For applications on the extreme edge, minimal networks of only a few dozen artificial neurons for event detection and classification in discrete time signals would be highly desirable. Feed-forward networks, RNNs, and CNNs evolved through evolutionary algorithms can all be successful in this respect but pose the problem of allowing little systematicity in mutation and recombination if the standard direct genetic encoding of the weights is used (as for instance in the classic NEAT algorithm). We therefore introduce Echo Networks, a type of recurrent network that consists of the connection matrix only, with the source neurons of the synapses represented as rows, destination neurons as columns and weights as entries. There are no layers, and connections between neurons can be bidirectional but are technically all recurrent. Input and output can be arbitrarily assigned to any of the neurons and only use an additional (optional) function in their computational path, e.g., a sigmoid to obtain a binary classification output. We evaluated Echo Networks successfully on the classification of electrocardiography signals but see the most promising potential in their genome representation as a single matrix, allowing matrix computations and factorisations as mutation and recombination operators.

翻译：在极端边缘应用场景中，仅由几十个人工神经元组成的微型网络对离散时间信号的事件检测与分类具有极高价值。通过进化算法演化出的前馈网络、循环神经网络（RNN）和卷积神经网络（CNN）均能在该领域取得成效，但若采用标准直接遗传编码权重（例如经典NEAT算法），会导致变异与重组过程中缺乏系统性。为此我们提出回声网络（Echo Networks）——一种仅由连接矩阵构成的递归网络：突触源神经元对应矩阵行、目标神经元对应列、权重对应矩阵元素。该网络无层次结构，神经元间可双向连接，但在技术层面上所有连接均为递归连接。输入/输出可任意指定至任意神经元，仅在计算路径中附加可选函数（如sigmoid函数用于二分类输出）。我们已成功将回声网络应用于心电图信号分类，但其最具潜力的价值在于基因组以单一矩阵形式呈现——该表示方式允许将矩阵运算与矩阵分解作为变异与重组算子。