Wijesinghe Parami, Liyanagedera Chamika, Roy Kaushik
School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States.
Front Neurosci. 2020 Aug 12;14:772. doi: 10.3389/fnins.2020.00772. eCollection 2020.
Biological brain stores massive amount of information. Inspired by features of the biological memory, we propose an algorithm to efficiently store different classes of spatio-temporal information in a Recurrent Neural Network (RNN). A given spatio-temporal input triggers a neuron firing pattern, known as an attractor, and it conveys information about the class to which the input belongs. These attractors are the basic elements of the memory in our RNN. Preparing a set of good attractors is the key to efficiently storing temporal information in an RNN. We achieve this by means of enhancing the "separation" and "approximation" properties associated with the attractors, during the RNN training. We furthermore elaborate how these attractors can trigger an action via the readout in the RNN, similar to the sensory motor action processing in the cerebellum cortex. We show how different voice commands by different speakers trigger hand drawn impressions of the spoken words, by means of our separation and approximation based learning. The method further recognizes the gender of the speaker. The method is evaluated on the TI-46 speech data corpus, and we have achieved classification accuracy on the TI-46 digit corpus.
生物大脑存储着海量信息。受生物记忆特征的启发，我们提出了一种算法，用于在循环神经网络（RNN）中高效存储不同类别的时空信息。给定的时空输入会触发一种神经元放电模式，即吸引子，它传达了关于输入所属类别的信息。这些吸引子是我们RNN中记忆的基本元素。准备一组良好的吸引子是在RNN中高效存储时间信息的关键。我们通过在RNN训练期间增强与吸引子相关的“分离”和“逼近”属性来实现这一点。我们还详细阐述了这些吸引子如何通过RNN中的读出触发动作，类似于小脑皮质中的感觉运动动作处理。我们展示了不同说话者发出的不同语音命令如何通过基于分离和逼近的学习触发对所说单词的手绘印象。该方法还能识别说话者的性别。我们在TI - 46语音数据语料库上对该方法进行了评估，并在TI - 46数字语料库上取得了分类准确率。
