PDF(Pubmed)
Abstract：
Gamma oscillations nested in a theta rhythm are observed in the hippocampus, where are assumed to play a role in sequential episodic memory, i.e., memorization and retrieval of events that unfold in time. In this work, we present an original neurocomputational model based on neural masses, which simulates the encoding of sequences of events in the hippocampus and subsequent retrieval by exploiting the theta-gamma code. The model is based on a three-layer structure in which individual Units oscillate with a gamma rhythm and code for individual features of an episode. The first layer (working memory in the prefrontal cortex) maintains a cue in memory until a new signal is presented. The second layer (CA3 cells) implements an auto-associative memory, exploiting excitatory and inhibitory plastic synapses to recover an entire episode from a single feature. Units in this layer are disinhibited by a theta rhythm from an external source (septum or Papez circuit). The third layer (CA1 cells) implements a hetero-associative net with the previous layer, able to recover a sequence of episodes from the first one. During an encoding phase, simulating high-acetylcholine levels, the network is trained with Hebbian (synchronizing) and anti-Hebbian (desynchronizing) rules. During retrieval (low-acetylcholine), the network can correctly recover sequences from an initial cue using gamma oscillations nested inside the theta rhythm. Moreover, in high noise, the network isolated from the environment simulates a mind-wandering condition, randomly replicating previous sequences. Interestingly, in a state simulating sleep, with increased noise and reduced synapses, the network can \"dream\" by creatively combining sequences, exploiting features shared by different episodes. Finally, an irrational behavior (erroneous superimposition of features in various episodes, like \"delusion\") occurs after pathological-like reduction in fast inhibitory synapses. The model can represent a straightforward and innovative tool to help mechanistically understand the theta-gamma code in different mental states.
摘要：
在海马中观察到嵌套在θ节律中的伽马振荡，假设在顺序情景记忆中发挥作用，即，记忆和检索及时展开的事件。在这项工作中,我们提出了一个基于神经质量的原始神经计算模型，它通过利用theta-gamma代码来模拟海马中事件序列的编码以及随后的检索。该模型基于三层结构，其中各个单元以伽玛节奏振荡，并编码情节的各个特征。第一层（前额叶皮层中的工作记忆）在记忆中保持提示，直到出现新信号。第二层（CA3单元）实现自动关联存储器，利用兴奋性和抑制性塑料突触从单个特征恢复整个发作。该层中的单位被来自外部来源（隔膜或Papez回路）的theta节律抑制。第三层（CA1单元）与上一层实现异质关联网，能够从第一个事件中恢复一系列事件。在编码阶段，模拟高乙酰胆碱水平，网络使用Hebbian（同步）和反Hebbian（去同步）规则进行训练。在检索过程中（低乙酰胆碱），网络可以使用嵌套在theta节奏内的伽马振荡从初始线索中正确恢复序列。此外，在高噪音中，与环境隔离的网络模拟了一种精神错乱的状态，随机复制以前的序列。有趣的是,在模拟睡眠的状态下，随着噪音的增加和突触的减少，网络可以通过创造性地组合序列来“梦想”，利用不同情节共有的特征。最后,非理性行为(错误叠加各种情节中的特征，像“妄想”）发生在快速抑制性突触的病理性减少之后。该模型可以代表一种简单而创新的工具，以帮助机械地理解不同精神状态下的theta-gamma代码。
