OpenTalks是OpenScience的學術策劃小組與NeuroChat團隊聯合組織的在線學術交流活動，旨在促進研究者之間的交流。我們將邀請國內外的研究者，尤其是青年研究者，進行在線學術報告與討論。學術報告的主題涉及可重複性、神經影像等。分享語言以中文為主，分享人偏好英文時，將使用英文。歡迎大家推薦報告人或者自薦作為報告人。
計算神經科學通過建立理論和數學模型來探尋大腦信息處理的一般性原理。這樣不僅為神經科學與認知科學的實驗觀測提供理論基礎以及實驗預測，而且可以從大腦得到啟發來發展新的類腦人工智慧算法。神經科學研究中的一個基本問題是大腦神經活動如何表徵、推測與提取外界信息，即神經編碼(Neural coding)，它是大腦（多模態）感覺信息處理、知覺決策、感覺運動轉換、學習與記憶的基礎。那麼大腦神經元如何相互作用產生神經活動，這些神經活動以何種形式來編碼外界信息？此外，大腦神經活動看似雜亂且充滿噪聲，它們又如何穩定地編碼外界信息進而產生穩定的知覺？本期OpenTalk我們有幸邀請到美國芝加哥大學的張文昊博士分享他在這方面的研究。具體內容詳見如下摘要。
Recurrent circuit based neural population codes for stimulus representation and inference
回饋神經環路如何對刺激進行表徵與推理?
A large part of the synaptic input received by cortical neurons comes from local cortico-cortical connectivity. Despite their abundance, the role of local recurrence in cortical function is unclear, and in simple coding schemes it is often the case that a circuit with no recurrent connections performs optimally. We consider a recurrent excitatory-inhibitory circuit model of a cortical hypercolumn which performs sampling-based Bayesian inference to infer latent hierarchical stimulus features. We show that local recurrent connections can store an internal model of the correlations between stimulus features that are present in the external world. When the resulting recurrent input is combined with feedforward input it produces a population code from which the posterior over the stimulus features can be linearly read out. Internal Poisson spiking variability provides the proper fluctuations for the population to sample stimulus features, yet the resultant population variability is aligned along the stimulus feature direction, producing what are termed differential correlations.
Importantly, the amplitude of these internally generated differential correlations is determined by the associative prior in the model stored in the recurrent connections, thus providing experimentally testable predictions for how population connectivity and response variability are connected to the structure of latent external stimuli.
北京時間[UTC+8] 1月31日(周日) 21:00
歐洲中部時間[CET] 1月31日(周日) 14:00美國東部時間[EST] 1月31日(周日) 08:00
Meeting ID: 913 9401 0836
Wenhao Zhang is a postdoc studying theoretical neuroscience at the University of Chicago. Before that, he did his postdoc research at the University of Pittsburgh and Carnegie Mellon University. His research mainly focuses on developing normative theories and biologically plausible models that address fundamental questions of neural information processing. A distinguishing feature of his research is it tightly combines abstract computational theories with concrete neural circuit models that are amenable to experimental testing. To achieve this broad research goal he combines techniques from nonlinear dynamics, Bayesian inference, neural coding, information theory, and Lie group theory. To ground his theoretical framework, most of his accomplished studies use correlated response variability and multisensory integration as examples to provide concrete experimental pre/post-dictions. He is one of few researchers in the field whose interdisciplinary work has been published in both top neuroscience journals and top machine learning conferences.
組織團隊(按姓氏首字母排序)
NeuroChat團隊
張文昊(Chicago)
張洳源(SJTU)
張磊(Vienna)
應浩江(Soochow)
徐婷(CMI)
王鑫迪(MNI)
滕相斌(MPI)
孔祥禎(ZJU)
胡傳鵬(NNU)
邸新(NJIT)
OpenTalks學術策劃小組
張磊 (博士), University of Vienna, Austria
張晗(博士), National University of Singapore
王鑫迪(博士), MNI, Canada
王慶(博士), MNI, Canada
金海洋(博士), New York University Abu Dhabi, United Arab Emirates
胡傳鵬(博士), Nanjing Normal University
耿海洋(博士), the University of Hong Kong
金淑嫻 (在讀博士生), Vrije Universiteit Amsterdam, the Netherlands
楊金驫 (在讀博士生), Max Planck Institute for Psycholinguistics, the Netherlands
排版：高小小
校對：王薇薇
胡傳鵬