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Open Access Publications from the University of California

Scholarly Works (86 results)

  • Article
  • Peer Reviewed
UC San Diego Previously Published Works (2015)
No one did more to draw neuroscientists' attention to the problem of consciousness in the twentieth century than Francis Crick, who may be better known as the co-discoverer (with James Watson) of the structure of DNA. Crick focused his research on visual awareness and based his analysis on the progress made over the last fifty years in uncovering the neural mechanisms underlying visual perception. Because much of what happens in our brains occurs below the level of consciousness and many of our intuitions about unconscious processing are misleading, consciousness remains an elusive problem. In the end, when all of the brain mechanisms that underlie consciousness have been identified, will we still be asking: "What is consciousness?" Or will the question shift, just as the question "What is life?" is no longer the same as it was before Francis Crick?
    Cover page: Consciousness
    • Article
    • Peer Reviewed
    UC San Diego Previously Published Works (2014)
    The goal of the 79th Cold Spring Harbor Symposia on Quantitative Biology on Cognition held on May 28-June 2, 2014 was to survey recent advances in cognitive neuroscience and assess future prospects. The symposium succeeded beyond the dreams of the organizers and the participants were treated to an extraordinarily rich feast of 58 long talks, six short talks, and 137 posters. Equally important to the success of the symposium was the perfect setting for informal scientific exchange between 260 participants generously provided by the Cold Spring Harbor Laboratory. The sense that emerged from the symposium was that a threshold had been crossed and a new era in the study of cognition was underway. My attempt here will be to capture that sense of awakening, to trace the strands that gave rise to it, and to access its implications for future discoveries.
      Cover page: Summary: Cognition in 2014
      • Article
      • Peer Reviewed
      Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 6 (1984)
        Cover page: Cortical Computation
        • Article
        • Peer Reviewed
        UC San Diego Previously Published Works (2016)
        To understand the origins of spatial navigational signals, Acharya et al. record the activity of hippocampal neurons in rats running in open two-dimensional environments in both the real world and in virtual reality. They find that a subset of hippocampal neurons have directional tuning that persists in virtual reality, where vestibular cues are absent.
          Cover page: Street View of the Cognitive Map
          • Article
          • Peer Reviewed
          UC San Diego Previously Published Works (2016)
          Natural images are scale invariant with structures at all length scales.We formulated a geometric view of scale invariance in natural images using percolation theory, which describes the behavior of connected clusters on graphs.We map images to the percolation model by defining clusters on a binary representation for images. We show that critical percolating structures emerge in natural images and study their scaling properties by identifying fractal dimensions and exponents for the scale-invariant distributions of clusters. This formulation leads to a method for identifying clusters in images from underlying structures as a starting point for image segmentation.
            Cover page: Correlated Percolation, Fractal Structures, and Scale-Invariant Distribution of Clusters in Natural Images
            • Article
            • Peer Reviewed
            UC San Diego Previously Published Works (2015)
            Inhibition-stabilized networks (ISNs) are neural architectures with strong positive feedback among pyramidal neurons balanced by strong negative feedback from inhibitory interneurons, a circuit element found in the hippocampus and the primary visual cortex. In their working regime, ISNs produce damped oscillations in the [Formula: see text]-range in response to inputs to the inhibitory population. In order to understand the properties of interconnected ISNs, we investigated periodic forcing of ISNs. We show that ISNs can be excited over a range of frequencies and derive properties of the resonance peaks. In particular, we studied the phase-locked solutions, the torus solutions, and the resonance peaks. Periodically forced ISNs respond with (possibly multistable) phase-locked activity, whereas networks with sustained intrinsic oscillations respond more dynamically to periodic inputs with tori. Hence, the dynamics are surprisingly rich, and phase effects alone do not adequately describe the network response. This strengthens the importance of phase-amplitude coupling as opposed to phase-phase coupling in providing multiple frequencies for multiplexing and routing information.
              Cover page: Periodic Forcing of Inhibition-Stabilized Networks: Nonlinear Resonances and Phase-Amplitude Coupling
              • Article
              • Peer Reviewed
              UC San Diego Previously Published Works (2015)
              In a planar free-hand drawing of an ellipse, the speed of movement is proportional to the -1/3 power of the local curvature, which is widely thought to hold for general curved shapes. We investigated this phenomenon for general curved hand movements by analyzing an optimal control model that maximizes a smoothness cost and exhibits the -1/3 power for ellipses. For the analysis, we introduced a new representation for curved movements based on a moving reference frame and a dimensionless angle coordinate that revealed scale-invariant features of curved movements. The analysis confirmed the power law for drawing ellipses but also predicted a spectrum of power laws with exponents ranging between 0 and -2/3 for simple movements that can be characterized by a single angular frequency. Moreover, it predicted mixtures of power laws for more complex, multifrequency movements that were confirmed with human drawing experiments. The speed profiles of arbitrary doodling movements that exhibit broadband curvature profiles were accurately predicted as well. These findings have implications for motor planning and predict that movements only depend on one radian of angle coordinate in the past and only need to be planned one radian ahead.
                Cover page: Spectrum of power laws for curved hand movements
                • Thesis
                • Peer Reviewed
                UC San Diego Electronic Theses and Dissertations (2023)

                Brain Connectomics is generating an ever-increasing deluge of data, which challenges us to develop new methods for analyzing and extracting new insights from these data. Connectomic researchers have focused on connectivity -- the pattern of connectivity between neurons. The strengths of synapses have also been studied by quantifying the sizes of synapses which can be regulated by learning. During my PhD, I have been developing computational methods for relating brain structure to function. I have developed tools and algorithms for analyzing connectomics data sets using Machine learning, statistical inference and Information Theory to find measures and biomarkers that can be used to probe mechanisms underlying leaning and memory in normal and diseased brains.

                We introduce here a powerful method for analyzing three-dimensional reconstruction from serial section electron microscopy (3DEM) to measure synaptic information storage capacity (SISC) and apply it to data following in vivo long-term potentiation (LTP). Quantifying precision is fundamental to understanding information storage and retrieval in neural circuits. We quantify this precision with Shannon information theory, which is a more reliable estimate than prior analyses based on signal detection theory. Spine head volumes are well correlated with other measures of synaptic weight, thus SISC can be determined by identifying the non-overlapping clusters of dendritic spine head volumes to determine the number of distinguishable synaptic weights. SISC analysis of spine head volumes in the stratum radiatum of hippocampal area CA1 revealed 24 distinguishable states (4.1 bits). In contrast, spine head volumes in the middle molecular layer of control dentate gyrus occupied only 5 distinguishable states (2 bits). Thus, synapses in different hippocampal regions had significantly different SISCs. Moreover, these were not fixed properties but increased by 30 min following induction of LTP in the dentate gyrus to occupy 10 distinguishable states (3 bits), and this increase lasted for at least 2 hours. We also observed a broader and nearly uniform distribution of spine head volumes across the increased number of states, suggesting the distribution evolved towards the theoretical upper bound of SISC following LTP. For dentate granule cells these findings show that the spine size range was broadened by the interplay among synaptic plasticity mechanisms. SISC provides a new analytical measure to probe these mechanisms in normal and diseased brains.

                • 4 supplemental videos
                Cover page: A Mathematical Theory of Synaptic Information Storage Capacity
                • Article
                • Peer Reviewed
                UC San Diego Previously Published Works (2014)
                Protein synthesis is crucial for both persistent synaptic plasticity and long-term memory. De novo protein expression can be restricted to specific neurons within a population, and to specific dendrites within a single neuron. Despite its ubiquity, the functional benefits of spatial protein regulation for learning are unknown. We used computational modeling to study this problem. We found that spatially patterned protein synthesis can enable selective consolidation of some memories but forgetting of others, even for simultaneous events that are represented by the same neural population. Key factors regulating selectivity include the functional clustering of synapses on dendrites, and the sparsity and overlap of neural activity patterns at the circuit level. Based on these findings, we proposed a two-step model for selective memory generalization during REM and slow-wave sleep. The pattern-matching framework we propose may be broadly applicable to spatial protein signaling throughout cortex and hippocampus.
                  Cover page: Selective Memory Generalization by Spatial Patterning of Protein Synthesis
                  • Article
                  • Peer Reviewed
                  UC San Diego Previously Published Works (2016)
                  Optimal control models of biological movements introduce external task factors to specify the pace of movements. Here, we present the dual to the principle of optimality based on a conserved quantity, called "drive," that represents the influence of internal motivation level on movement pace. Optimal control and drive conservation provide equivalent descriptions for the regularities observed within individual movements. For regularities across movements, drive conservation predicts a previously unidentified scaling law between the overall size and speed of various self-paced hand movements in the absence of any external tasks, which we confirmed with psychophysical experiments. Drive can be interpreted as a high-level control variable that sets the overall pace of movements and may be represented in the brain as the tonic levels of neuromodulators that control the level of internal motivation, thus providing insights into how internal states affect biological motor control.
                    Cover page: Conservation law for self-paced movements
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