Abstract

How does the brain represent different modes of information? Can we design a system that automatically understands what the user is thinking? Such questions can be answered by studying brain recordings like functional magnetic resonance imaging (fMRI). As a first step, the neuroscience community has contributed several large cognitive neuroscience datasets related to passive reading/listening/viewing of concept words, narratives, pictures and movies. Encoding and decoding models using these datasets have also been proposed in the past two decades. These models serve as additional tools for basic research in cognitive science and neuroscience. Encoding models aim at generating fMRI brain representations given a stimulus automatically. They have several practical applications in evaluating and diagnosing neurological conditions and thus also help design therapies for brain damage. Decoding models solve the inverse problem of reconstructing the stimuli given the fMRI. They are useful for designing brain-machine or brain-computer interfaces. Inspired by the effectiveness of deep learning models for natural language processing, computer vision, and speech, recently several neural encoding and decoding models have been proposed. In this survey, we will first discuss popular representations of language, vision and speech stimuli, and present a summary of neuroscience datasets. Further, we will review popular deep learning based encoding and decoding architectures and note their benefits and limitations. Finally, we will conclude with a brief summary and discussion about future trends

Abstract

Self-supervised speech based models have been found to be successful in predicting brain recordings of subjects experiencing naturalistic story listening. Inspired by the recent progress on deep learning models for various speech-processing tasks, existing literature has leveraged pretrained speech Transformer models for brain encoding. However, there is no work on exploring the efficacy of task-specific finetuned Transformer representations for this task. Hence, in this paper, we explore transfer learning from representations finetuned for eight different tasks from Speech processing Universal PERformance Benchmark (SUPERB) for predicting brain responses. Encoding models based on task features are used to predict activity in different regions across the whole brain, and also in language and auditory brain regions. Our experiments on finetuning the Wav2Vec2.0 model for these eight tasks show that the model finetuned on automatic speech recognition (ASR) yields the best encoding performance for the whole brain, language and auditory regions.

Abstract

Perception of interval timing influences the behaviour of the organisms. Computational models of interval timing are categorized into Pacemaker Accumulator models, Memory-based models, Oscillator models and Random Process models, Ramping Activity models and Population Clock models. Random process models or drift diffusion models are biologically plausible models and are based on the activity of spiking neurons. In this paper, we proposed a computational model of interval timing based on spiking neurons. The results are validated against the Scalar property of interval timing

Abstract

Measuring subjective experience of time helps the designer of the product to understand the usability of the product and the user experience. Time is not given it’s due importance in HCI and particularly in the usability design of the products. Though it is evident that cognitive load impacts time/duration estimation, thereby the user creates a positive or negative valence towards the product, there is no much research done to measure the usability of the products with cognitive load and duration judgement as dependent variables. This paper focuses on empirical investigation of prospective duration judgement as a measure to assess the cognitive load experienced by the users while using the product, one-on-one mentoring mobile app. We consider Jakob Nielsen’s heuristics and meCUE questionnaire as the anchors for our empirical investigation. Index Terms—Human-Computer Interaction, Duration Judge- ment, Usability, User Experience,

Abstract

Cognitive brain mapping is enjoying its growth with the availability of large open data sharing efforts as well as the application of modern machine learning and deep learning methods. In this article, we review some of the current practices in cognitive neuroscience predominantly focusing on functional imaging and highlight the tremendous opportunities fostered by the unprecedented scale of datasets in cognitive neuroscience. We also point out challenges and limitations to keep in mind while working with these datasets.

Abstract

Conventional Reinforcement Learning (RL) algorithms assume the distribution of the data to be uniform or mostly uniform. However, this is not the case with most real-world applications like autonomous driving or in nature, where animals roam. Some objects are encountered frequently, and most of the remaining experiences occur rarely; the resulting distribution is called Zipfian. Taking inspiration from the theory of complementary learning systems, an architecture for learning from Zipfian distributions is proposed where long tail states are discovered in an unsupervised manner and states along with their recurrent activation are kept longer in episodic memory. The recurrent activations are then reinstated from episodic memory using a similarity search, giving weighted importance. The proposed architecture yields improved performance in a Zipfian task over conventional architectures. Our method outperforms IMPALA by a significant margin of 20.3% when maps/objects occur with a uniform distribution and by 50.2% on the rarest 20% of the distribution.

Abstract

In this smart and rapidly growing computing world, most of the organizations and companies needs to share necessary information (data) to third parties for their analysis which plays major role in decision making. Any data generally consists of sensitive (personal) information about people and organizations and when the sensitive information to be revealed to third parties, there is always chance of privacy loss. In this regard any organization or an individual requires the application of privacy preserving techniques to ensure data privacy, that means the data to be exchanged between two or more sites, without data loss or privacy violation. When data to

Abstract

How does the brain represent different modes of information? Can we design a system that can automatically understand what the user is thinking? We can make progress towards answering such questions by studying brain recordings from devices such as functional magnetic resonance imaging (fMRI). The brain encoding problem aims to automatically generate fMRI brain representations given a stimulus. The brain decoding problem is the inverse problem of reconstructing the stimuli given the fMRI brain representation. Both the brain encoding and decoding problems have been studied in detail in the past two decades and the foremost attraction of studying these solutions is that they serve as additional tools for basic research in cognitive science and cognitive neuroscience. Recently, inspired by the effectiveness of deep learning models for natural language processing and computer vision, such models have been applied for neuroscience as well. In this tutorial, we plan to discuss different kinds of stimulus representations, and popular encoding and decoding architectures in detail. The tutorial will provide a working knowledge of the state of the art methods for encoding and decoding, a thorough understanding of the literature, and a better understanding of the benefits and limitations of encoding/decoding with deep learning. Encoding models that accurately predict brain activity have several practical applications in evaluation and diagnosis of neurological conditions and thus also help designing therapies for brain damage. Invertible encoding models enable principled formulation of brain decoding models which in turn are useful for designing brain-machine or brain-computer interfaces. Recent advances in the use of pretrained deep network models enable us to use them as priors for brain decoding tasks. Deep learning models are useful for improving accuracy but also offer the flexibility of decoding across a gamut of tasks and domains.

Abstract

In this smart and rapidly growing computing world, most of the organizations and companies needs to share necessary information (data) to third parties for their analysis which plays major role in decision making. Any data generally consists of sensitive (personal) information about people and organizations and when the sensitive information to be revealed to third parties, there is always chance of privacy loss. In this regard any organization or an individual requires the application of privacy preserving techniques to ensure data privacy, that means the data to be exchanged between two or more sites, without data loss or privacy violation. When data to be shared between multiple organizations or sites (parties) the privacy issues will be arises more. In this line we choose collaborative fuzzy clustering that gives better solutions to preserve privacy of data while sharing information between multiple parties to achieve …

Abstract

Neuroimaging techniques such as fMRI record brain activation while participants experience a stimulus. The concreteness of concepts defines how well our brain is able to imagine them. We hypothesise that brain activation would be distinctly different when participants view stimuli corresponding to concrete words versus abstract words. Specifically, we expect primary sensory areas to engage more in the former. To study this, we used a multi-view dataset with each concept being displayed as a picture, as a word in a sentence and also as a wordcloud [1]. We investigate how well a machine learning-based decoder trained on concrete concepts can decode both concrete and abstract concepts and vice versa for the model trained on abstract concepts. We find that a model trained on stimuli related to concrete concepts yields better accuracy than the model trained on abstract concepts. We also explore the contribution of voxels from different brain regions in this decoding process. Analysis of the contribution of different brain networks reveals exciting cognitive insights.