Skilful precipitation nowcasting using deep generative models of radar.

Precipitation nowcasting, the high-resolution forecasting of precipitation up to two hours ahead, supports the real-world socioeconomic needs of many sectors reliant on weather-dependent decision-making1,2. State-of-the-art operational nowcasting methods typically advect precipitation fields with radar-based wind estimates, and struggle to capture important non-linear events such as convective initiations3,4. Recently introduced deep learning methods use radar to directly predict future rain rates, free of physical constraints5,6. While they accurately predict low-intensity rainfall, their operational utility is limited because their lack of constraints produces blurry nowcasts at longer lead times, yielding poor performance on rarer medium-to-heavy rain events. Here we present a deep generative model for the probabilistic nowcasting of precipitation from radar that addresses these challenges. Using statistical, economic and cognitive measures, we show that our method provides improved forecast quality, forecast consistency and forecast value. Our model produces realistic and spatiotemporally consistent predictions over regions up to 1,536 km × 1,280 km and with lead times from 5-90 min ahead. Using a systematic evaluation by more than 50 expert meteorologists, we show that our generative model ranked first for its accuracy and usefulness in 89% of cases against two competitive methods. When verified quantitatively, these nowcasts are skillful without resorting to blurring. We show that generative nowcasting can provide probabilistic predictions that improve forecast value and support operational utility, and at resolutions and lead times where alternative methods struggle.

Vector-based navigation using grid-like representations in artificial agents.

Deep neural networks have achieved impressive successes in fields ranging from object recognition to complex games such as Go1,2. Navigation, however, remains a substantial challenge for artificial agents, with deep neural networks trained by reinforcement learning3-5 failing to rival the proficiency of mammalian spatial behaviour, which is underpinned by grid cells in the entorhinal cortex 6 . Grid cells are thought to provide a multi-scale periodic representation that functions as a metric for coding space7,8 and is critical for integrating self-motion (path integration)6,7,9 and planning direct trajectories to goals (vector-based navigation)7,10,11. Here we set out to leverage the computational functions of grid cells to develop a deep reinforcement learning agent with mammal-like navigational abilities. We first trained a recurrent network to perform path integration, leading to the emergence of representations resembling grid cells, as well as other entorhinal cell types 12 . We then showed that this representation provided an effective basis for an agent to locate goals in challenging, unfamiliar, and changeable environments-optimizing the primary objective of navigation through deep reinforcement learning. The performance of agents endowed with grid-like representations surpassed that of an expert human and comparison agents, with the metric quantities necessary for vector-based navigation derived from grid-like units within the network. Furthermore, grid-like representations enabled agents to conduct shortcut behaviours reminiscent of those performed by mammals. Our findings show that emergent grid-like representations furnish agents with a Euclidean spatial metric and associated vector operations, providing a foundation for proficient navigation. As such, our results support neuroscientific theories that see grid cells as critical for vector-based navigation7,10,11, demonstrating that the latter can be combined with path-based strategies to support navigation in challenging environments.

Transmeningeal muscimol can prevent focal EEG seizures in the rat neocortex without stopping multineuronal activity in the treated area.

Muscimol has potent antiepileptic efficacy after transmeningeal administration in animals. However, it is unknown whether this compound stops local neuronal firing at concentrations that prevent seizures. The purpose of this study was to test the hypothesis that epidurally administered muscimol can prevent acetylcholine (Ach)-induced focal seizures in the rat neocortex without causing cessation of multineuronal activity. Rats were chronically implanted with a modified epidural cup over the right frontal cortex, with microelectrodes positioned underneath the cup. In each postsurgical experimental day, either saline or 0.005-, 0.05-, 0.5- or 5.0-mM muscimol was delivered through the cup, followed by a 20-min monitoring of the multineuronal activity and the subsequent delivery of Ach in the same way. Saline and muscimol pretreatment in the concentration range of 0.005-0.05 mM did not prevent EEG seizures. In contrast, 0.5-mM muscimol reduced the average EEG Seizure Duration Ratio value from 0.30±0.04 to 0. At this muscimol concentration, the average baseline multineuronal firing rate of 10.9±4.4 spikes/s did not change significantly throughout the 20-min pretreatment. Muscimol at 5.0mM also prevented seizures, but decreased significantly the baseline multineuronal firing rate of 7.0±1.8 to 3.7±0.9 spikes/s in the last 10 min of pretreatment. These data indicate that transmeningeal muscimol in a submillimolar concentration range can prevent focal neocortical seizures without stopping multineuronal activity in the treated area, and thus this treatment is unlikely to interrupt local physiological functions.

Predictive network modeling of the high-resolution dynamic plant transcriptome in response to nitrate.

BACKGROUND: Nitrate, acting as both a nitrogen source and a signaling molecule, controls many aspects of plant development. However, gene networks involved in plant adaptation to fluctuating nitrate environments have not yet been identified. RESULTS: Here we use time-series transcriptome data to decipher gene relationships and consequently to build core regulatory networks involved in Arabidopsis root adaptation to nitrate provision. The experimental approach has been to monitor genome-wide responses to nitrate at 3, 6, 9, 12, 15 and 20 minutes using Affymetrix ATH1 gene chips. This high-resolution time course analysis demonstrated that the previously known primary nitrate response is actually preceded by a very fast gene expression modulation, involving genes and functions needed to prepare plants to use or reduce nitrate. A state-space model inferred from this microarray time-series data successfully predicts gene behavior in unlearnt conditions. CONCLUSIONS: The experiments and methods allow us to propose a temporal working model for nitrate-driven gene networks. This network model is tested both in silico and experimentally. For example, the over-expression of a predicted gene hub encoding a transcription factor induced early in the cascade indeed leads to the modification of the kinetic nitrate response of sentinel genes such as NIR, NIA2, and NRT1.1, and several other transcription factors. The potential nitrate/hormone connections implicated by this time-series data are also evaluated.

Classification of patterns of EEG synchronization for seizure prediction.

OBJECTIVE: Research in seizure prediction from intracranial EEG has highlighted the usefulness of bivariate measures of brainwave synchronization. Spatio-temporal bivariate features are very high-dimensional and cannot be analyzed with conventional statistical methods. Hence, we propose state-of-the-art machine learning methods that handle high-dimensional inputs. METHODS: We computed bivariate features of EEG synchronization (cross-correlation, nonlinear interdependence, dynamical entrainment or wavelet synchrony) on the 21-patient Freiburg dataset. Features from all channel pairs and frequencies were aggregated over consecutive time points, to form patterns. Patient-specific machine learning-based classifiers (support vector machines, logistic regression or convolutional neural networks) were trained to discriminate interictal from preictal patterns of features. In this explorative study, we evaluated out-of-sample seizure prediction performance, and compared each combination of feature type and classifier. RESULTS: Among the evaluated methods, convolutional networks combined with wavelet coherence successfully predicted all out-of-sample seizures, without false alarms, on 15 patients, yielding 71% sensitivity and 0 false positives. CONCLUSIONS: Our best machine learning technique applied to spatio-temporal patterns of EEG synchronization outperformed previous seizure prediction methods on the Freiburg dataset. SIGNIFICANCE: By learning spatio-temporal dynamics of EEG synchronization, pattern recognition could capture patient-specific seizure precursors. Further investigation on additional datasets should include the seizure prediction horizon.

Effects of subdural application of lidocaine in patients with focal epilepsy.

Antiepileptic drug (AED) delivery directly into the neocortex has recently been shown to be able to both prevent and terminate focal seizures in rats. The present clinical experiment aimed to test the local effects of lidocaine delivered onto the pia mater adjacent to epileptogenic zones in human patients. Administration of lidocaine resulted in a marked diminishment of spike counts on all patients, with a decremental effect of lidocaine on the faster frequency elements of individual spikes and overall testing epochs. The direct cortical application of lidocaine appears to affect local epileptogenic activity in human patients with intractable focal epilepsy.