Learning Spike-Based Population Codes by Reward and Population Feedback
Learning Spike-Based Population Codes by Reward and Population Feedback
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DOI：
10.1162/neco.2010.05-09-1010
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发表时间：
2010-07-01
影响因子：
2.9
通讯作者：
Senn, Walter
中科院分区：
文献类型：
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作者：
Friedrich, Johannes;Urbanczik, Robert;Senn, Walter
我们研究了最近提出的一种在尖峰神经元群体中进行决策学习的模型，其中除了奖励反馈之外，突触可塑性还受到群体信号的调节。对于基本模型，基于尖峰/无尖峰编码的二元群体决策，给出了关于学习性能如何依赖于群体规模和任务复杂性的详细计算分析。接下来，我们将基本模型扩展到 n 元决策，并表明它也可以与其他总体代码（例如速率甚至延迟编码）结合使用。
We investigate a recently proposed model for decision learning in a population of spiking neurons where synaptic plasticity is modulated by a population signal in addition to reward feedback. For the basic model, binary population decisionmaking based on spike/no-spike coding, a detailed computational analysis is given about how learning performance depends on population size and task complexity. Next, we extend the basic model to n-ary decision making and show that it can also be used in conjunction with other population codes such as rate or even latency coding.