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Bapi, R. S.

Publications and source records attributed to Bapi, R. S..

3 recordsLinked to original sources

Recalling a single object: going beyond the capacity debate

Working memory is now established as a limited capacity system. The debate regarding working memory has been largely between slots and resource based models. The resource model suggests that as the number of items increases, precision of recall decreases because neural resources are dynamically allocate to all the objects needed for task. Slot model on the other hand implies that an item is stored either with the highest precision or not at all. If both these models stand true then quality of memory performance would be near perfect for a single object. However, that may not be the case. In the current work, we investigated recall accuracy for feature(s) of a single object in three successive experiments. In all three experiments, the memory array consisted of a single colored oriented short line presented a short distance away from the center of the display for 1 sec. We probed recall of features from the set of color, location, orientation, and size. In experiment 1 number of recall question varied between 1 - 4 with the order randomized in each trial. In experiment 2 we chose to probe only two feature recall questions, whereas only one recall question was probed for the third experiment. In experiment 3 we varied the delay before the recall probe between 1 and 2 s. The recall response for each feature was mapped on to a continuous variable. Subjects used a color wheel to respond to color, on-screen mouse click to indicate center of the line location, click away from the center to indicate size, and a mouse click to the periphery of centered circle to indicate orientation with the slant of the resulting radial line. We calculated z-scores of errors for each feature for every subject separately. In experiment 1 that for color, location, and size the errors increase significantly with the position of the questions asked. In experiment 2, the errors increased significantly between questions for color and location (but not for orientation and size). In experiment 3, we did not see any significant increase in error with recall probe delay. Overall run-time for each trial was within 10 secs, well within the limits of operation of working memory. This drop in performance poses questions for memory mechanisms proposed by slot and resource models as both would predict near-perfect recall within the time-period for the trials.

animal behavior and cognition↗

Integration and segregation in Autism Spectrum Disorders modulated by age, disease, and interaction: A graph-theoretic study of intrinsic functional connectivity

Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting 1 in 50 children between the ages of 6 and 17 years. Brain connectivity and graph theoretic methods have been particularly very useful in shedding light on the differences between high functioning autistic children compared to typically developing (TD) ones. However, very recent developments in network measures raise a cautionary note by highlighting gross under- and over-connectivity in ASD may be an oversimplified hypothesis. Thus the primary aim of our study is to investigate these notions in functional connectomics of ASD versus TD by subjecting the data to reproducibility experiments using two independent datasets.\n\nFurther, we tested the hypothesis of alteration in network segregation and integration in the ASD subjects. We have analyzed the resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) data from the University of California Los Angeles (UCLA) multimodal connectivity database (n=42 ASD, n=37 TD) and rs-fMRI data from the Autism Brain Imaging Data Exchange (ABIDE) (n=187 ASD, n=176 TD) dataset. We assessed the differences in connection strength between TD and ASD subjects. We also performed graph theoretical analysis to analyze the effect of disease on various network measures. Further, using the larger ABIDE dataset, we performed two-factor ANOVA test, to study the effect of age, disease and their interaction by classifying the TD and ASD participants into two cohorts: children (9-12 years, n=73 TD and n=87 ASD) and adolescents (13-16 years, n=103 TD and n=100 ASD). In ASD, we show the existence of atypical connectivity within and between functional networks as compared to TD. We also found in ASD both hypo-and hyper-connectivity within functional networks such as the default mode network (DMN). Further, graph theoretic analysis showed that there is significant effect of age and disease on modularity, clustering coefficient, and local efficiency. We also identified specific areas within the DMN, sensorimotor, visual and attention networks that are affected by age, disease and their interaction. Overall, our findings suggest that maturation, disease and their interaction are critical for unraveling the biological basis and developmental trajectory in ASD and other neuropsychiatric disorders.

neuroscience↗

From Neural Network To Psychophysics Of Time: Exploring Emergent Properties Of RNNs Using Novel Hamiltonian Formalism

The stability analysis of dynamical neural network systems generally follows the route of finding a suitable Liapunov function after the fashion Hopfields famous paper on content addressable memory network or by finding conditions that make divergent solutions impossible. For the current work we focused on biological recurrent neural networks (bRNNs) that require transient external inputs (Cohen-Grossberg networks). In the current work we have proposed a general method to construct Liapunov functions for recurrent neural network with the help of a physically meaningful Hamiltonian function. This construct allows us to explore the emergent properties of the recurrent network (e.g., parameter configuration needed for winner-take-all competition in a leaky accumulator design) beyond that available in standard stability analysis, while also comparing well with standard stability analysis (ordinary differential equation approach) as a special case of the general stability constraint derived from the Hamiltonian formulation. We also show that the Cohen-Grossberg Liapunov function can be derived naturally from the Hamiltonian formalism. A strength of the construct comes from its usability as a predictor for behavior in psychophysical experiments involving numerosity and temporal duration judgements.

neuroscience↗