ArticleScientific reports2021
A temporal hierarchical feedforward model explains both the time and the accuracy of object recognition.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Sensory and palatability coding of taste stimuli in cortex involves dynamic and asymmetric cortico-amygdalar interactions.Journal of neurophysiology · 2026Article
- Spatiotemporal Evidence Accumulation Through Saccadic Sampling for Object Recognition.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- A Simulated Visual Field Defect Impairs Temporal Processing: An Effect Not Modulated by Emotional Faces.Vision (Basel, Switzerland) · 2025Article
- Coupled Dynamics of Stimulus-Evoked Gustatory Cortical and Basolateral Amygdalar Activity.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Linear Integration of Sensory Evidence over Space and Time Underlies Face Categorization.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
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Authors and funding
3 authors.
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Abstract
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.
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