full-FORCE: A Target-Based Method for Training Recurrent Networks

Trained recurrent networks are powerful tools for modeling dynamic neural computations. We present a target-based method for modifying the full connectivity matrix of a recurrent network to train it to perform tasks involving temporally complex input/output transformations. The method introduces a second network during training to provide suitable "target" dynamics useful for performing the task. Because it exploits the full recurrent connectivity, the method produces networks that perform tasks with fewer neurons and greater noise robustness than traditional least-squares (FORCE) approaches. In addition, we show how introducing additional input signals into the target-generating network, which act as task hints, greatly extends the range of tasks that can be learned and provides control over the complexity and nature of the dynamics of the trained, task-performing network.Comment: 20 pages, 8 figure

Methods for Building Network Models of Neural Circuits

Artificial recurrent neural networks (RNNs) are powerful models for understanding and modeling dynamic computation in neural circuits. As such, RNNs that have been constructed to perform tasks analogous to typical behaviors studied in systems neuroscience are useful tools for understanding the biophysical mechanisms that mediate those behaviors. There has been significant progress in recent years developing gradient-based learning methods to construct RNNs. However, the majority of this progress has been restricted to network models that transmit information through continuous state variables since these methods require the input-output function of individual neuronal units to be differentiable. Overwhelmingly, biological neurons transmit information by discrete action potentials. Spiking model neurons are not differentiable and thus gradient-based methods for training neural networks cannot be applied to them. This work focuses on the development of supervised learning methods for RNNs that do not require the computation of derivatives. Because the methods we develop do not rely on the differentiability of the neural units, we can use them to construct realistic RNNs of spiking model neurons that perform a variety of benchmark tasks, and also to build networks trained directly from experimental data. Surprisingly, spiking networks trained with these non-gradient methods do not require significantly more neural units to perform tasks than their continuous-variable model counterparts. The crux of the method draws a direct correspondence between the dynamical variables of more abstract continuous-variable RNNs and spiking network models. The relationship between these two commonly used model classes has historically been unclear and, by resolving many of these issues, we offer a perspective on the appropriate use and interpretation of continuous-variable models as they relate to understanding network computation in biological neural circuits. Although the main advantage of these methods is their ability to construct realistic spiking network models, they can equally well be applied to continuous-variable network models. An example is the construction of continuous-variable RNNs that perform tasks for which they provide performance and computational cost competitive with those of traditional methods that compute derivatives and outperform previous non-gradient-based network training approaches. Collectively, this thesis presents efficient methods for constructing realistic neural network models that can be used to understand computation in biological neural networks and provides a unified perspective on how the dynamic quantities in these models relate to each other and to quantities that can be observed and extracted from experimental recordings of neurons

A cross-sectional study examining the nature and extent of interprofessional education in schools of pharmacy in the United Kingdom.

Interprofessional education can prepare the workforce for collaborative practice in complex health and social care systems. The aim of this study was to examine the nature and extent of interprofessional education in schools of pharmacy in the United Kingdom. An online questionnaire was developed using systems theory, published literature and input from an interprofessional expert panel. It included closed and open-ended questions and a demographic section. Following piloting, it was distributed to 31 schools of pharmacy. Descriptive statistics were used for quantitative data, a content analysis approach for qualitative data. Ten schools of pharmacy responded. All reported delivering compulsory interprofessional education. Most (80%) reported an interprofessional steering group overseeing development. Formative and/or summative assessment varied depending on year of study. Mechanism and purpose of evaluation varied, with respondents reporting Kirkpatrick Evaluation Model Levels 1-3 (100%;80%;70%). Two themes were identified: "Variation in Interprofessional Education Approaches and Opportunities" and "Factors Influencing Development and Implementation of Interprofessional Education". Formal teaching was mainly integrated into other modules; various pedagogic approaches and topics were used for campus-based activities. Respondents referred to planned interprofessional education during practice-based placements; some still at pilot stage. Overall, respondents agreed that practice-based placements offered opportunistic interprofessional education, but a more focused approach is needed to maximise student pharmacists' learning potential. Most interprofessional education offered in undergraduate pharmacy curricula in the United Kingdom is campus-based, the nature and extent of which varies between programmes. Very few examples of practice-based activities were reported. Results may inform future interprofessional education curricular developmen

The Three-Dimensional Expansion of the Ejecta from Tycho's Supernova Remnant

We present the first three-dimensional measurements of the velocity of various ejecta knots in Tycho's supernova remnant, known to result from a Type Ia explosion. Chandra X-ray observations over a 12-year baseline from 2003 to 2015 allow us to measure the proper motion of nearly 60 "tufts" of Si-rich ejecta, giving us the velocity in the plane of the sky. For the line of sight velocity, we use two different methods: a non-equilibrium ionization model fit to the strong Si and S lines in the 1.2-2.8 keV regime, and a fit consisting of a series of Gaussian lines. These methods give consistent results, allowing us to determine the red or blue shift of each of the knots. Assuming a distance of 3.5 kpc, we find total velocities that range from 2400 to 6600 km s$^{-1}$, with a mean of 4430 km s$^{-1}$. We find several regions where the ejecta knots have overtaken the forward shock. These regions have proper motions in excess of 6000 km s$^{-1}$. Some Type Ia supernova explosion models predict a velocity asymmetry in the ejecta. We find no such velocity asymmetries in Tycho, and discuss our findings in light of various explosion models, favoring those delayed detonation models with relatively vigorous and symmetrical deflagrations. Finally, we compare measurements with models of the remnant's evolution that include both smooth and clumpy ejecta profiles, finding that both ejecta profiles can be accommodated by the observations.Comment: Accepted for publication in ApJ. Some figures slightly degraded to reduce file siz

The Science Performance of JWST as Characterized in Commissioning

This paper characterizes the actual science performance of the James Webb Space Telescope (JWST), as determined from the six month commissioning period. We summarize the performance of the spacecraft, telescope, science instruments, and ground system, with an emphasis on differences from pre-launch expectations. Commissioning has made clear that JWST is fully capable of achieving the discoveries for which it was built. Moreover, almost across the board, the science performance of JWST is better than expected; in most cases, JWST will go deeper faster than expected. The telescope and instrument suite have demonstrated the sensitivity, stability, image quality, and spectral range that are necessary to transform our understanding of the cosmos through observations spanning from near-earth asteroids to the most distant galaxies.Comment: 5th version as accepted to PASP; 31 pages, 18 figures; https://iopscience.iop.org/article/10.1088/1538-3873/acb29

The impact of immediate breast reconstruction on the time to delivery of adjuvant therapy: the iBRA-2 study

Background: Immediate breast reconstruction (IBR) is routinely offered to improve quality-of-life for women requiring mastectomy, but there are concerns that more complex surgery may delay adjuvant oncological treatments and compromise long-term outcomes. High-quality evidence is lacking. The iBRA-2 study aimed to investigate the impact of IBR on time to adjuvant therapy. Methods: Consecutive women undergoing mastectomy ± IBR for breast cancer July–December, 2016 were included. Patient demographics, operative, oncological and complication data were collected. Time from last definitive cancer surgery to first adjuvant treatment for patients undergoing mastectomy ± IBR were compared and risk factors associated with delays explored. Results: A total of 2540 patients were recruited from 76 centres; 1008 (39.7%) underwent IBR (implant-only [n = 675, 26.6%]; pedicled flaps [n = 105,4.1%] and free-flaps [n = 228, 8.9%]). Complications requiring re-admission or re-operation were significantly more common in patients undergoing IBR than those receiving mastectomy. Adjuvant chemotherapy or radiotherapy was required by 1235 (48.6%) patients. No clinically significant differences were seen in time to adjuvant therapy between patient groups but major complications irrespective of surgery received were significantly associated with treatment delays. Conclusions: IBR does not result in clinically significant delays to adjuvant therapy, but post-operative complications are associated with treatment delays. Strategies to minimise complications, including careful patient selection, are required to improve outcomes for patients

full-FORCE demos for oscillation and ready-set-go tasks

<p>Simulations illustrating the results of full-FORCE.</p><p><br></p><p>Thanks to Eli Pollock ([email protected]) for the Python implementation.</p><p><br></p><p>Contact Brian DePasquale ([email protected]) for questions about the MATLAB implementation.</p><p><br></p><p>A more frequently maintained repository can be found here: http://www.princeton.edu/~briandd/.</p

Bursts of beta oscillation differentiate postperformance activity in the striatum and motor cortex of monkeys performing movement tasks

Studies of neural oscillations in the beta band (13–30 Hz) have demonstrated modulations in beta-band power associated with sensory and motor events on time scales of 1 s or more, and have shown that these are exaggerated in Parkinson’s disease. However, even early reports of beta activity noted extremely fleeting episodes of beta-band oscillation lasting <150 ms. Because the interpretation of possible functions for beta-band oscillations depends strongly on the time scale over which they occur, and because of these oscillations’ potential importance in Parkinson’s disease and related disorders, we analyzed in detail the distributions of duration and power for beta-band activity in a large dataset recorded in the striatum and motor-premotor cortex of macaque monkeys performing reaching tasks. Both regions exhibited typical beta-band suppression during movement and postmovement rebounds of up to 3 s as viewed in data averaged across trials, but single-trial analysis showed that most beta oscillations occurred in brief bursts, commonly 90–115 ms long. In the motor cortex, the burst probabilities peaked following the last movement, but in the striatum, the burst probabilities peaked at task end, after reward, and continued through the postperformance period. Thus, what appear to be extended periods of postperformance beta-band synchronization reflect primarily the modulated densities of short bursts of synchrony occurring in region-specific and task-time-specific patterns. We suggest that these short-time-scale events likely underlie the functions of most beta-band activity, so that prolongation of these beta episodes, as observed in Parkinson’s disease, could produce deleterious network-level signaling.National Institutes of Health (U.S.) (Grant R01 NS025529)United States. Office of Naval Research (Grant N00014-07-1-0903)United States. Defense Advanced Research Projects Agency (Grant NBCHC070105