temperature dependence of the climate radiative feedback influences the slow mode of the surface temperature response. We question whether long-term climate change is described by a single e-folding mode with a constant timescale which is commonly assumed to be independent of temperature or forcing and the evolution of time. To do so, we analyze AOGCM simulations which have an integration time of 1000 years and are forced by atmospheric CO2 concentrations ranging from 2 times (2X) to 8 times (8X) the preindustrial level. Our findings suggest that feedback temperature dependence strongly influences the equilibrium temperature response and adjustment timescale of the slow mode. The magnitude and timescale of the slow mode is approximately reproduced by a zero-dimensional energy balance model that has a constant effective heat capacity and incorporates a background feedback parameter and a coefficient for feedback temperature dependence. However, the effective heat capacity of the slow mode increases over time, which makes the adjustment timescale also time-dependent. The time-varying adjustment timescale can be approximated by a multiple timescale structure of the slow temperature response, or vice versa, a multiple timescale structure of the slow temperature response is described by a time-dependent timescale. The state-dependence and time-dependence of the adjustment timescale of long-term climate change puts into question common eigenmode decomposition with a fast and a slow timescale in the sense that the slow mode is not well described by a single linear e-folding mode with a constant timescale. We find that such an eigenmode decomposition is valid at a certain forcing level only, and an additional mode or a multiple mode and timescale structure of the slow adjustment is necessary to reproduce the details of AOGCM simulated long-term climate change.

Loss of Determinacy at Small Scales, with Application to Multiple Timescale and Nonsmooth Dynamics

A singularity is described that creates a forward time loss of determinacy in a two-timescale system, in the limit where the timescale separation is large. We describe how the situation can arise in a dynamical system of two fast variables and three slow variables or parameters, with weakly coupling between the fast variables. A wide set of initial conditions enters the [Formula: see text]-neighborhood of the singularity, and explodes back out of it to fill a large region of phase space, all in finite time. The scenario has particular significance in the application to piecewise-smooth systems, where it arises in the blow up of dynamics at a discontinuity and is followed by abrupt recollapse of solutions to “hide” the loss of determinacy, and yet leave behind a remnant of it in the global dynamics. This constitutes a generalization of a “micro-slip” phenomenon found recently in spring-coupled blocks, whereby coupled oscillators undergo unpredictable stick-slip-stick sequences instigated by a higher codimension form of the singularity. The indeterminacy is localized to brief slips events, but remains evident in the indeterminate sequencing of near-simultaneous slips of multiple blocks.

A Dynamic Model for Limb Selection

Two experiments and a model on limb selection are reported. In Experiment 1 left-handed and right-handed participants (N = 36) repeatedly used one hand for grasping a small cube. After a clear switch in the cube’s location, perseverative limb selection was revealed in both handedness groups. In Experiment 2 the cubes were presented in a clockwise and counter-clockwise sequence to right-handed participant (N = 15). A spatial delay in the switch point between right-hand use and left-hand use was observed. The model simulates the experiments, by implementing the multiple-timescale dynamics of the action-selection process underlying limb selection. It integrates two mechanisms that were earlier proposed to underlie this selection aspect of manual activity: limb dominance and attentional information. Finally, the model is used to simulate Gabbard et al.’s (1997) experiment, offering a concise coupling of strength and direction of handedness.

Validation of CHIRPS Precipitation Estimates over Taiwan at Multiple Timescales

The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), which incorporates satellite imagery and in situ station information, is a new high-resolution long-term precipitation dataset available since 1981. This study aims to understand the performance of the latest version of CHIRPS in depicting the multiple timescale precipitation variation over Taiwan. The analysis is focused on examining whether CHIRPS is better than another satellite precipitation product—the Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM) final run (hereafter IMERG)—which is known to effectively capture the precipitation variation over Taiwan. We carried out the evaluations made for annual cycle, seasonal cycle, interannual variation, and daily variation during 2001–2019. Our results show that IMERG is slightly better than CHIRPS considering most of the features examined; however, CHIRPS performs better than that of IMERG in representing the (1) magnitude of the annual cycle of monthly precipitation climatology, (2) spatial distribution of the seasonal mean precipitation for all four seasons, (3) quantitative precipitation estimation of the interannual variation of area-averaged winter precipitation in Taiwan, and (4) occurrence frequency of the non-rainy grids in winter. Notably, despite the fact that CHIRPS is not better than IMERG for many examined features, CHIRPS can depict the temporal variation in precipitation over Taiwan on annual, seasonal, and interannual timescales with 95% significance. This highlights the potential use of CHIRPS in studying the multiple timescale variation in precipitation over Taiwan during the years 1981–2000, for which there are no data available in the IMERG database.