Eye-tracking studies in young children with autism spectrum disorder (ASD) have shown a visual attention preference for geometric patterns when viewing paired dynamic social images (DSIs) and dynamic geometric images (DGIs). In the present study, eye-tracking of two different paired presentations of DSIs and DGIs was monitored in a group of 13 children aged 4 to 6 years with ASD and 20 chronologically age-matched typically developing children (TDC). The results indicated that compared with the control group, children with ASD attended significantly less to DSIs showing two or more children playing than to similar DSIs showing a single child. Visual attention preference in 4- to 6-year-old children with ASDs, therefore, appears to be modulated by the type of visual stimuli.

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The anterior cingulate cortex (ACC) is frequently reported to have functionally distinct sub-regions that play key roles in different intrinsic networks. However, the contribution of the ACC, which is connected to several cortical areas and the limbic system, to autism is not clearly understood, although it may be involved in dysfunctions across several distinct but related functional domains. By comparing resting-state fMRI data from persons with autism and healthy controls, we sought to identify the abnormalities in the functional connectivity (FC) of ACC sub-regions in autism. The analyses found autism-related reductions in FC between the left caudal ACC and the right rolandic operculum, insula, postcentral gyrus, superior temporal gyrus, and the middle temporal gyrus. The FC (z-scores) between the left caudal ACC and the right insula was negatively correlated with the Stereotyped Behaviors and Restricted Interests scores of the autism group. These findings suggest that the caudal ACC is recruited selectively in the pathomechanism of autism.

Event-related potentials (ERPs) were recorded during a hybrid Simon-spatial Stroop task. We compared interference control and conflict monitoring in children with and without attention-deficit/hyperactivity disorder (ADHD), to examine developmental functional patterns. We found that children with ADHD exhibited lower accuracy rates and longer and more variable reaction time (RT) in both tasks, especially in the incongruent condition. In both controls and ADHD children, the accuracy rate increased and RT decreased with age. Major development in interference control occurred from 6-7 to 8 years in ADHD children and controls, yet only occurred from 9 to 10-11 years in normal children. The ERP results revealed that the N2 potentials were not significantly different from age-matched controls in the two tasks and that the development pattern of conflict monitoring was not different in school age children with and without ADHD. Children with ADHD had normal conflict monitoring ability.

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In this study, we investigated the age difference in numeral recognition and calculation in one group of school-aged children (n = 38) and one of undergraduate students (n = 26) using the event-related potential (ERP) methods. Consistent with previous reports, the age difference was significant in behavioral results. Both numeral recognition and calculation elicited a negativity peaking at about 170-280 ms (N2) and a positivity peaking at 200-470 ms (pSW) in raw ERPs, and a difference potential (dN3) between 360 and 450 ms. The difference between the two age groups indicated that more attention resources were devoted to arithmetical tasks in school-aged children, and that school-aged children and undergraduate students appear to use different strategies to solve arithmetical problems. The analysis of frontal negativity suggested that numeral recognition and mental calculation impose greater load on working memory and executive function in schoolchildren than in undergraduate students. The topography data determined that the parietal regions were responsible for arithmetical function in humans, and there was an age-related difference in the area of cerebral activation.

The explosive growth of data and information has motivated various emerging non-von Neumann computational approaches in the More-than-Moore era. Photonics neuromorphic computing has attracted lots of attention due to the fascinating advantages such as high speed, wide bandwidth, and massive parallelism. Here, we offer a review on the optical neural computing in our research groups at the device and system levels. The photonics neuron and photonics synapse plasticity are presented. In addition, we introduce several optical neural computing architectures and algorithms including photonic spiking neural network, photonic convolutional neural network, photonic matrix computation, photonic reservoir computing, and photonic reinforcement learning. Finally, we summarize the major challenges faced by photonic neuromorphic computing, and propose promising solutions and perspectives.