Response time variability and response inhibition predict affective problems in adolescent girls, not in boys: the TRAILS study

The present study examines the relationship between neurocognitive functioning and affective problems through adolescence, in a cross-sectional and longitudinal perspective. Baseline response speed, response speed variability, response inhibition, attentional flexibility and working memory were assessed in a cohort of 2,179 adolescents (age 10–12 years) from the TRacking Adolescents’ Individual Lives Survey (TRAILS). Affective problems were measured with the DSM-oriented Affective Problems scale of the Youth Self Report at wave 1 (baseline assessment), wave 2 (after 2.5 years) and wave 3 (after 5 years). Cross-sectionally, baseline response speed, response time variability, response inhibition and working memory were associated with baseline affective problems in girls, but not in boys. Longitudinally, enhanced response time variability predicted affective problems after 2.5 and 5 years in girls, but not in boys. Decreased response inhibition predicted affective problems after 5 years follow-up in girls, and again not in boys. The results are discussed in light of recent insights in gender differences in adolescence and state–trait issues in depression

Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model's feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison

Electroweak parameters of the z0 resonance and the standard model

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