Abstract

Many studies on animals, children and adults advocate for an inherent mechanism for number perception in humans and animal brains. There are some computational models for visual number perception. The on-centre off-surround Recurrent Neural Network (RNN) From Sengupta et al.(2014) accounts for some major behavioural findings regarding number perception. The RNN entails an abstract form of input and output. In our current work, we have devised a biologically interpretable method to use real images as the network's input and retrieve numerical estimation from the network's output (mean activation), resulting in an end-to-end model for enumeration of images. The model uses information from Saliency maps of images as input current for the neurons for a small duration (presentation time). The activation of the neurons changes based on the internal dynamics of the network, characterised by self …

Abstract

Can we build multi-view decoders that can decode concepts from brain recordings corresponding to any view (picture, sentence, word cloud) of stimuli? Can we build a system that can use brain recordings to automatically describe what a subject is watching using keywords or sentences? How about a system that can automatically extract important keywords from sentences that a subject is reading? Previous brain decoding efforts have focused only on single view analysis and hence cannot help us build such systems. As a first step toward building such systems, inspired by Natural Language Processing literature on multilingual and cross-lingual modeling, we propose two novel brain decoding setups: (1) multiview decoding (MVD) and (2) cross-view decoding (CVD). In MVD, the goal is to build an MV decoder that can take brain recordings for any view as input and predict the concept. In CVD, the goal is to train a model which takes brain recordings for one view as input and decodes a semantic vector representation of another view. Specifically, we study practically useful CVD tasks like image captioning, image tagging, keyword extraction, and sentence formation.

Abstract

The interference model supports a limited capacity of working memory (WM): if multiple to-be-remembered objects are presented in succession, features of later objects interfere with the recall of features of earlier objects. Feature recall of earlier objects can be strengthened with rehearsal, but this is possible for a limited amount of information. We investigated whether rehearsal can reduce interference in the recall of features of a single object. In the current study (N= 24), a circle was presented with randomly varying color, size, and location over 400 trials. Within a single trial, the stimulus appeared followed by two sequential response screens. We probed the recall of color and location of the circle, and considered two conditions (repeat, non-repeat) to investigate recall accuracy. In the repeat condition, participants recalled one of the features (either color or location) twice in a row. In the non-repeat condition, each …

Abstract

Modeling effective representations using multiple views that positively influence each other is challenging, and the existing methods perform poorly on Electroencephalogram (EEG) signals for sleepstaging tasks. In this paper, we propose a novel multi-view self-supervised method (mulEEG) for unsupervised EEG representation learning. Our method attempts to effectively utilize the complementary information available in multiple views to learn better representations. We introduce diverse loss that further encourages complementary information across multiple views. Our method with no access to labels, beats the supervised training while outperforming multi-view baseline methods on transfer learning experiments carried out on sleep-staging tasks. We posit that our method was able to learn better representations by using complementary multi-views.

Abstract

Classical Conditioning is a fundamental learning mechanism where the Ventral 14 Striatum is generally thought to be the source of inhibition to Ventral Tegmental Area (VTA) 15 Dopamine neurons when a reward is expected. However, recent evidences point to a new 16 candidate in VTA GABA encoding expectation for computing the reward prediction error in the VTA. 17 In this system-level computational model, the VTA GABA signal is hypothesised to be a combination 18 of magnitude and timing computed in the Peduncolopontine and Ventral Striatum respectively. 19 This dissociation enables the model to explain recent results wherein Ventral Striatum lesions 20 affected the temporal expectation of the reward but the magnitude of the reward was intact. This 21 model also exhibits other features in classical conditioning namely, progressively decreasing firing 22 for early rewards closer to the actual reward, twin peaks of VTA dopamine during training and 23 cancellation of US dopamine after training

Abstract

Preserving privacy of data in data mining domain has been a challenging objective to achieve. There has been lot of progress in applying secure algorithms in order to achieve privacy preserving of sensitive information. In distributed data mining perspective, there is necessity to share the information among the different parties aiming for combined outcomes. Privacy preserving methods and algorithms helps in preserving privacy of data when it is compulsory to share the information between the collaborated parties to perform various data mining operations like Classification and Clustering. It is always important to address the privacy issues of an individual when the data is shared among different parties to achieve a common goal. It is observed that there has been great development in addressing the privacy issues and designing privacy preserving algorithms applied to achieve a secured model. Adopting neural …

Abstract

n the rapidly growing computing world, the data is being generated every minute, and it has been a big challenge to manage and maintain privacy of the data when it is required to share among different locations. In this regard the privacy preserving data mining is playing a major role to design and develop the methods and models to preserve privacy of data in data owners perspectives, when ever the necessity of exchanging the information between multiple parties to be done. There has been lot of research going on in the field of computational intelligence, machine learning and soft computing domains and some good number of algorithms and secured models are being developed by the researchers. We have worked on the random forest classification as the best example model to preserve privacy of two parties when a combined classification to be done in order to build a privacy preserving global random …

Abstract

Sound source localization is crucial in acoustic sensing and monitoring-related applications. In this paper, we do a comprehensive analysis of improvement in sound source localization by combining the direction of arrivals (DOAs) with their derivatives which quantify the changes in the positions of sources over time. This study uses the SALSA-Lite feature with a convolutional recurrent neural network (CRNN) model for predicting DOAs and their first-order derivatives. An update rule is introduced to combine the predicted DOAs with the estimated derivatives to obtain the final DOAs. The experimental validation is done using TAU-NIGENS Spatial Sound Events (TNSSE) 2021 dataset. We compare the performance of the networks predicting DOAs with derivative vs. the one predicting only the DOAs at low SNR levels. The results show that combining the derivatives with the DOAs improves the localization accuracy of moving sources. Index Terms— Deep learning, Microphone array, SALSALite, Sound event localization and detection (SELD).

Abstract

Sparse Bayesian learning (SBL) has been successful in direction of arrival (DOA) estimation due to its robustness and high resolution using a few snapshots. Most wideband SBL algorithms make the simplifying assumption that distinct sources have the same power spectrum across frequency bands. However, this assumption may not be true in practice (for example speech signals). We analyze three wideband signal models and compare variants of wideband SBL (called SBL1, SBL2, and SBL3) with different assumptions on source signal power spectrum. The localization performance of SBL algorithms is compared with wideband processing of conventional beamforming (CBF) and multiple signal classification (MUSIC). The experimental validation is presented using simulated data and experimental LOCATA data. This comparative study shows that SBL3 which simultaneously enforces sparsity and models frequency-dependent signal spectrum shows superior performance in most scenarios. Index Terms—Compressive sensing, Sparse Bayesian learning, DOA estimation, MUSIC, CBF.

Abstract

State estimation in non-linear models is performed by tracking the posterior distribution recursively. A plethora of algorithms have been proposed for this task. Among them, the Gaussian particle filter uses a weighted set of particles to construct a Gaussian approximation to the posterior. In this paper, we propose to use invertible particle flow methods, derived under the Gaussian boundary conditions for a flow equation, to generate a proposal distribution close to the posterior. The resultant particle flow Gaussian particle filter (PFGPF) algorithm retains the asymptotic properties of Gaussian particle filters, with the potential for improved state estimation performance in highdimensional spaces. We compare the performance of PFGPF with the particle flow filters and particle flow particle filters in two challenging numerical simulation examples. Index Terms—Particle filters, particle flow filters, Gaussian particle filters, particle flow particle filters.