Institut für Neuroinformatik, Ruhr-University Bochum, Bochum, Germany.
Mental Health Research and Treatment Center, Department of Clinical Child and Adolescent Psychology, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany.
PLoS One. 2018 Oct 4;13(10):e0204685. doi: 10.1371/journal.pone.0204685. eCollection 2018.
Episodic memories have been suggested to be represented by neuronal sequences, which are stored and retrieved from the hippocampal circuit. A special difficulty is that realistic neuronal sequences are strongly correlated with each other since computational memory models generally perform poorly when correlated patterns are stored. Here, we study in a computational model under which conditions the hippocampal circuit can perform this function robustly. During memory encoding, CA3 sequences in our model are driven by intrinsic dynamics, entorhinal inputs, or a combination of both. These CA3 sequences are hetero-associated with the input sequences, so that the network can retrieve entire sequences based on a single cue pattern. We find that overall memory performance depends on two factors: the robustness of sequence retrieval from CA3 and the circuit's ability to perform pattern completion through the feedforward connectivity, including CA3, CA1 and EC. The two factors, in turn, depend on the relative contribution of the external inputs and recurrent drive on CA3 activity. In conclusion, memory performance in our network model critically depends on the network architecture and dynamics in CA3.
情景记忆被认为是由神经元序列来表示的，这些序列存储在海马体回路中，并从该回路中检索出来。一个特殊的难题是，由于计算记忆模型在存储相关模式时通常表现不佳，因此真实的神经元序列彼此之间是强烈相关的。在这里，我们在一个计算模型中研究了在什么条件下海马体回路可以稳健地执行此功能。在记忆编码期间，我们的模型中的 CA3 序列由内在动力学、内嗅输入或两者的组合驱动。这些 CA3 序列与输入序列异关联，因此网络可以基于单个提示模式来检索整个序列。我们发现，整体记忆性能取决于两个因素：从 CA3 中检索序列的稳健性和通过前馈连接（包括 CA3、CA1 和 EC）执行模式完成的能力。这两个因素反过来又取决于外部输入和 CA3 活动的递归驱动的相对贡献。总之，我们的网络模型中的记忆性能严重依赖于 CA3 中的网络架构和动力学。
