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Dynamical Synapses Enhance Neural Information Processing: Gracefulness, Accuracy, and Mobility
文献类型：期刊论文
| 作者 | Fung, C. C. Alan ; Wong, K. Y. Michael ; Wang, He ; Wu, Si |
| 刊名 | NEURAL COMPUTATION
 |
| 出版日期 | 2012 |
| 卷号 | 24期号:5页码:1147-1185 |
| ISSN号 | 0899-7667 |
| 关键词 | NEOCORTICAL PYRAMIDAL NEURONS SYNAPTIC DEPRESSION RELEASE PROBABILITY WORKING-MEMORY HEAD-DIRECTION NETWORKS FACILITATION CORTEX ORIENTATION PLASTICITY |
| 通讯作者 | Fung, CCA (reprint author), Hong Kong Univ Sci & Technol, Dept Phys, Hong Kong, Hong Kong, Peoples R China,alanfung@ust.hk ; phkywong@ust.hk ; wangheaq@gmail.com ; wusi@bnu.edu.cn |
| 英文摘要 | Experimental data have revealed that neuronal connection efficacy exhibits two forms of short-term plasticity: short-term depression (STD) and short-term facilitation (STF). They have time constants residing between fast neural signaling and rapid learning and may serve as substrates for neural systems manipulating temporal information on relevant timescales. This study investigates the impact of STD and STF on the dynamics of continuous attractor neural networks and their potential roles in neural information processing. We find that STD endows the network with slow-decaying plateau behaviors: the network that is initially being stimulated to an active state decays to a silent state very slowly on the timescale of STD rather than on that of neuralsignaling. This provides a mechanism for neural systems to hold sensory memory easily and shut off persistent activities gracefully. With STF, we find that the network can hold a memory trace of external inputs in the facilitated neuronal interactions, which provides a way to stabilize the network response to noisy inputs, leading to improved accuracy in population decoding. Furthermore, we find that STD increases the mobility of the network states. The increased mobility enhances the tracking performance of the network in response to time-varying stimuli, leading to anticipative neural responses. In general, we find that STD and STP tend to have opposite effects on network dynamics and complementary computational advantages, suggesting that the brain may employ a strategy of weighting them differentially depending on the computational purpose. |
| 学科主题 | Computer Science ; Neurosciences & Neurology |
| 资助信息 | Research Grants Council of Hong Kong[604008, 605010]; 973 program of China[2011CBA00406]; Natural Science Foundation of China[91132702] |
| 收录类别 | SCI |
| 语种 | 英语 |
| 公开日期 | 2012-07-13 |
| 源URL | [http://ir.sibs.ac.cn/handle/331001/1488]  |
| 专题 | 上海神经科学研究所_神经所(总) |
| 推荐引用方式 GB/T 7714 | Fung, C. C. Alan,Wong, K. Y. Michael,Wang, He,et al. Dynamical Synapses Enhance Neural Information Processing: Gracefulness, Accuracy, and Mobility[J]. NEURAL COMPUTATION,2012,24(5):1147-1185. |
| APA | Fung, C. C. Alan,Wong, K. Y. Michael,Wang, He,&Wu, Si.(2012).Dynamical Synapses Enhance Neural Information Processing: Gracefulness, Accuracy, and Mobility.NEURAL COMPUTATION,24(5),1147-1185. |
| MLA | Fung, C. C. Alan,et al."Dynamical Synapses Enhance Neural Information Processing: Gracefulness, Accuracy, and Mobility".NEURAL COMPUTATION 24.5(2012):1147-1185. |
入库方式： OAI收割
来源：上海神经科学研究所
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