Efficient Computation in Adaptive Artificial Spiking Neural Networks

Artificial Neural Networks (ANNs) are bio-inspired models of neural computation that have proven highly effective. Still, ANNs lack a natural notion of time, and neural units in ANNs exchange analog values in a frame-based manner, a computationally and energetically inefficient form of communication. This contrasts sharply with biological neurons that communicate sparingly and efficiently using binary spikes. While artificial Spiking Neural Networks (SNNs) can be constructed by replacing the units of an ANN with spiking neurons, the current performance is far from that of deep ANNs on hard benchmarks and these SNNs use much higher firing rates compared to their biological counterparts, limiting their efficiency. Here we show how spiking neurons that employ an efficient form of neural coding can be used to construct SNNs that match high-performance ANNs and exceed state-of-the-art in SNNs on important benchmarks, while requiring much lower average firing rates. For this, we use spike-time coding based on the firing rate limiting adaptation phenomenon observed in biological spiking neurons. This phenomenon can be captured in adapting spiking neuron models, for which we derive the effective transfer function. Neural units in ANNs trained with this transfer function can be substituted directly with adaptive spiking neurons, and the resulting Adaptive SNNs (AdSNNs) can carry out inference in deep neural networks using up to an order of magnitude fewer spikes compared to previous SNNs. Adaptive spike-time coding additionally allows for the dynamic control of neural coding precision: we show how a simple model of arousal in AdSNNs further halves the average required firing rate and this notion naturally extends to other forms of attention. AdSNNs thus hold promise as a novel and efficient model for neural computation that naturally fits to temporally continuous and asynchronous applications

Visual pathways from the perspective of cost functions and multi-task deep neural networks

Vision research has been shaped by the seminal insight that we can understand the higher-tier visual cortex from the perspective of multiple functional pathways with different goals. In this paper, we try to give a computational account of the functional organization of this system by reasoning from the perspective of multi-task deep neural networks. Machine learning has shown that tasks become easier to solve when they are decomposed into subtasks with their own cost function. We hypothesize that the visual system optimizes multiple cost functions of unrelated tasks and this causes the emergence of a ventral pathway dedicated to vision for perception, and a dorsal pathway dedicated to vision for action. To evaluate the functional organization in multi-task deep neural networks, we propose a method that measures the contribution of a unit towards each task, applying it to two networks that have been trained on either two related or two unrelated tasks, using an identical stimulus set. Results show that the network trained on the unrelated tasks shows a decreasing degree of feature representation sharing towards higher-tier layers while the network trained on related tasks uniformly shows high degree of sharing. We conjecture that the method we propose can be used to analyze the anatomical and functional organization of the visual system and beyond. We predict that the degree to which tasks are related is a good descriptor of the degree to which they share downstream cortical-units.Comment: 16 pages, 5 figure

Guided Act and Feel Indonesia (GAF-ID) – Internet-based behavioral activation intervention for depression in Indonesia: study protocol for a randomized controlled trial

Background: Depression is a leading cause of disease burden across the world. However, in low-middle income countries (LMICs), access to mental health services is severely limited because of the insufficient number of mental health professionals available. The WHO initiated the Mental Health Gap Action Program (mhGAP) aiming to provide a coherent strategy for closing the gap between what is urgently needed and what is available in LMICs. Internet-based treatment is a promising strategy that can be made available to a large number of people now that Internet access is increasing rapidly throughout the world. The present study will investigate whether such an Internet-based treatment for depression is effective in Indonesia.  Methods: An Internet-based behavioral activation treatment, with support by lay counselors who will provide online feedback on the assignments and supportive phone contact to encourage participants to work in the program (Guided Act and Feel Indonesia/GAF-ID), is compared to an online-delivered minimal psychoeducation without any support (psychoeducation/PE). Initial assessment for inclusion is based on a Patient Health Questionnaire-9 (PHQ-9) score of at least 10 and meeting criteria for major depressive disorder or persistent depressive disorder as assessed using the Structured Clinical Interview for DSM-5 (SCID-5). Participants with depression (N=312) will be recruited and randomly assigned to GAF-ID or PE. Overall assessments will be done at baseline, post intervention (10 weeks from baseline) and follow-ups (3 months and 6 months from baseline). The primary outcome is the reduction of depression symptoms as measured by the PHQ-9 after 10 weeks from baseline.  Discussion: To our knowledge, this is the first study in Indonesia that examines the effectiveness of an Internet-based intervention for depression in a randomized controlled trial. The hope is that it can serve as a starting point for bridging the mental health gap in Indonesia and other LMICs. Trial registration: Nederlands Trial Register ( www.trialregister.nl ): NTR5920 , registered on 1 July 2016

Visual features drive the category-specific impairments on categorization tasks in a patient with object agnosia

Object and scene recognition both require mapping of incoming sensory information to existing conceptual knowledge about the world. A notable finding in brain-damaged patients is that they may show differentially impaired performance for specific categories, such as for “living exemplars”. While numerous patients with category-specific impairments have been reported, the explanations for these deficits remain controversial. In the current study, we investigate the ability of a brain injured patient with a well-established category-specific impairment of semantic memory to perform two categorization experiments: ‘natural’ vs. ‘manmade’ scenes (experiment 1) and objects (experiment 2). Our findings show that the pattern of categorical impairment does not respect the natural versus manmade distinction. This suggests that the impairments may be better explained by differences in visual features, rather than by category membership. Using Deep Convolutional Neural Networks (DCNNs) as ‘artificial animal models’ we further explored this idea. Results indicated that DCNNs with ‘lesions’ in higher order layers showed similar response patterns, with decreased relative performance for manmade scenes (experiment 1) and natural objects (experiment 2), even though they have no semantic category knowledge, apart from a mapping between pictures and labels. Collectively, these results suggest that the direction of category-effects to a large extent depends, at least in MS′ case, on the degree of perceptual differentiation called for, and not semantic knowledge

Act quickly, decide later: long latency visual processing underlies perceptual decisions but not reflexive behavior

Jolij J, Scholte H, Van Gaal S, Hodgson TL, Lamme VAF (2011) Act quickly, decide later: Long latency visual processing underlies perceptual decisions but not reflexive behavior. Journal of Cognitive Neuroscience 23(12), p 3734-3745

Bullying Among Adolescents With Autism Spectrum Disorders: Prevalence and Perception

This study examined: (a) the prevalence of bullying and victimization among adolescents with ASD, (b) whether they correctly perceived bullying and victimization, and (c) whether Theory of Mind (ToM) and bullying involvement were related to this perception. Data were collected among 230 adolescents with ASD attending special education schools. We found prevalence rates of bullying and victimization between 6 and 46%, with teachers reporting significantly higher rates than peers. Furthermore, adolescents who scored high on teacher- and self-reported victimization were more likely to misinterpret non-bullying situations as bullying. The more often adolescents bullied, according to teachers and peers, and the less developed their ToM, the more they misinterpreted bullying situations as non-bullying. Implications for clinical practice are discussed

Parental resilience and the quality of life of children with developmental disabilities in Indonesia

Cultures could influence parents in the way they perceive adverse situations and in how external factors influence resilience, which in turn, may differentially affect the quality of life of a child. The present study aimed to examine the associations between different dimensions of parental resilience and the quality of life of children in Indonesia. The samples consisted of 497 families. This study used the Parenting Resilience Elements and the Quality of Life Questionnaire. Parental resilience consists of three dimensions, knowledge of child’s characteristics, positive perception of parenting, and perceived social support. Knowledge of child’s characteristics, one of the parental resilience dimensions, significantly related to the Quality of Life dimensions of communication and influence, and development. Positive perceptions of parenting related to socio-emotional well-being. Perceived social support related to material well-being, activity, and socio-emotional well-being. We found that the parental resilience related to Quality of Life of children with developmental disabilities. Some findings could be unique for a collectivist culture and highlight the complexities of the association between different factors of parent resilience and Quality of Life of children with developmental disabilities in Indonesia

Implicit scene segmentation in deeper convolutional neural networks

Feedforward deep convolutional neural networks (DCNNs) are matching and even surpassing human performance on object recognition. This performance suggests that activation of a loose collection of image features could support the recognition of natural object categories, without dedicated systems to solve specific visual subtasks. Recent findings in humans however, suggest that while feedforward activity may suffice for sparse scenes with isolated objects, additional visual operations ('routines') that aid the recognition process (e.g. segmentation or grouping) are needed for more complex scenes. Linking human visual processing to performance of DCNNs with increasing depth, we here explored if, how, and when object information is differentiated from the backgrounds they appear on. To this end, we controlled the information in both objects and backgrounds, as well as the relationship between them by adding noise, manipulating background congruence and systematically occluding parts of the image. Results indicated less distinction between object- and background features for more shallow networks. For those networks, we observed a benefit of training on segmented objects (as compared to unsegmented objects). Overall, deeper networks trained on natural (unsegmented) scenes seem to perform implicit 'segmentation' of the objects from their background, possibly by improved selection of relevant features