Abstract

fMRI semantic category understanding using linguistic encoding models attempt to learn a forward mapping that relates stimuli to the corresponding brain activation. Classical encoding models use linear multi-variate methods to predict the brain activation (all voxels) given the stimulus. However, these methods essentially assume multiple regions as one large uniform region or several independent regions, ignoring connections among them. In this paper, we present a mixture of expertsbased model where a group of experts captures brain activity patterns related to particular regions of interest (ROI) and also show the discrimination across different experts. The model is trained word stimuli encoded as 25-dimensional feature vectors as input and the corresponding brain responses as output. Given a new word (25-dimensional feature vector), it predicts the entire brain activation as the linear combination of multiple experts’ brain activations. We argue that each expert learns a certain region of brain activations corresponding to its category of words, which solves the problem of identifying the regions with a simple encoding model. We showcase that proposed mixture of experts-based model indeed learns region-based experts to predict the brain activations with high spatial accuracy.

Abstract

What makes a computational neuronal model ’large scale’ ? Is it the number of neurons modeled? Or the number of brain regions modeled in a network? Most of the higher cognitive processes span across co-ordinated activity in a network of different brain areas. However at the same time, the basic information transfer takes place at a single neuron level, together with multiple other neurons. We explore modeling a neural system involving some areas of cortex, the basal ganglia (BG) and thalamus for the process of decision making, using a largescale neural engineering framework, Nengo. Early results tend to replicate the known neural activity patterns as found in the previous action selection model by Guthrie et al. 2013, besides operating with a larger neuronal populations. The power of converting algorithms to efficiently weighed neural networks in Nengo (Stewart et al. 2009 and Bekolay et al. 2013) is exploited in this work. Crucial aspects in a computational model, like parameter tuning and detailed neural implementations, while moving from a simplistic to large-scale model, are studied.

Abstract

We assess change in metastability to characterize age-effects on the dynamic repertoire of the functional networks at rest. Resting state fMRI signals from each subject (N=48) have been used and metastability is evaluated as the standard deviation of mean phase synchrony of BOLD signals across whole-brain as well as across known resting state networks. The results suggest that significant whole-brain metastability changes occur between middle to old age. We also demonstrate that static time-averaged FC largely undermines age-effects on the interaction between functional networks. Discriminant Function Analysis reveals existence of two different patterns of change in metastability, which maximally discriminates between two different processes of maturation and ageing.

Abstract

The existence of perceptually distinct numerosity ranges has been proposed for small (i.e., subitizing range) and larger numbers based on differences in precision, Weber fractions, and reaction times. This raises the question of whether such dissociations reflect distinct mechanisms operating across the two numerosity ranges. In the present work, we explore the predictions of a single-layer recurrent on-center, off-surround network model of attentional priority that has been applied to object individuation and enumeration. Activity from the network can be used to model various phenomena in the domain of visual number perception based on a single parameter: the strength of inhibition between nodes. Specifically, higher inhibition allows for precise representation of small numerosities, while low inhibition is preferred for high numerosities. The model makes novel predictions, including that enumeration of …

Abstract

Perception of time is an active process that takes place continually. However, we are yet to learn its exact mechanisms conclusively. The temporal bisection task is ideal to investigate the circuitry underlying time perception. Caffeine, a commonly used stimulant, has been known to play a role in modulation of time perception. The objective of this article is to explore the role of caffeine, a neuromodulator, in the perception of time in human beings by conducting suitable experiments. The experiment shows that an expansion of time is perceived by subjects after caffeine ingestion and that caffeine has an accelerating effect on our time perception system. Additionally, we present a preliminary 2-step decision model that fits the results of the experiment and potentially gives insights into the mechanisms of caffeine. We conclude by pointing out future directions towards a more biologically realistic computational model.

Abstract

Bilingualism has been found to delay onset of dementia and this has been attributed to an advantage in executive control in bilinguals. However, the relationship between bilingualism and cognition is complex, with costs as well as benefits to language functions. To further explore the cognitive consequences of bilingualism, the study used Frontotemporal dementia (FTD) syndromes, to examine whether bilingualism modifies the age at onset of behavioral and language variants of Frontotemporal dementia (FTD) differently. Case records of 193 patients presenting with FTD (121 of them bilingual) were examined and the age at onset of the first symptoms were compared between monolinguals and bilinguals. A significant effect of bilingualism delaying the age at onset of dementia was found in behavioral variant FTD (5.7 years) but not in progressive nonfluent aphasia (0.7 years), semantic dementia (0.5 years …

Abstract

What makes a computational neuronal model `large scale'? Is it the number of neurons modeled? Or the number of brain regions modeled in a network? Most of the higher cognitive processes span across co-ordinated activity in a network of different brain areas. However at the same time, the basic information transfer takes place at a single neuron level, together with multiple other neurons. We explore modeling a neural system involving some areas of cortex, the basal ganglia (BG) and thalamus for the process of decision making, using a large-scale neural engineering framework, Nengo. Early results tend to replicate the known neural activity patterns as found in the previous action selection model by Guthrie et al. 2013, besides operating with a larger neuronal populations. The power of converting algorithms to efficiently weighed neural networks in Nengo (Stewart et al. 2009 and Bekolay et al. 2013) is …

Abstract

The neurocomputational model described here proposes that two dimensions involved in computation of reward prediction errors i.e magnitude and time could be computed separately and later combined unlike traditional reinforcement learning models. The model is built on biological evidences and is able to reproduce various aspects of classical conditioning, namely, the progressive cancellation of the predicted reward, the predictive firing from conditioned stimuli, and delineation of early rewards by showing firing for sooner early rewards and not for early rewards that occur with a longer latency in accordance with biological data.

Abstract

utomatic photo aesthetic assessment is a challenging artificial intelligence task. Existing computational approaches have focused on modeling a single aesthetic score or a class (good or bad), however these do not provide any details on why the photograph is good or bad, or which attributes contribute to the quality of the photograph. To obtain both accuracy and human interpretation of the score, we advocate learning the aesthetic attributes along with the prediction of the overall score. For this purpose, we propose a novel multitask deep convolution neural network, which jointly learns eight aesthetic attributes along with the overall aesthetic score. We report near human performance in the prediction of the overall aesthetic score. To understand the internal representation of these attributes in the learned model, we also develop the visualization technique using back propagation of gradients. These visualizations highlight the important image regions for the corresponding attributes, thus providing insights about model's representation of these attributes. We showcase the diversity and complexity associated with different attributes through a qualitative analysis of the activation maps.

Abstract

Papillary Renal Cell Carcinoma (PRCC) is a heterogeneous disease accounting for 10%-15% of renal cell carcinomas. A comprehensive analysis is required to find the genes that are responsible for the stage progression in PRCC. The advent of next generation sequencing techniques (NGS) has produced a lot of high throughput data from patients that can be analyzed to address this problem. The low sample size, noise and high dimensionality of the data though enhances the complexity, requiring the use of sophisticated methods. In our study we propose a machine learning pipeline fulfilling a two-fold objective: 1) To find suitable genes that could serve as potential biomarkers for stage progression in PRCC. 2) To build a classifier using the above biomarkers that can predict the stage of a given patient. The RNA-Seq data of PRCC was taken and divided into training set (80%) and test set (20%). Different …