Exploiting Noise as a Resource for Computation and Learning in Spiking Neural Networks

Networks of spiking neurons underpin the extraordinary information-processing capabilities of the brain and have emerged as pillar models in neuromorphic intelligence. Despite extensive research on spiking neural networks (SNNs), most are established on deterministic models. Integrating noise into SNNs leads to biophysically more realistic neural dynamics and may benefit model performance. This work presents the noisy spiking neural network (NSNN) and the noise-driven learning rule (NDL) by introducing a spiking neuron model incorporating noisy neuronal dynamics. Our approach shows how noise may act as a resource for computation and learning and theoretically provides a framework for general SNNs. Moreover, NDL provides an insightful biological rationale for surrogate gradients. By incorporating various SNN architectures and algorithms, we show that our approach exhibits competitive performance and improved robustness against challenging perturbations than deterministic SNNs. Additionally, we demonstrate the utility of the NSNN model for neural coding studies. Overall, NSNN offers a powerful, flexible, and easy-to-use tool for machine learning practitioners and computational neuroscience researchers.Comment: Fixed the bug in the BBL file generated with bibliography management progra

Temporal Conditioning Spiking Latent Variable Models of the Neural Response to Natural Visual Scenes

Developing computational models of neural response is crucial for understanding sensory processing and neural computations. Current state-of-the-art neural network methods use temporal filters to handle temporal dependencies, resulting in an unrealistic and inflexible processing flow. Meanwhile, these methods target trial-averaged firing rates and fail to capture important features in spike trains. This work presents the temporal conditioning spiking latent variable models (TeCoS-LVM) to simulate the neural response to natural visual stimuli. We use spiking neurons to produce spike outputs that directly match the recorded trains. This approach helps to avoid losing information embedded in the original spike trains. We exclude the temporal dimension from the model parameter space and introduce a temporal conditioning operation to allow the model to adaptively explore and exploit temporal dependencies in stimuli sequences in a natural paradigm. We show that TeCoS-LVM models can produce more realistic spike activities and accurately fit spike statistics than powerful alternatives. Additionally, learned TeCoS-LVM models can generalize well to longer time scales. Overall, while remaining computationally tractable, our model effectively captures key features of neural coding systems. It thus provides a useful tool for building accurate predictive computational accounts for various sensory perception circuits.Comment: spiking neural networks, neural coding, visual coding, latent variable models, variational information bottleneck, noisy spiking neural network

Geographic differential privacy for mobile crowd coverage maximization

For real-world mobile applications such as location-based advertising and spatial crowdsourcing, a key to success is targeting mobile users that can maximally cover certain locations in a future period. To find an optimal group of users, existing methods often require information about users' mobility history, which may cause privacy breaches. In this paper, we propose a method to maximize mobile crowd's future location coverage under a guaranteed location privacy protection scheme. In our approach, users only need to upload one of their frequently visited locations, and more importantly, the uploaded location is obfuscated using a geographic differential privacy policy. We propose both analytic and practical solutions to this problem. Experiments on real user mobility datasets show that our method significantly outperforms the state-of-the-art geographic differential privacy methods by achieving a higher coverage under the same level of privacy protection

MR Diffusion Tensor Imaging Detects Rapid Microstructural Changes in Amygdala and Hippocampus Following Fear Conditioning in Mice

Background: Following fear conditioning (FC), ex vivo evidence suggests that early dynamics of cellular and molecular plasticity in amygdala and hippocampal circuits mediate responses to fear. Such altered dynamics in fear circuits are thought to be etiologically related to anxiety disorders including posttraumatic stress disorder (PTSD). Consistent with this, neuroimaging studies of individuals with established PTSD in the months after trauma have revealed changes in brain regions responsible for processing fear. However, whether early changes in fear circuits can be captured in vivo is not known. Methods: We hypothesized that in vivo magnetic resonance diffusion tensor imaging (DTI) would be sensitive to rapid microstructural changes elicited by FC in an experimental mouse PTSD model. We employed a repeated measures paired design to compare in vivo DTI measurements before, one hour after, and one day after FC-exposed mice (n = 18). Results: Using voxel-wise repeated measures analysis, fractional anisotropy (FA) significantly increased then decreased in amygdala, decreased then increased in hippocampus, and was increasing in cingulum and adjacent gray matter one hour and one day post-FC respectively. These findings demonstrate that DTI is sensitive to early changes in brain microstructure following FC, and that FC elicits distinct, rapid in vivo responses in amygdala and hippocampus. Conclusions: Our results indicate that DTI can detect rapid microstructural changes in brain regions known to mediate fear conditioning in vivo. DTI indices could be explored as a translational tool to capture potential early biological changes in individuals at risk for developing PTSD. © 2013 Ding et al.published_or_final_versio

Bioimaging of Dissolvable Microneedle Arrays: Challenges and Opportunities

The emergence of microneedle arrays (MNAs) as a novel, simple, and minimally invasive administration approach largely addresses the challenges of traditional drug delivery. In particular, the dissolvable MNAs act as a promising, multifarious, and well-controlled platform for micro-nanotransport in medical research and cosmetic formulation applications. The effective delivery mostly depends on the behavior of the MNAs penetrated into the body, and accurate assessment is urgently needed. Advanced imaging technologies offer high sensitivity and resolution visualization of cross-scale, multidimensional, and multiparameter information, which can be used as an important aid for the evaluation and development of new MNAs. The combination of MNA technology and imaging can generate considerable new knowledge in a cost-effective manner with regards to the pharmacokinetics and bioavailability of active substances for the treatment of various diseases. In addition, noninvasive imaging techniques allow rapid, receptive assessment of transdermal penetration and drug deposition in various tissues, which could greatly facilitate the translation of experimental MNAs into clinical application. Relying on the recent promising development of bioimaging, this review is aimed at summarizing the current status, challenges, and future perspective on in vivo assessment of MNA drug delivery by various imaging technologies

Long-term outcomes of endovascular treatment of acute lower limb deep-vein thrombosis combined with May–Thurner syndrome: A single-center retrospective cohort study

OBJECTIVE: May–Thurner syndrome (MTS) is a common cause of lower-extremity deep venous thrombosis (DVT). Stenting is effective in the treatment of MTS; however, there are still complications that may affect the patency of the stent. In addition, the long-term efficacy of stenting remains uncertain. This retrospective study investigated the efficacy and prognosis of endovascular treatment of acute proximal DVT with MTS. MATERIALS AND METHODS: Between June 2014 and December 2017, 122 patients with acute symptomatic proximal DVT at our hospital underwent endovascular surgery. The clinical data of the patients were retrospectively collected including follow-up information. The analysis used the generalized additive mixed model and Kaplan–Meier curves (log-rank test). RESULTS: Of the 122 patients, 75 underwent only catheter-directed thrombolysis (CDT), and 47 underwent CDT with stent placement. Characteristics such as age (P = 0.630 ) and gender (P = 0.842) did not show significant differences between the two groups. The freedom from target lesion revascularization did not show significant differences between the two groups (P = 0.82). There were no significant differences between the two groups in the Venous Clinical Severity Score and Villalta Score. CONCLUSIONS: Endovascular treatment of acute lower limb DVT with MTS has good overall efficacy. In young patients with DVT caused by multiple factors besides MTS, prolonged anticoagulation and close follow-up may be more appropriate than primary stenting after thrombus clearance and significant relief of lower limb symptoms

ROI illustration.

<p>ROIs obtained from the significant clusters pointed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051704#pone-0051704-g002" target="_blank">Figure 2</a>. ROIs were overlaid on an FA map averaged from all animals. Cingulum and adjacent gray matter (red), amygdala (blue) and hippocampus (green) are shown from anterior to posterior (left to right, up to down).</p

Freezing behavior analysis.

<p>Percentage of freezing behavior during pre-shock period (free exploration), FC period (three paired CS with US), and cue/contextual test performed one month post-FC. One-way ANOVA with Dunnett’s multiple comparison test was employed (***p<0.001). Error bars represent the standard error of the mean (n = 8).</p

Statistical maps from voxel-wise planned comparisons between time points for FA.

<p>Upper row: statistical maps (colored regions) from planned comparisons are overlaid on a mean fractional anisotropy (FA) map (shown in corresponding axial, coronal and sagital views respectively) averaged from all animals. In the clusters indicated by arrows, FA first increased then decreased in amygdala (Amg, p<0.005, 31 voxels, n = 18), first decreased then increased in hippocampus (HP, p<0.005, 33 voxels, n = 9), and kept increasing in cingulum (CG, p<0.005, 49 voxels, n = 18) 1-hr, 1-day post-FC respectively. Colors are coded according to the threshold they exceeded. Lower row: corresponding FA values were presented by normalization of pre-FC at all time points within the significant voxels. Error bars represent mean ± standard error of the mean (n = 18 for Amg and CG, n = 9 for HP).</p

Comparisons of DTI quantitation measurements between current study and previous studies.

<p>Data for hippocampus and amygdala in current study are the pre-FC measurements shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051704#pone-0051704-g004" target="_blank">Fig. 4</a>, and data for cortex, caudate putamen and thalamus were measured from manually defined ROIs (n = 18).</p