Fundamental limits on persistent activity in networks of noisy neurons
Fundamental limits on persistent activity in networks of noisy neurons
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DOI：
10.1073/pnas.1117386109
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发表时间：
2012-10-23
影响因子：
11.1
通讯作者：
Fiete, Ila R.
中科院分区：
文献类型：
--
作者：
Burak, Yoram;Fiete, Ila R.
神经噪声限制了大脑中表征的保真度。这种限制已经被广泛分析为感官编码。然而，在短期记忆和积分器网络中，噪声会积累并发挥更重要的作用，关于神经噪声如何与神经和网络参数相互作用以确定计算精度的知识要少得多。在这里，我们分析得出如何存储在连续吸引子网络的概率尖峰神经元的记忆将随着时间的推移，通过扩散退化。通过结合统计和动力学的方法，我们建立了一个基本的限制网络的能力，以保持一个持久的状态：噪声引起的漂移的记忆状态随着时间的推移在网络内是严格的下限由一个理想的外部观察者的网络的瞬时记忆状态的估计的准确性。这一结果表现为信息扩散不等式。我们得到了一些意想不到的结果：尽管短期记忆网络的持续时间，它不值得积累尖峰超过细胞的时间常数读出他们的内容。对于某些神经传递函数，最佳感觉编码的条件与最佳存储的条件一致，这意味着短期记忆可能与感觉表征共定位。
Neural noise limits the fidelity of representations in the brain. This limitation has been extensively analyzed for sensory coding. However, in short-term memory and integrator networks, where noise accumulates and can play an even more prominent role, much less is known about how neural noise interacts with neural and network parameters to determine the accuracy of the computation. Here we analytically derive how the stored memory in continuous attractor networks of probabilistically spiking neurons will degrade over time through diffusion. By combining statistical and dynamical approaches, we establish a fundamental limit on the network's ability to maintain a persistent state: The noise-induced drift of the memory state over time within the network is strictly lower-bounded by the accuracy of estimation of the network's instantaneous memory state by an ideal external observer. This result takes the form of an information-diffusion inequality. We derive some unexpected consequences: Despite the persistence time of short-term memory networks, it does not pay to accumulate spikes for longer than the cellular time-constant to read out their contents. For certain neural transfer functions, the conditions for optimal sensory coding coincide with those for optimal storage, implying that short-termmemory may be co-localized with sensory representation.