报告题目：The space and time of neuronal variability in a spiking neuron network
主讲：Dr ChengCheng Huang
日期：2017 年6月6日 (周二)
时间：上午9:30 – 10:30
地点：jbo竞博电竞官方网站南校区336栋A210
主持：邹青松 教授
摘要:
Shared variability among neurons (noise correlations) have been commonly observed in multiple cortical areas (Cohen and Kohn, 2011). Moreover, noise correlations are modulated by cognitive factors, such as overall arousal, task engagement and attention (Cohen and Maunsell, 2009; Doiron et al. 2016). While there is much discussion about the consequences of noise correlations on neuronal coding, there is a general lack of understanding of the circuit mechanisms that generate and modulate shared variability in the brain. Recently, simultaneous microelectrode array recordings from V1 and MT in behaving monkeys (Ruff and Cohen, 2016) suggest that attention not only decreases correlations within a cortical area (MT), but also increases correlations between cortical areas (V1 and MT). The differential modulation of between-areas and within-area noise correlations impose further constraints on circuit mechanisms for the generation and propagation of noise correlations. We develop a spiking neuron network with spatiotemporal dynamics that internally generates shared variability matching the low dimensional structure widely reported across cortex. This variability results from macroscopic chaos in population rates, which correlates neurons from the same recurrent network while decoupling them from feedforward inputs. Attention is modeled as depolarizing the inhibitory neuron population, which reduces the internally generated shared variability and allows the network to better track input signal. Moreover, dimensionality analysis of spike trains in both model and data shows that attention modulates only the first dimension of fluctuations, again consistent with past phenomenological models of cortical data. Our model provides a much needed mechanism for how shared variability is both generated and modulated in recurrent cortical networks.
报告人简介:
黄橙橙博士, 2010年本科毕业于南京大学数学系，2015年博士毕业于纽约大学Courant数学研究所。2015. 8 - 现在 美国匹兹堡大学 做博士后。博士期间主要工作是对听觉感知现象的神经网络模拟。博士后期间主要工作是研究神经网络中个神经元的关联性和活动空间的维度。