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Open Access Publications from the University of California

Scholarly Works (109 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
      • Thesis
      • Peer Reviewed
      UC San Diego Electronic Theses and Dissertations (2023)

      We sense, move and think by dynamical interactions between neurons and brain regions. This thesis focuses on 1) how neural activities are organized in a network level to 2) achieve essential computation for cognition. With tools from dynamical systems, probability theory, statistical learning and information theory. I approached the above two questions as below:

      In Chapter 2, I developed a Dynamical Differential Covariance (DDC) to efficiently infer directed functional connectivity through neural activity recordings. It was validated and compared favorably with other causality inference methods on networks with false positive motifs and multiscale neural simulations where the ground truth connectivity was known. When applied to resting-state fMRI recordings, DDC consistently detected regional interactions with strong structural connectivity in over 1,000 individual subjects obtained by diffusion MRI (dMRI). The topological measures of DDC connectivity showed better behavioral relevance. For example, the global efficiency of DDC network is inversely correlated with reaction time in multiple psychological tasks.

      In Chapter 3, I linked the brain's anatomical connectivity to its functional connectivity with temporal precision in the circuit of hippocampal formation. I proposed the notion of predicting ahead to compensate for inter-regional transmission delays in the regime of precisely regulated temporal coding. The hypothesis was supported by analyzing the spike coupling between simultaneously recorded neurons in hippocampal subregions. Further modeling work also recapitulated characteristic place cell features.

        • Article
        • Peer Reviewed
        Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 6 (1984)
          Cover page: Cortical Computation
          • Thesis
          • Peer Reviewed
          UC San Diego Electronic Theses and Dissertations (2016)

          Measuring functional connectivity from neural recordings is important in understanding

          processing in cortical networks. The covariance-based methods are the current

          golden standard for functional connectivity estimation. However, the link between the

          pair-wise correlations and the physiological connections inside the neural network is unclear.

          Therefore, the power of inferring physiological basis from functional connectivity estimation

          is limited. To build a stronger tie and better understand the relationship between functional

          connectivity and physiological neural network, we need (1) a realistic model to simulate

          different types of neural recordings with known ground truth for benchmarking; (2) a new

          functional connectivity method that produce estimations closely reflecting the physiological

          basis.

          In this thesis, (1) I tune a spiking neural network model to match with human sleep

          EEG data, (2) introduce a new class of methods for estimating connectivity from different

          kinds of neural signals and provide theory proof for its superiority, (3) apply it to simulated

          fMRI data as an application.

            Cover page: A new class of methods for functional connectivity estimation
            • Article
            • Peer Reviewed
            UC San Diego Previously Published Works (2014)
            Data sets with high dimensionality such as natural images, speech, and text have been analyzed with methods from condensed matter physics. Here we compare recent approaches taken to relate the scale invariance of natural images to critical phenomena. We also examine the method of studying high-dimensional data through specific heat curves by applying the analysis to noncritical systems: 1D samples taken from natural images and 2D binary pink noise. Through these examples, we concluded that due to small sample sizes, specific heat is not a reliable measure for gauging whether high-dimensional data are critical. We argue that identifying order parameters and universality classes is a more reliable way to identify criticality in high-dimensional data.
              Cover page: On Criticality in High-Dimensional Data
              • 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 (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
                  • 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 (2015)
                  Nonlinear dynamical system analysis based on embedding theory has been used for modeling and prediction, but it also has applications to signal detection and classification of time series. An embedding creates a multidimensional geometrical object from a single time series. Traditionally either delay or derivative embeddings have been used. The delay embedding is composed of delayed versions of the signal, and the derivative embedding is composed of successive derivatives of the signal. The delay embedding has been extended to nonuniform embeddings to take multiple timescales into account. Both embeddings provide information on the underlying dynamical system without having direct access to all the system variables. Delay differential analysis is based on functional embeddings, a combination of the derivative embedding with nonuniform delay embeddings. Small delay differential equation (DDE) models that best represent relevant dynamic features of time series data are selected from a pool of candidate models for detection or classification. We show that the properties of DDEs support spectral analysis in the time domain where nonlinear correlation functions are used to detect frequencies, frequency and phase couplings, and bispectra. These can be efficiently computed with short time windows and are robust to noise. For frequency analysis, this framework is a multivariate extension of discrete Fourier transform (DFT), and for higher-order spectra, it is a linear and multivariate alternative to multidimensional fast Fourier transform of multidimensional correlations. This method can be applied to short or sparse time series and can be extended to cross-trial and cross-channel spectra if multiple short data segments of the same experiment are available. Together, this time-domain toolbox provides higher temporal resolution, increased frequency and phase coupling information, and it allows an easy and straightforward implementation of higher-order spectra across time compared with frequency-based methods such as the DFT and cross-spectral analysis.
                    Cover page: Delay Differential Analysis of Time Series
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