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首页> 外文期刊>Neuron >Dynamics of memory representations in networks with novelty-facilitated synaptic plasticity.
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Dynamics of memory representations in networks with novelty-facilitated synaptic plasticity.

机译：具有新颖性的突触可塑性网络中的内存表示动力学。

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摘要

The ability to associate some stimuli while differentiating between others is an essential characteristic of biological memory. Theoretical models identify memories as attractors of neural network activity, with learning based on Hebb-like synaptic modifications. Our analysis shows that when network inputs are correlated, this mechanism results in overassociations, even up to several memories "merging" into one. To counteract this tendency, we introduce a learning mechanism that involves novelty-facilitated modifications, accentuating synaptic changes proportionally to the difference between network input and stored memories. This mechanism introduces a dependency of synaptic modifications on previously acquired memories, enabling a wide spectrum of memory associations, ranging from absolute discrimination to complete merging. The model predicts that memory representations should be sensitive to learning order, consistent with recent psychophysical studies of face recognition and electrophysiological experiments on hippocampal place cells. The proposed mechanism is compatible with a recent biological model of novelty-facilitated learning in hippocampal circuitry.
机译：关联某些刺激而区分其他刺激的能力是生物记忆的基本特征。理论模型将记忆识别为神经网络活动的吸引者，并基于类似赫布的突触修饰进行学习。我们的分析表明，当网络输入相互关联时，这种机制会导致过度关联，甚至多达几个内存“合并”为一个。为了抵消这种趋势，我们引入了一种学习机制，该机制涉及新颖的修饰，与网络输入和存储的内存之间的差异成比例地强调突触变化。该机制引入了突触修饰对先前获取的存储器的依赖性，从而实现了从绝对判别到完全合并的广泛范围的存储器关联。该模型预测，记忆表征应对学习顺序敏感，这与最近对面部识别的心理生理研究和对海马体细胞的电生理实验一致。提出的机制与海马电路中新颖性促进学习的最新生物学模型兼容。

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