Objective. Object recognition and making a choice regarding the recognized object is pivotal for most animals. This process in the brain contains information representation and decision making steps which both take different amount of times for different objects. While dynamics of object recognition and decision making are usually ignored in object recognition models, here we proposed a fully spiking hierarchical model, explaining the process of object recognition from information representation to making decision. Approach. Coupling a deep neural network and a recurrent attractor based decision making model beside using spike time dependent plasticity learning rules in several convolutional and pooling layers, we proposed a model which can resemble brain behaviors during an object recognition task. We also measured human choices and reaction times in a psychophysical object recognition task and used it as a reference to evaluate the model. Main results. The proposed model explains not only the probability of making a correct decision but also the time that it takes to make a decision. Importantly, neural firing rates in both feature representation and decision making levels mimic the observed patterns in animal studies (number of spikes (p-value < 10⁻¹⁷³) and the time of the peak response (p-value < 10⁻³¹) are significantly modulated with the strength of the stimulus). Moreover, the speed-accuracy trade-off as a well-known characteristic of decision making process in the brain is also observed in the model (changing the decision bound significantly affect the reaction time (p-value < 10⁻⁵⁹) and accuracy (p-value < 10⁻¹⁶⁵)). Significance. We proposed a fully spiking deep neural network which can explain dynamics of making decision about an object in both neural and behavioral level. Results showed that there is a strong and significant correlation (r = 0.57) between the reaction time of the model and of human participants in the psychophysical object recognition task.

中文翻译：

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深度神经网络和循环吸引子的完全尖峰耦合模型解释了对象识别任务中决策的动态

客观的。对于大多数动物来说，物体识别和针对识别物体做出选择至关重要。大脑中的这个过程包含信息表示和决策步骤，对于不同的对象，这两个步骤都需要不同的时间。虽然物体识别和决策的动态性在物体识别模型中通常被忽略，但在这里我们提出了一个完全尖峰的层次模型，解释了物体识别从信息表示到做出决策的过程。方法。除了在多个卷积层和池化层中使用尖峰时间相关的可塑性学习规则之外，将深度神经网络和基于循环吸引子的决策模型耦合起来，我们提出了一种可以在对象识别任务期间类似于大脑行为的模型。我们还测量了心理物理对象识别任务中人类的选择和反应时间，并将其用作评估模型的参考。主要结果。所提出的模型不仅解释了做出正确决策的概率，还解释了做出决策所需的时间。重要的是，特征表示和决策水平的神经放电率模仿了动物研究中观察到的模式（尖峰数量（p-value < 10 ⁻¹⁷³ ) 和峰值响应的时间 (p-值 < 10 ^-31 ) 随刺激强度显着调节)。此外，模型中还观察到速度与准确度权衡作为大脑决策过程的众所周知的特征（改变决策界限会显着影响反应时间（p-值 < 10 ⁻⁵⁹ ) 和准确度 (p-值 < 10 ^-165 ))。意义。我们提出了一个完全尖峰深度神经网络，它可以在神经和行为层面解释对对象做出决策的动态。结果表明，存在很强且显着的相关性（r= 0.57）模型和人类参与者在心理物理对象识别任务中的反应时间之间。