Sequence-dependent sensitivity explains the accuracy of decisions when cues are separated with a gap

A fully spiking coupled model of a deep neural network and a recurrent attractor explains dynamics of decision making in an object recognition task

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.

Explaining Integration of Evidence Separated by Temporal Gaps with Frontoparietal Circuit Models

Perceptual decisions rely on accumulating sensory evidence over time. However, the accumulation process is complicated in real life when evidence resulted from separated cues over time. Previous studies demonstrate that participants are able to integrate information from two separated cues to improve their performance invariant to an interval between the cues. However, there is no neural model that can account for accuracy and confidence in decisions when there is a time interval in evidence. We used behavioral and EEG datasets from a visual choice task -Random dot motion- with separated evidence to investigate three candid distributed neural networks. We showed that decisions based on evidence accumulation by separated cues over time are best explained by the interplay of recurrent cortical dynamics of centro-parietal and frontal brain areas while an uncertainty-monitoring module included in the model.

A temporal hierarchical feedforward model explains both the time and the accuracy of object recognition

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.

Flexible categorization in perceptual decision making

Perceptual decisions rely on accumulating sensory evidence. This computation has been studied using either drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both models can account for a large amount of data, it remains unclear whether their dynamics are qualitatively equivalent. Here we show that in the attractor model, but not in the drift diffusion model, an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision states. The increase in the number of transitions leads to a crossover between weighting mostly early evidence (primacy) to weighting late evidence (recency), a prediction we validate with psychophysical data. Between these two limiting cases, we found a novel flexible categorization regime, in which fluctuations can reverse initially-incorrect categorizations. This reversal asymmetry results in a non-monotonic psychometric curve, a distinctive feature of the attractor model. Our findings point to correcting decision reversals as an important feature of perceptual decision making.