Organizing recurrent network dynamics by task-computation to enable continual learning

Biological systems face dynamic environments that require continual learning. It is not well understood how these systems balance the tension between flexibility for learning and robustness for memory of previous behaviors. Continual learning without catastrophic interference also remains a challenging problem in machine learning. Here, we develop a novel learning rule designed to minimize interference between sequentially learned tasks in recurrent networks. Our learning rule preserves network dynamics within activity-defined subspaces used for previously learned tasks. It encourages dynamics associated with new tasks that might otherwise interfere to instead explore orthogonal subspaces, and it allows for reuse of previously established dynamical motifs where possible. Employing a set of tasks used in neuroscience, we demonstrate that our approach successfully eliminates catastrophic interference and offers a substantial improvement over previous continual learning algorithms. Using dynamical systems analysis, we show that networks trained using our approach can reuse similar dynamical structures across similar tasks. This possibility for shared computation allows for faster learning during sequential training. Finally, we identify organizational differences that emerge when training tasks sequentially versus simultaneously

Prior context in audition informs binding and shapes simple features

A perceptual phenomenon is reported, whereby prior acoustic context has a large, rapid and long-lasting effect on a basic auditory judgement. Pairs of tones were devised to include ambiguous transitions between frequency components, such that listeners were equally likely to report an upward or downward ‘pitch’ shift between tones. We show that presenting context tones before the ambiguous pair almost fully determines the perceived direction of shift. The context effect generalizes to a wide range of temporal and spectral scales, encompassing the characteristics of most realistic auditory scenes. Magnetoencephalographic recordings show that a relative reduction in neural responsivity is correlated to the behavioural effect. Finally, a computational model reproduces behavioural results, by implementing a simple constraint of continuity for binding successive sounds in a probabilistic manner. Contextual processing, mediated by ubiquitous neural mechanisms such as adaptation, may be crucial to track complex sound sources over time

Learning shapes cortical dynamics to enhance integration of relevant sensory input

Adaptive sensory behavior is thought to depend on processing in recurrent cortical circuits, but how dynamics in these circuits shapes the integration and transmission of sensory information is not well understood. Here, we study neural coding in recurrently connected networks of neurons driven by sensory input. We show analytically how information available in the network output varies with the alignment between feedforward input and the integrating modes of the circuit dynamics. In light of this theory, we analyzed neural population activity in the visual cortex of mice that learned to discriminate visual features. We found that over learning, slow patterns of network dynamics realigned to better integrate input relevant to the discrimination task. This realignment of network dynamics could be explained by changes in excitatory-inhibitory connectivity among neurons tuned to relevant features. These results suggest that learning tunes the temporal dynamics of cortical circuits to optimally integrate relevant sensory input

Human African Trypanosomiasis and challenges to its control in Urambo, Kasulu and Kibondo Districts, western Tanzania

A study was carried out to determine the prevalence and management of Human African Trypanosomiasis (HAT) in Urambo, Kasulu and Kibondo districts of western Tanzania. Parasitological surveys for trypanosome and other blood parasites were conducted in selected villages. Interviews with health workers were conducted to explore facility capacity to diagnose and manage HAT. Community knowledge on tsetse and availability of trypanocidal drugs was explored. Results showed that, although health facility records showed HAT is an important public health problem in the three districts, typanosomes were found in 0.6% of the examined individuals in Urambo district only. Malaria parasites with a prevalence of 12.1%, 19.7% and 9.7%, in Urambo, Kibondo and Kasulu, respectively were detected in blood samples from the same individuals examined for trypanosomes. There was poor capacity for most of the health facilities in the diagnosis, treatment and control of HAT. In both districts, communities were knowledgeable of the tsetse identity (82.4%) and had experienced tsetse bites (94%). The majority (91.4%) of the community members knew that they were at risk of acquiring HAT. However, only 29% of the respondents knew that anti-trypanocidal drugs were readily available free of charge from health care facilities. Late treatment seeking behaviour was common in Kasulu and Urambo districts. In conclusion, health facilities in western Tanzania are faced with problems of poor capacity to diagnose and manage HAT and that treatment seeking behaviour among the communities at risk is poor. Efforts should be made to strengthen the capacity of the health facility to handle HAT cases and health education to the population at risk. Keywords: Human African Trypanosomiasis, diagnosis, control, TanzaniaTanzania Health Research Bulletin Vol. 8 (2) 2006: pp. 80-8

Fast, scalable, Bayesian spike identification for multi-electrode arrays

We present an algorithm to identify individual neural spikes observed on high-density multi-electrode arrays (MEAs). Our method can distinguish large numbers of distinct neural units, even when spikes overlap, and accounts for intrinsic variability of spikes from each unit. As MEAs grow larger, it is important to find spike-identification methods that are scalable, that is, the computational cost of spike fitting should scale well with the number of units observed. Our algorithm accomplishes this goal, and is fast, because it exploits the spatial locality of each unit and the basic biophysics of extracellular signal propagation. Human intervention is minimized and streamlined via a graphical interface. We illustrate our method on data from a mammalian retina preparation and document its performance on simulated data consisting of spikes added to experimentally measured background noise. The algorithm is highly accurate

Higher dimensional dust collapse with a cosmological constant

The general solution of the Einstein equation for higher dimensional (HD) spherically symmetric collapse of inhomogeneous dust in presence of a cosmological term, i.e., exact interior solutions of the Einstein field equations is presented for the HD Tolman-Bondi metrics imbedded in a de Sitter background. The solution is then matched to exterior HD Scwarschild-de Sitter. A brief discussion on the causal structure singularities and horizons is provided. It turns out that the collapse proceed in the same way as in the Minkowski background, i.e., the strong curvature naked singularities form and that the higher dimensions seem to favor black holes rather than naked singularities.Comment: 7 Pages, no figure

Non-vacuum Solutions of Bianchi Type VI_0 Universe in f(R) Gravity

In this paper, we solve the field equations in metric f(R) gravity for Bianchi type VI_0 spacetime and discuss evolution of the expanding universe. We find two types of non-vacuum solutions by taking isotropic and anisotropic fluids as the source of matter and dark energy. The physical behavior of these solutions is analyzed and compared in the future evolution with the help of some physical and geometrical parameters. It is concluded that in the presence of isotropic fluid, the model has singularity at $\tilde{t}=0$ and represents continuously expanding shearing universe currently entering into phantom phase. In anisotropic fluid, the model has no initial singularity and exhibits the uniform accelerating expansion. However, the spacetime does not achieve isotropy as $t\rightarrow\infty$ in both of these solutions.Comment: 20 pages, 5 figures, accepted for publication in Astrophys. Space Sc

Non-invasive detection of ischemic vascular damage in a pig model of liver donation after circulatory death

Background and Aims: Liver graft quality is evaluated by visual inspection prior to transplantation, a process highly dependent on the surgeon's experience. We present an objective, noninvasive, quantitative way of assessing liver quality in real time using Raman spectroscopy, a laser-based tool for analyzing biomolecular composition. Approach and Results: A porcine model of donation after circulatory death (DCD) with normothermic regional perfusion (NRP) allowed assessment of liver quality premortem, during warm ischemia (WI) and post-NRP. Ten percent of circulating blood volume was removed in half of experiments to simulate blood recovery for DCD heart removal. Left median lobe biopsies were obtained before circulatory arrest, after 45 minutes of WI, and after 2 hours of NRP and analyzed using spontaneous Raman spectroscopy, stimulated Raman spectroscopy (SRS), and staining. Measurements were also taken in situ from the porcine liver using a handheld Raman spectrometer at these time points from left median and right lateral lobes. Raman microspectroscopy detected congestion during WI by measurement of the intrinsic Raman signal of hemoglobin in red blood cells (RBCs), eliminating the need for exogenous labels. Critically, this microvascular damage was not observed during WI when 10% of circulating blood was removed before cardiac arrest. Two hours of NRP effectively cleared RBCs from congested livers. Intact RBCs were visualized rapidly at high resolution using SRS. Optical properties of ischemic livers were significantly different from preischemic and post-NRP livers as measured using a handheld Raman spectrometer. Conclusions: Raman spectroscopy is an effective tool for detecting microvascular damage which could assist the decision to use marginal livers for transplantation. Reducing the volume of circulating blood before circulatory arrest in DCD may help reduce microvascular damage

Receptive Field Inference with Localized Priors

The linear receptive field describes a mapping from sensory stimuli to a one-dimensional variable governing a neuron's spike response. However, traditional receptive field estimators such as the spike-triggered average converge slowly and often require large amounts of data. Bayesian methods seek to overcome this problem by biasing estimates towards solutions that are more likely a priori, typically those with small, smooth, or sparse coefficients. Here we introduce a novel Bayesian receptive field estimator designed to incorporate locality, a powerful form of prior information about receptive field structure. The key to our approach is a hierarchical receptive field model that flexibly adapts to localized structure in both spacetime and spatiotemporal frequency, using an inference method known as empirical Bayes. We refer to our method as automatic locality determination (ALD), and show that it can accurately recover various types of smooth, sparse, and localized receptive fields. We apply ALD to neural data from retinal ganglion cells and V1 simple cells, and find it achieves error rates several times lower than standard estimators. Thus, estimates of comparable accuracy can be achieved with substantially less data. Finally, we introduce a computationally efficient Markov Chain Monte Carlo (MCMC) algorithm for fully Bayesian inference under the ALD prior, yielding accurate Bayesian confidence intervals for small or noisy datasets