No abstract

Modern neural interfaces allow access to the activity of up to a million neurons within brain circuits. However, bandwidth limits often create a trade-off between greater spatial sampling (more channels or pixels) and the temporal frequency of sampling. Here we demonstrate that it is possible to obtain spatio-temporal super-resolution in neuronal time series by exploiting relationships among neurons, embedded in latent low-dimensional population dynamics. Our novel neural network training strategy, selective backpropagation through time (SBTT), enables learning of deep generative models of latent dynamics from data in which the set of observed variables changes at each time step. The resulting models are able to infer activity for missing samples by combining observations with learned latent dynamics. We test SBTT applied to sequential autoencoders and demonstrate more efficient and higher-fidelity characterization of neural population dynamics in electrophysiological and calcium imaging data. In electrophysiology, SBTT enables accurate inference of neuronal population dynamics with lower interface bandwidths, providing an avenue to significant power savings for implanted neuroelectronic interfaces. In applications to two-photon calcium imaging, SBTT accurately uncovers high-frequency temporal structure underlying neural population activity, substantially outperforming the current state-of-the-art. Finally, we demonstrate that performance could be further improved by using limited, highbandwidth sampling to pretrain dynamics models, and then using SBTT to adapt these models for sparsely-sampled data.

In many brain areas, neural populations act as a coordinated network whose state is tied to behavior on a moment-by-moment basis and millisecond timescale. Two-photon (2p) calcium imaging is a powerful tool to probe network-scale computation, as it can measure the activity of many individual neurons, monitor multiple layers simultaneously, and sample from identified cell types. However, estimating network states and dynamics from 2p measurements has proven challenging because of noise, inherent nonlinearities, and limitations on temporal resolution. Here we describe RADICaL, a deep learning method to overcome these limitations at the population level. RADICaL extends methods that exploit dynamics in spiking activity for application to deconvolved calcium signals, whose statistics and temporal dynamics are quite distinct from electrophysiologically-recorded spikes. It incorporates a novel network training strategy that exploits the timing of 2p sampling to recover network dynamics with high temporal precision. In synthetic tests, RADICaL infers network states more accurately than previous methods, particularly for high-frequency components. In real 2p recordings from sensorimotor areas in mice performing a “water grab” task, RADICaL infers network states with close correspondence to single-trial variations in behavior, and maintains high-quality inference even when neuronal populations are substantially reduced.

Since the outbreak of COVID-19, China and Japan have fought against the epidemic through high-level contacts, donation and so on. The Chinese proverb “mountains and rivers on foreign land, wind and moon under the same sky” gave warmth of friendship to the bilateral relations. However, since Yoshihide Suga won the election, the Sino-Japanese relations have taken a worsening turn. The current Prime Minister Fumio Kishida further takes counterbalancing China as top priority. The “anti-COVID-19 diplomacy” in 2020 had very limited improvement on Sino-Japanese relations. It is clear that since the implementation of the U.S. strategic competition policy against China, Japan, without hesitation, turns to the “national interest theory” and “international contribution theory” by positioning to counterbalance China. Japan has become one of the few Asian countries that have completely sided with US in the China–US strategic competition. Since the normalization of Sino-Japanese relations, the principle of “good-neighborhood” has been totally replaced by the seemingly determined approach of “counterbalance” proposed by Japanese politicians. Japan has gone beyond the issue of its neighboring areas, trying to assist the United States’ strategic containment of China, which has added a new variable to China’s neighboring diplomacy.