To navigate the world around us, neural circuits rapidly adapt to their environment learning generalizable strategies to decode information. When modeling these learning strategies, network models find the optimal solution to satisfy one task condition but fail when introduced to a novel task or even a different stimulus in the same space. In the experiments described in this letter, I investigate the role of lateral gap junctions in learning generalizable strategies to process information. Lateral gap junctions are formed by connexin proteins creating an open pore that allows for direct electrical signaling between two neurons. During neural development, the rate of gap junctions is high, and daughter cells that share similar tuning properties are more likely to be connected by these junctions. Gap junctions are highly plastic and get heavily pruned throughout development. I hypothesize that they mediate generalized learning by imprinting the weighting structure within a layer to avoid overfitting to one task condition. To test this hypothesis, I implemented a feedforward probabilistic neural network mimicking a cortical fast spiking neuron circuit that is heavily involved in movement. Many of these cells are tuned to speeds that I used as the input stimulus for the network to estimate. When training this network using a delta learning rule, both a laterally connected network and an unconnected network can estimate a single speed. However, when asking the network to estimate two or more speeds, alternated in training, an unconnected network either cannot learn speed or optimizes to a singular speed, while the laterally connected network learns the generalizable strategy and can estimate both speeds. These results suggest that lateral gap junctions between neurons enable generalized learning, which may help explain learning differences across life span.

中文翻译：

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横向连接提高了简单神经网络中学习的泛化性

为了驾驭我们周围的世界，神经回路快速适应环境，学习通用策略来解码信息。在对这些学习策略进行建模时，网络模型会找到满足一个任务条件的最佳解决方案，但在引入新任务甚至同一空间中的不同刺激时会失败。在这封信中描述的实验中，我研究了横向间隙连接在学习处理信息的通用策略中的作用。横向间隙连接由连接蛋白形成，形成开放孔，允许两个神经元之间直接进行电信号传导。在神经发育过程中，间隙连接的比率很高，具有相似调节特性的子细胞更有可能通过这些连接进行连接。间隙连接具有高度可塑性，并且在整个开发过程中会被大量修剪。我假设它们通过在一层内印上权重结构来调节广义学习，以避免过度拟合某个任务条件。为了测试这个假设，我实现了一个前馈概率神经网络，模仿与运动密切相关的皮质快速尖峰神经元电路。其中许多单元都调整到我用作网络估计的输入刺激的速度。当使用增量学习规则训练该网络时，横向连接的网络和未连接的网络都可以估计单个速度。然而，当要求网络在训练中交替估计两个或多个速度时，未连接的网络要么无法学习速度，要么无法优化到单一速度，而横向连接的网络则学习可概括的策略并可以估计两种速度。这些结果表明，神经元之间的横向间隙连接能够实现广义学习，这可能有助于解释整个生命周期的学习差异。