Computational Account of Spontaneous Activity as a Signature of Predictive Coding.
Computational Account of Spontaneous Activity as a Signature of Predictive Coding.
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DOI：
10.1371/journal.pcbi.1005355
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发表时间：
2017-01
影响因子：
4.3
通讯作者：
Denève S
中科院分区：
文献类型：
--
作者：
Koren V;Denève S
自发活动通常在各种皮质状态下观察到。实验证据表明，即使网络与大脑的其他部分隔离，神经组件也会经历上下状态的缓慢振荡。在这里，我们表明，这些自发事件可以由网络内的循环连接产生，并被理解为正在纠正其内部表示的神经回路的签名。当兴奋电流和抑制电流尽可能强且尽可能紧密平衡时，无噪声尖峰神经网络可以最准确地表示其输入信号。然而，在现实的神经噪声和突触延迟的存在下，这可能导致过大的尖峰计数。一个最佳的工作制度，可以找到通过考虑的条款，控制射击率的目标函数，从该网络推导出，然后同时最小化的编码错误和神经活动的成本。从生物学角度讲，这相当于调整神经阈值和后峰超极化。在次优的工作制度，我们观察到自发活动，即使在没有前馈输入。在一个全对全随机连接的网络中，整个种群都处于上状态。在具有局部连通性的空间组织网络中，Up状态通过相似选择性的神经元之间的局部连接传播，并以行波的形式传播。观察到的状态的参数范围很广，并在活动和静止状态具有相似的统计特性。在最佳工作机制中，Up状态消失，为异步活动留下空间，这表明这种工作机制是最有效编码的标志。虽然它们导致放电活动的大量增加，但自发Up状态的读出实际上与刺激表示正交，因此对网络功能的干扰最小。通常在大脑中观察到的自发活动爆发可以通过神经网络中的纠错计算来理解。在一个没有有效纠正其内部表现的网络中，爆发会自动出现。
Spontaneous activity is commonly observed in a variety of cortical states. Experimental evidence suggested that neural assemblies undergo slow oscillations with Up ad Down states even when the network is isolated from the rest of the brain. Here we show that these spontaneous events can be generated by the recurrent connections within the network and understood as signatures of neural circuits that are correcting their internal representation. A noiseless spiking neural network can represent its input signals most accurately when excitatory and inhibitory currents are as strong and as tightly balanced as possible. However, in the presence of realistic neural noise and synaptic delays, this may result in prohibitively large spike counts. An optimal working regime can be found by considering terms that control firing rates in the objective function from which the network is derived and then minimizing simultaneously the coding error and the cost of neural activity. In biological terms, this is equivalent to tuning neural thresholds and after-spike hyperpolarization. In suboptimal working regimes, we observe spontaneous activity even in the absence of feed-forward inputs. In an all-to-all randomly connected network, the entire population is involved in Up states. In spatially organized networks with local connectivity, Up states spread through local connections between neurons of similar selectivity and take the form of a traveling wave. Up states are observed for a wide range of parameters and have similar statistical properties in both active and quiescent state. In the optimal working regime, Up states are vanishing, leaving place to asynchronous activity, suggesting that this working regime is a signature of maximally efficient coding. Although they result in a massive increase in the firing activity, the read-out of spontaneous Up states is in fact orthogonal to the stimulus representation, therefore interfering minimally with the network function. Spontaneous bursts of activity, commonly observed in the brain, can be understood in terms of error-correcting computation within a neural network. Bursts arise automatically in a network that is inefficiently correcting its internal representation.