Capacity analysis in multi-state synaptic models: a retrieval probability perspective
Capacity analysis in multi-state synaptic models: a retrieval probability perspective
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DOI：
10.1007/s10827-010-0287-7
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发表时间：
2011-06-01
影响因子：
1.2
通讯作者：
Amit, Yali
中科院分区：
文献类型：
--
作者：
Huang, Yibi;Amit, Yali
我们定义的二进制神经元与有限状态突触的网络的记忆能力的检索概率的学习模式下的标准异步动态与预定的阈值。设定阈值是为了控制非选择性神经元的比例。选择一个最佳的抑制水平来稳定网络行为。对于任何本地学习规则，我们提供了一个计算效率和高度准确的近似检索概率的模式作为其年龄的函数。该方法应用于序列模型（Fusi和Abbott，Nat Neurosci 10：485-493，2007）和元塑性模型（Fusi等人，Neuron 45（4）：599-611，2005; Leibold和Kempter，Cereb Cortex 18：67-77，2008）。我们表明，随着突触状态的数量增加，容量，如这里所定义的，要么高原或减少。在少数情况下，多状态模型超过二进制突触模型的能力，改善是小的。
We define the memory capacity of networks of binary neurons with finite-state synapses in terms of retrieval probabilities of learned patterns under standard asynchronous dynamics with a predetermined threshold. The threshold is set to control the proportion of non-selective neurons that fire. An optimal inhibition level is chosen to stabilize network behavior. For any local learning rule we provide a computationally efficient and highly accurate approximation to the retrieval probability of a pattern as a function of its age. The method is applied to the sequential models (Fusi and Abbott, Nat Neurosci 10:485-493, 2007) and meta-plasticity models (Fusi et al., Neuron 45(4):599-611, 2005; Leibold and Kempter, Cereb Cortex 18:67-77, 2008). We show that as the number of synaptic states increases, the capacity, as defined here, either plateaus or decreases. In the few cases where multi-state models exceed the capacity of binary synapse models the improvement is small.