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: | 神经适应是神经元在输入刺激的大动态范围内最大化编码信息的能力的基础。虽然适应是像霍奇金-赫胥黎模型这样的神经元模型的固有特征,但挑战在于将适应整合到神经计算模型中。最近的计算模型,如自适应尖峰响应模型,将自适应作为基于尖峰的固定尺寸快速尖峰触发阈值动力学和缓慢尖峰触发电流的添加来实现。这种适应已被证明能够在有限的动态范围内精确地模拟神经刺穿行为。基于适应动力学模型,我们提出了一种乘性自适应尖峰响应模型,其中尖峰触发的适应动力学与尖峰时的适应状态相乘。结果表明,与加性自适应模型不同,乘性自适应模型中的发射率达到最大峰值速率。在模拟方差切换实验时,该模型还定量地拟合了大动态范围内的实验数据。此外,动态适应阈值模型提出了一个简单的解释神经活动的动态信号编码移位和加权指数核。结果表明,当对校正后的滤波刺激信号进行编码时,乘性自适应尖峰响应模型在不改变模型参数的情况下,在几个数量级的动态信号范围内保持了较高的编码效率。 |
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: | 39 |