Rapid learning of predictive maps with STDP and theta phase precession

The predictive map hypothesis is a promising candidate principle for hippocampal function. A favoured formalisation of this hypothesis, called the successor representation, proposes that each place cell encodes the expected state occupancy of its target location in the near future. This predictive framework is supported by behavioural as well as electrophysiological evidence and has desirable consequences for both the generalisability and efficiency of reinforcement learning algorithms. However, it is unclear how the successor representation might be learnt in the brain. Error-driven temporal difference learning, commonly used to learn successor representations in artificial agents, is not known to be implemented in hippocampal networks. Instead, we demonstrate that spike-timing dependent plasticity (STDP), a form of Hebbian learning, acting on temporally compressed trajectories known as 'theta sweeps', is sufficient to rapidly learn a close approximation to the successor representation. The model is biologically plausible - it uses spiking neurons modulated by theta-band oscillations, diffuse and overlapping place cell-like state representations, and experimentally matched parameters. We show how this model maps onto known aspects of hippocampal circuitry and explains substantial variance in the temporal difference successor matrix, consequently giving rise to place cells that demonstrate experimentally observed successor representation-related phenomena including backwards expansion on a 1D track and elongation near walls in 2D. Finally, our model provides insight into the observed topographical ordering of place field sizes along the dorsal-ventral axis by showing this is necessary to prevent the detrimental mixing of larger place fields, which encode longer timescale successor representations, with more fine-grained predictions of spatial location

How to Stay Curious while avoiding Noisy TVs using Aleatoric Uncertainty Estimation

When extrinsic rewards are sparse, artificial agents struggle to explore an environment. Curiosity, implemented as an intrinsic reward for prediction errors, can improve exploration but it is known to fail when faced with action-dependent noise sources (‘noisy TVs’). In an attempt to make exploring agents robust to noisy TVs, we present a simple solution: aleatoric mapping agents (AMAs). AMAs are a novel form of curiosity that explicitly ascertain which state transitions of the environment are unpredictable, even if those dynamics are induced by the actions of the agent. This is achieved by generating separate forward predictions for the mean and aleatoric uncertainty of future states, with the aim of reducing intrinsic rewards for those transitions that are unpredictable. We demonstrate that in a range of environments AMAs are able to circumvent actiondependent stochastic traps that immobilise conventional curiosity driven agents. Furthermore, we demonstrate empirically that other common exploration approaches—previously thought to be immune to agent-induced randomness—can be trapped by stochastic dynamics. Code to reproduce our experiments is provided

Pilot study of positive airway pressure usage, patient journey and program engagement for users of a digital obstructive sleep apnea program

PurposeThis single-arm, decentralized pilot study assessed patient journey, positive airway pressure (PAP) usage and program satisfaction for users of an entirely virtual telemedicine program for obstructive sleep apnea (OSA) diagnosis and management. This analysis focuses specifically on the subset of participants in the program who were diagnosed with OSA and prescribed PAP therapy.MethodsThe Verily Clinical Studies Platform was used for virtual screening, consent, and enrolling eligible patients from North Carolina and Texas. After completing the virtual OSA diagnosis process, participants diagnosed with OSA and prescribed PAP therapy downloaded the program's mobile app. The app featured tools such as educational content, live coaching support, and motivational enhancement.ResultsOf the patients included in this analysis (N = 105), the majority were female (58%), and white (90%). The mean time from first televisit to PAP initiation was 29.2 (SD 12.8) days and f 68 out of the 105 patients (65%) reached 90-day adherence. On average, patients used their PAP device for 4.4 h per day, and 5.4 h on days used. Engagement with the app was associated with higher rates of PAP adherence. Adherent individuals used the mobile app 52 out of the 90 days on average, compared to non-adherent individuals who used the app on 35 out of the 90 days on average (p = 0.0003).ConclusionsAll of the 105 patients in this program diagnosed with OSA and prescribed PAP therapy were able to efficiently complete the entire OSA diagnostic pathway. The majority of these individuals also were able to adhere to their prescribed PAP therapy and had clinically meaningful PAP usage rates over the 90 days of therapy. Future studies might further evaluate the impact of this type of end-to-end virtual program on longer-term adherence and clinical outcomes over time.Clinical Trial Registrationhttps://clinicaltrials.gov/ct2/show/NCT04599803?term=NCT04599803&draw=2&rank=1, identifier NCT04599803

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Parenting About Complex Issues: Qualitative Data about Desired Parental Involvement

https://digitalcommons.usf.edu/student_images/1011/thumbnail.jp

Parenting About Complex Issues: Qualitative Data about Desired Parental Involvement

https://digitalcommons.usf.edu/student_images/1011/thumbnail.jp