Faculty of Computer Engineering, Shahid Rajaee Teacher Training University, P.O. Box 16785-163, Tehran, Iran.
School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), P.O. Box 19395-5746, Tehran, Iran.
Sci Rep. 2021 Mar 11;11(1):5640. doi: 10.1038/s41598-021-85198-2.
Brain can recognize different objects as ones it has previously experienced. The recognition accuracy and its processing time depend on different stimulus properties such as the viewing conditions, the noise levels, etc. Recognition accuracy can be explained well by different models. However, most models paid no attention to the processing time, and the ones which do, are not biologically plausible. By modifying a hierarchical spiking neural network (spiking HMAX), the input stimulus is represented temporally within the spike trains. Then, by coupling the modified spiking HMAX model, with an accumulation-to-bound decision-making model, the generated spikes are accumulated over time. The input category is determined as soon as the firing rates of accumulators reaches a threshold (decision bound). The proposed object recognition model accounts for both recognition time and accuracy. Results show that not only does the model follow human accuracy in a psychophysical task better than the well-known non-temporal models, but also it predicts human response time in each choice. Results provide enough evidence that the temporal representation of features is informative, since it can improve the accuracy of a biologically plausible decision maker over time. In addition, the decision bound is able to adjust the speed-accuracy trade-off in different object recognition tasks.
大脑可以识别出它之前经历过的不同物体。识别的准确性及其处理时间取决于不同的刺激特性，如观察条件、噪声水平等。不同的模型可以很好地解释识别准确性。然而，大多数模型都没有关注处理时间，而关注处理时间的模型则不具有生物学意义。通过修改分层尖峰神经网络（spiking HMAX），输入的刺激在尖峰脉冲序列中进行时间表示。然后，通过将修改后的尖峰 HMAX 模型与累积到边界的决策模型相结合，生成的尖峰脉冲随着时间的推移而累积。一旦累加器的放电率达到阈值（决策边界），就确定输入类别。所提出的目标识别模型同时考虑了识别时间和准确性。结果表明，该模型不仅在心理物理任务中比著名的非时间模型更能准确地反映人类的准确性，而且还能预测人类在每个选择中的反应时间。结果提供了充分的证据表明，特征的时间表示是有信息的，因为它可以随着时间的推移提高生物上合理的决策者的准确性。此外，决策边界能够调整不同目标识别任务中的速度-准确性权衡。
