天码欧美日本一道免费 偷自拍亚洲综合在线 特级毛片a级毛片免费观看

<ins id="kdes6"></ins>
<th id="kdes6"></th>
  • <progress id="kdes6"><dd id="kdes6"><big id="kdes6"></big></dd></progress>
  • <li id="kdes6"></li>
      1. <li id="kdes6"><em id="kdes6"></em></li>

          首 頁機構概況機構設置科研人才隊伍合作交流研究生教育博士后圖書館創新文化黨群園地院重點實驗室彭桓武中心信息公開
            學術活動
            您現在的位置:首頁 > 學術活動 > 專題學術報告/Seminar
          (Seminar) Distributed Sampling-based Bayesian Inference in Coupled Neural Circuits
          2020-10-12  【 】【打印】【關閉

          CAS Key Laboratory of Theoretical Physics

          Institute of Theoretical Physics

          Chinese Academy of Sciences

          Seminar

          Title

          題目

          Distributed Sampling-based Bayesian Inference in Coupled Neural Circuits

          Speaker

          報告人

          Dr. Wenhao Zhang

          Affiliation

          所在單位

          University of Pittsburg, USA

          Date

          日期

          Oct.12 (Monday) 2020, 09:30 - 10:30
           

          Venue

          地點

          Zoom Meeting Room ID: 518 052 9336

          Contact Person

          所內聯系人

          周海軍

          Abstract

          摘要

          The brain performs probabilistic inference to interpret the external world, but the underlying neuronal mechanisms remain not well understood. The stimulus structure of natural scenes exists in a high-dimensional feature space, and how the brain represents and infers the joint posterior distribution in this rich, combinatorial space is a challenging problem. There is added difficulty when considering the neuronal mechanics of this representation, since many of these features are com- puted in parallel by distributed neural circuits. Here, we present a novel solution to this problem. We study continuous attractor neural networks (CANNs), each representing and inferring a stimulus attribute, where attractor coupling supports sampling-based inference on the multivariate posterior of the high-dimensional stimulus features. Using perturbative analysis, we show that the dynamics of coupled CANNs realizes Langevin sampling on the stimulus feature manifold embedded in neural population responses. In our framework, feedforward inputs convey the likelihood, reciprocal connections encode the stimulus correlational priors, and the internal Poisson variability of the neurons generate the correct random walks for sampling. Our model achieves high-dimensional joint probability representation and Bayesian inference in a distributed manner, where each attractor network infers the marginal posterior of the corresponding stimulus feature. The stimulus feature can be read out simply with a linear decoder based only on local activities of each network. Simulation experiments confirm our theoretical analysis. The study provides insight into the fundamental neural mechanisms for realizing efficient high-dimensional probabilistic inference.
          IE6.0瀏覽器,1024X768分辨率 版權所有 ? 中國科學院理論物理研究所
          地址:北京市海淀區中關村東路55號 郵政編碼:100190
          京ICP備05002865號】 京公網安備1101080094號
          <ins id="kdes6"></ins>
          <th id="kdes6"></th>
        1. <progress id="kdes6"><dd id="kdes6"><big id="kdes6"></big></dd></progress>
        2. <li id="kdes6"></li>
            1. <li id="kdes6"><em id="kdes6"></em></li>

                天码欧美日本一道免费 偷自拍亚洲综合在线 特级毛片a级毛片免费观看

                品牌简介

                天码欧美日本一道免费 偷自拍亚洲综合在线 特级毛片a级毛片免费观看