图 书 馆
尖峰响应模型中具有乘法适应的高效穗编码Efficient Spike-Coding with Multiplicative Adaptation in a Spike Response Model |
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| 课程网址: | http://videolectures.net/machine_bohte_model/ |
| 主讲教师: | Sander M. Bohte |
| 开课单位: | 数学与信息中心 |
| 开课时间: | 信息不详。欢迎您在右侧留言补充。 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Neural adaptation underlies the ability of neurons to maximize encoded information over a wide dynamic range of input stimuli. While adaptation is an intrinsic feature of neuronal models like the Hodgkin-Huxley model, the challenge is to integrate adaptation in models of neural computation. Recent computational models like the Adaptive Spike Response Model implement adaptation as spike-based addition of fixed-size fast spike-triggered threshold dynamics and slow spike-triggered currents. Such adaptation has been shown to accurately model neural spiking behavior over a limited dynamic range. Taking a cue from kinetic models of adaptation, we propose a multiplicative Adaptive Spike Response Model where the spike-triggered adaptation dynamics are scaled multiplicatively by the adaptation state at the time of spiking. We show that unlike the additive adaptation model, the firing rate in the multiplicative adaptation model saturates to a maximum spike-rate. When simulating variance switching experiments, the model also quantitatively fits the experimental data over a wide dynamic range. Furthermore, dynamic threshold models of adaptation suggest a straightforward interpretation of neural activity in terms of dynamic signal encoding with shifted and weighted exponential kernels. We show that when thus encoding rectified filtered stimulus signals, the multiplicative Adaptive Spike Response Model achieves a high coding efficiency and maintains this efficiency over changes in the dynamic signal range of several orders of magnitude, without changing model parameters. |
| 关 键 词: | 生物学; 神经科学; 计算机科学; 机器学习 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-11-16：cwx |
| 阅读次数: | 33 |