A hierarchy of phase transitions in optimal neuronal coding: from binary to M -ary discrete optimal codes
A hierarchy of phase transitions in optimal neuronal coding: from binary to M -ary discrete optimal codes
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最优神经元编码中的相变层次：从二进制到M元离散最优码
DOI：
10.1117/12.724410
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
Nikitin A
中科院分区：
文献类型：
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作者：
Nikitin A
我们研究了神经系统的最佳编码是如何随着解码时间的变化而变化的。优化是指最大化信息传递。我们在速率编码的假设下估计了优化香农转信息的泊松神经元的参数。我们观察到从二进制编码到离散（M-ary）编码的相变层次结构，对于较小的解码时间，具有两个，三个和更多的量化水平，用于较大的解码时间。我们假设具有特定神经特征的亚种群的存在可能是最优种群编码方案的标志，并以哺乳动物听觉系统为例。
We have investigated how optimal coding for neural systems changes with the time available for decoding. Optimization was in terms of maximizing information transmission. We have estimated the parameters for Poisson neurons that optimize Shannon transinformation with the assumption of rate coding. We observed a hierarchy of phase transitions from binary coding, for small decoding times, toward discrete (M-ary) coding with two, three and more quantization levels for larger decoding times. We postulate that the presence of subpopulations with specific neural characteristics could be a signiture of an optimal population coding scheme and we use the mammalian auditory system as an example.