The representational capacity of the distributed encoding of information provided by populations of neurons in primate temporal visual cortex
The representational capacity of the distributed encoding of information provided by populations of neurons in primate temporal visual cortex
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DOI：
10.1007/pl00005615
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发表时间：
1997-03-01
影响因子：
2
通讯作者：
Tovee, MJ
中科院分区：
文献类型：
--
作者：
Rolls, ET;Treves, A;Tovee, MJ
已经证明，仅从一小部分神经元的放电速率就可以读取猕猴颞叶视觉皮质中用来区分被观察的不同面孔的代码。为了分析灵长类动物颞叶皮质视区单个神经元群体提供的信息，分析了一只猕猴在执行视觉注视任务时，14个神经元群体对20个视觉刺激的反应。被分析的神经元群体主要对面孔做出反应，使用的刺激都是人类和猴子的面孔。每个神经元对刺激集的不同成员都有自己的反应曲线。集合中每个神经元对每个刺激物的平均反应是从每个刺激物可用的十次数据试验中的一小部分计算出来的。从剩余的数据中，可以计算任何群体反应向量由集合中的每个刺激所引发的相对可能性。通过与实际显示的刺激进行比较，计算了平均正确识别率，并计算了关于刺激的平均信息(以位为单位)，即神经元群体进行了一次试验。当用于此计算的解码算法接近相对概率的最优贝叶斯估计时，正确百分比从对一个神经元的14%正确(机会为5%正确)增加到对14个神经元的67%。神经元种群传递的信息量从1个神经元的0.33比特近似线性增加到14个神经元的2.77比特。这导致了重要的结论，即可由视觉系统这一部分中的神经元群体编码的刺激数量随着样本中的细胞数量的增加而近似指数地增加(因为刺激数量的对数几乎线性地增加)。这与局部编码方案形成对比，在局部编码方案中，编码的刺激数量随着样本中的细胞数量线性增加。因此，分布式表示的潜在重要特性之一，即可以表示的刺激数量的指数增加，已经在具有这一组神经元的大脑中得到证明，当用于估计刺激似然度的算法与接收该组的输出的神经元容易地实现一样简单时(仅基于组响应向量和每个平均响应向量之间的点积)，仍然发现由14个神经元组成的组产生了66%的正确猜测并传达了2.30比特的信息，或者用几乎最优的过程可以提取的信息的83%。研究还表明，尽管表征中存在一些冗余(每个神经元对整个群体单独携带的信息贡献了60%的信息，而不是100%)，但这是因为集合中的刺激数量有限(它是20个)，对于足够大和不同的刺激集合，数据符合最小冗余。分布式编码方案对大脑连通性的含义是，如果神经元能够从这些神经元的随机子集接收其输入，即使是有限数量(例如，数百个)的输入，它也可以接收关于由大量神经元编码的大量信息。
It has been shown that it is possible to read, from the firing rates of just a small population of neurons, the code that is used in the macaque temporal lobe visual cortex to distinguish between different faces being looked at. To analyse the information provided by populations of single neurons in the primate temporal cortical visual areas, the responses of a population of 14 neurons to 20 visual stimuli were analysed in a macaque performing a visual fixation task. The population of neurons analysed responded primarily to faces, and the stimuli utilised were all human and monkey faces. Each neuron had its own response profile to the different members of the stimulus set. The mean response of each neuron to each stimulus in the set was calculated from a fraction of the ten trials of data available for every stimulus. From the remaining data, it was possible to calculate, for any population response vector, the relative likelihoods that it had been elicited by each of the stimuli in the set. By comparison with the stimuli actually shown, the mean percentage correct identification was computed and also the mean information about the stimuli, in bits, that the population of neurons carried on a single trial. When the decoding algorithm used for this calculation approximated an optimal, Bayesian estimate of the relative likelihoods, the percentage correct increased from 14% correct (chance was 5% correct) with one neuron to 67% with 14 neurons. The information conveyed by the population of neurons increased approximately linearly from 0.33 bits with one neuron to 2.77 bits with 14 neurons. This leads to the important conclusion that the number of stimuli that can be encoded by a population of neurons in this part of the visual system increases approximately exponentially as the number of cells in the sample increases (in that the log of the number of stimuli increases almost linearly). This is in contrast to a local encoding scheme (of ''grandmother'' cells), in which the number of stimuli encoded increases linearly with the number of cells in the sample. Thus one of the potentially important properties of distributed representations, an exponential increase in the number of stimuli that can be represented, has been demonstrated in the brain with this population of neurons, When the algorithm used for estimating stimulus likelihood was as simple as could be easily implemented by neurons receiving the population's output (based on just the dot product between the population response vector and each mean response vector), it was still found that the 14-neuron population produced 66% correct guesses and conveyed 2.30 bits of information, or 83% of the information that could be extracted with the nearly optimal procedure. It was also shown that, although there was some redundancy in the representation (with each neuron contributing to the information carried by the whole population 60% of the information it carried alone, rather than 100%), this is due to the fact that the number of stimuli in the set was limited (it was 20), The data are consistent with minimal redundancy for sufficiently large and diverse sets of stimuli. The implication for brain connectivity of the distributed encoding scheme, which was demonstrated here in the case of faces, is that a neuron can receive a great deal of information about what is encoded by a large population of neurons if it is able to receive, its inputs from a random subset of these neurons, even of limited numbers (e.g. hundreds).