IUNI has over 165 faculty affiliates from across IU. You may browse through listings below – clicking on a name will expand to show you full listings. You may also search through keywords and biographies in the search bar below.
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|Izquierdo, Eduardo J.||Cognitive Science Program, School of Informatics and Computing / IUB|
Bio: My research interest is in understanding the neural basis of behavior, as it arises from the interaction between the organism’s nervous system, its body, and its environment. We combine connectome graph analysis, neural network simulations, evolutionary algorithms for optimization, taking into account experimental observations, and mathematical analysis, including information theory and dynamical systems theory, to generate and understand complete brain-body-environment models of simple but biologically and cognitively interesting behaviors.
|José, Jorge V.||Physics, Stark Neurosciences Institute, College of Arts and Sciences, School of Medicine / Bloomington|
Bio: The research in my lab goes from computational neuroscience studies of neurons and neuronal networks modeling animal behaviors to studies in humans affected by neurological disorders, including translational research applications. All research done in my lab is guided by a general principle of connecting neuronal dynamics to behavior. Autism Spectrum Disorder (ASD) is characterized by the lack of communicative and cognitive abilities. The current clinical diagnostic models have focused almost exclusively on the deficits providing qualitative behavioral treatments to improve the individual’s condition. In collaboration with Rutgers University and members of the Indiana University School of Medicine, we have been thinking about autism in a very different way. Recent technical advances in wearable sensing technology have helped us bridge the gap between observational clinical practices and quantitative objective research outcomes. The instruments we used in our laboratory settings allow motion tracking kinematics for different parts of the body, including the eyes’ minute motions, facial micro-expressions and body micro-movements. To analyze the “big data sets” produced by these recordings we developed new statistical analytics. Our analyses provide novel physiologically biometrics which may be used to characterizing sensory-motor signatures many which occur largely beneath detection of our naked eye capabilities. Our recent results offer new avenues for connecting the cognitive abilities of individuals by quantitatively studying their moment-by-moment natural micro-movements at a millisecond time scales. Synchronization of inhibitory neurons as a possible mechanism for attentional gain modulation. Naturally occurring visual scenes contain large amounts of spatial and temporal information that are transduced into neuronal spike trains along the visual sensory pathway. Human psychophysics indicates that only a small part of that information is attended. We have developed Hodgkin-Huxley neuronal models to analyze data obtained from electrophysiological experiments with nonhuman primates. We have suggested that attentional modulation of the synchrony of local interneuronal networks could potentially account for these observations. We also considered the case when two stimuli are presented simultaneously. The neuronal response is in between those for each stimulus presented separately (stimulus competition) and when one stimulus is attended. The neuronal response gets closer to the response to this stimulus presented alone (biased competition). When the stimulus contrast is varied, several types of gain responses have been found with attention. We introduced a biophysical neural network model of V4, constraining it to reproduce the dynamics observed in the absence of attention. We were able to reproduce some of the detailed neural activity reported experimentally and the stimulus competition. We are exploring the possibility that our model may provide a unified framework for attentional modulation in V4. From neuronal to an hydrodynamic model describing larvae zebra fish rich swimming repertoire. Larval zebrafish (LZF) provide a unique opportunity to study realistic neuronal models since the fish is transparent and most of its neuronal properties are measured. The LZF exhibits a variety of complex undulatory swimming patterns. This repertoire is controlled by the 300 neurons projecting from brain into spinal cord. We developed a segmental oscillator model (using the NEURON program) to investigate this system. By adjusting NMDA strengths and glycinergic synapses produced the generation of oscillation (tail-beat) frequency patterns over the range exhibited experimentally. To describe visually the experimentally observed bending patterns we also developed a biomechanical-hydrodynamic model to better understand how those outputs are generated by the neuronal model we developed.