Optimal stimulation protocol in a bistable synaptic consolidation model

Consolidation of synaptic changes in response to neural activity is thought to be fundamental for memory maintenance over a timescale of hours. In experiments, synaptic consolidation can be induced by repeatedly stimulating presynaptic neurons. However, the effectiveness of such protocols depends crucially on the repetition frequency of the stimulations and the mechanisms that cause this complex dependence are unknown. Here we propose a simple mathematical model that allows us to systematically study the interaction between the stimulation protocol and synaptic consolidation. We show the existence of optimal stimulation protocols for our model and, similarly to LTP experiments, the repetition frequency of the stimulation plays a crucial role in achieving consolidation. Our results show that the complex dependence of LTP on the stimulation frequency emerges naturally from a model which satisfies only minimal bistability requirements.Comment: 23 pages, 6 figure

Molecular Biomarkers Predictive of Sertraline Treatment Response in Young Children With Autism Spectrum Disorder.

Sertraline is one among several selective serotonin reuptake inhibitors (SSRIs) that exhibited improvement of language development in Autism Spectrum Disorder (ASD); however, the molecular mechanism has not been elucidated. A double blind, randomized, 6-month, placebo-controlled, clinical trial of low-dose sertraline in children ages (3-6 years) with ASD was conducted at the UC Davis MIND Institute. It aimed at evaluating the efficacy and benefit with respect to early expressive language development and global clinical improvement. This study aimed to identify molecular biomarkers that might be key players in the serotonin pathway and might be predictive of a clinical response to sertraline. Fifty eight subjects with the diagnosis of ASD were randomized to sertraline or placebo. Eight subjects from the sertraline arm and five from the placebo arm discontinued from the study. Furthermore, four subjects did not have a successful blood draw. Hence, genotypes for 41 subjects (20 on placebo and 21 on sertraline) were determined for several genes involved in the serotonin pathway including the serotonin transporter-linked polymorphic region (5-HTTLPR), the tryptophan hydroxylase 2 (TPH2), and the Brain-Derived Neurotrophic Factor (BDNF). In addition, plasma levels of BDNF, Matrix metallopeptidase 9 (MMP-9) and a selected panel of cytokines were determined at baseline and post-treatment. Intent-to-treat analysis revealed several primary significant correlations between molecular changes and the Mullen Scales of Early Learning (MSEL) and Clinical Global Impression Scale - Improvement (CGI-I) of treatment and control groups but they were not significant after adjustment for multiple testing. Thus, sertraline showed no benefit for treatment of young children with ASD in language development or changes in molecular markers in this study. These results indicate that sertraline may not be beneficial for the treatment of children with ASD; however, further investigation of larger groups as well as longer term follow-up studies are warranted

Controlled trial of lovastatin combined with an open-label treatment of a parent-implemented language intervention in youth with fragile X syndrome.

BackgroundThe purpose of this study was to conduct a 20-week controlled trial of lovastatin (10 to 40 mg/day) in youth with fragile X syndrome (FXS) ages 10 to 17 years, combined with an open-label treatment of a parent-implemented language intervention (PILI), delivered via distance video teleconferencing to both treatment groups, lovastatin and placebo.MethodA randomized, double-blind trial was conducted at one site in the Sacramento, California, metropolitan area. Fourteen participants were assigned to the lovastatin group; two participants terminated early from the study. Sixteen participants were assigned to the placebo group. Lovastatin or placebo was administered orally in a capsule form, starting at 10 mg and increasing weekly or as tolerated by 10 mg increments, up to a maximum dose of 40 mg daily. A PILI was delivered to both groups for 12 weeks, with 4 activities per week, through video teleconferencing by an American Speech-Language Association-certified Speech-Language Pathologist, in collaboration with a Board-Certified Behavior Analyst. Parents were taught to use a set of language facilitation strategies while interacting with their children during a shared storytelling activity. The main outcome measures included absolute change from baseline to final visit in the means for youth total number of story-related utterances, youth number of different word roots, and parent total number of story-related utterances.ResultsSignificant increases in all primary outcome measures were observed in both treatment groups. Significant improvements were also observed in parent reports of the severity of spoken language and social impairments in both treatment groups. In all cases, the amount of change observed did not differ across the two treatment groups. Although gains in parental use of the PILI-targeted intervention strategies were observed in both treatment groups, parental use of the PILI strategies was correlated with youth gains in the placebo group and not in the lovastatin group.ConclusionParticipants in both groups demonstrated significant changes in the primary outcome measures. The magnitude of change observed across the two groups was comparable, providing additional support for the efficacy of the use of PILI in youth with FXS.Trial registrationUS National Institutes of Health (ClinicalTrials.gov), NCT02642653. Registered 12/30/2015

A Randomized Controlled Trial of Sertraline in Young Children With Autism Spectrum Disorder.

Objective: Selective serotonin reuptake inhibitors like sertraline have been shown in observational studies and anecdotal reports to improve language development in young children with fragile X syndrome (FXS). A previous controlled trial of sertraline in young children with FXS found significant improvement in expressive language development as measured by the Mullen Scales of Early Learning (MSEL) among those with comorbid autism spectrum disorder (ASD) in post hoc analysis, prompting the authors to probe whether sertraline is also indicated in nonsyndromic ASD. Methods: The authors evaluated the efficacy of 6 months of treatment with low-dose sertraline in a randomized, double-blind, placebo-controlled trial in 58 children with ASD aged 24 to 72 months. Results: 179 subjects were screened for eligibility, and 58 were randomized to sertraline (32) or placebo (26). Eight subjects from the sertraline arm and five from the placebo arm discontinued. Intent-to-treat analysis showed no significant difference from placebo on the primary outcomes (MSEL expressive language raw score and age equivalent combined score) or secondary outcomes. Sertraline was well tolerated, with no difference in side effects between sertraline and placebo groups. No serious adverse events possibly related to study treatment occurred. Conclusion: This randomized controlled trial of sertraline treatment showed no benefit with respect to primary or secondary outcome measures. For the 6-month period, treatment in young children with ASD appears safe, although the long-term side effects of low-dose sertraline in early childhood are unknown. Clinical Trial Registration: www.ClinicalTrials.gov, identifier NCT02385799

Training Probabilistic Spiking Neural Networks with First-to-spike Decoding

Third-generation neural networks, or Spiking Neural Networks (SNNs), aim at harnessing the energy efficiency of spike-domain processing by building on computing elements that operate on, and exchange, spikes. In this paper, the problem of training a two-layer SNN is studied for the purpose of classification, under a Generalized Linear Model (GLM) probabilistic neural model that was previously considered within the computational neuroscience literature. Conventional classification rules for SNNs operate offline based on the number of output spikes at each output neuron. In contrast, a novel training method is proposed here for a first-to-spike decoding rule, whereby the SNN can perform an early classification decision once spike firing is detected at an output neuron. Numerical results bring insights into the optimal parameter selection for the GLM neuron and on the accuracy-complexity trade-off performance of conventional and first-to-spike decoding.Comment: A shorter version will be published on Proc. IEEE ICASSP 201

Simulation of networks of spiking neurons: A review of tools and strategies

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of Computational Neuroscience, in press (2007

Creativity and Movement Maintain Synaptic Activity, Improving QOL in Older Adults: A Critical Review

People are living longer. Hence, the global population of older adults is increasing. Likewise, the population of individuals living with Alzheimer’s disease, other forms of dementia, and general cognitive decline is also growing and is expected to double within the next ten years. This literature review examines the effects of exercise, movement, and creative cognition seeking a positive connection to improvement in an individual’s brain function, cognitive abilities, and synaptic plasticity while focusing on their relation to memory recall abilities. It is suggested that exercise and movement increases a chemical within the brain that is involved with memory recall and increased synaptic firing. Additionally, creative cognition utilizes multiple networks within the brain indicating greater opportunities for boosting cognitive abilities. One of these systems is directly involved in the storage and retrieval of episodic memories. Creative thinking has been found to improve the coping, adaptability, and flexibility of older adults’ everyday problem-solving skills; thereby, implying it elevates quality of life. Dance/movement therapy combines creative cognition and movement, as well as treats the whole person. Therefore, through neurological, physiological, and psychological lenses, dance/movement therapy is presented as a beneficial and all-encompassing intervention to use with older adults to improve their recall abilities, engage their working memory, maintain synaptic plasticity, and increase quality of life

Toward the language oscillogenome

Language has been argued to arise, both ontogenetically and phylogenetically, from specific patterns of brain wiring. We argue that it can further be shown that core features of language processing emerge from particular phasal and cross-frequency coupling properties of neural oscillations; what has been referred to as the language 'oscillome.' It is expected that basic aspects of the language oscillome result from genetic guidance, what we will here call the language 'oscillogenome,' for which we will put forward a list of candidate genes. We have considered genes for altered brain rhythmicity in conditions involving language deficits: autism spectrum disorders, schizophrenia, specific language impairment and dyslexia. These selected genes map on to aspects of brain function, particularly on to neurotransmitter function. We stress that caution should be adopted in the construction of any oscillogenome, given the range of potential roles particular localized frequency bands have in cognition. Our aim is to propose a set of genome-to-language linking hypotheses that, given testing, would grant explanatory power to brain rhythms with respect to language processing and evolution.Economic and Social Research Council scholarship 1474910Ministerio de Economía y Competitividad (España) FFI2016-78034-C2-2-

The ELM Neuron: an Efficient and Expressive Cortical Neuron Model Can Solve Long-Horizon Tasks

Traditional large-scale neuroscience models and machine learning utilize simplified models of individual neurons, relying on collective activity and properly adjusted connections to perform complex computations. However, each biological cortical neuron is inherently a sophisticated computational device, as corroborated in a recent study where it took a deep artificial neural network with millions of parameters to replicate the input-output relationship of a detailed biophysical model of a cortical pyramidal neuron. We question the necessity for these many parameters and introduce the Expressive Leaky Memory (ELM) neuron, a biologically inspired, computationally expressive, yet efficient model of a cortical neuron. Remarkably, our ELM neuron requires only 8K trainable parameters to match the aforementioned input-output relationship accurately. We find that an accurate model necessitates multiple memory-like hidden states and intricate nonlinear synaptic integration. To assess the computational ramifications of this design, we evaluate the ELM neuron on various tasks with demanding temporal structures, including a sequential version of the CIFAR-10 classification task, the challenging Pathfinder-X task, and a new dataset based on the Spiking Heidelberg Digits dataset. Our ELM neuron outperforms most transformer-based models on the Pathfinder-X task with 77% accuracy, demonstrates competitive performance on Sequential CIFAR-10, and superior performance compared to classic LSTM models on the variant of the Spiking Heidelberg Digits dataset. These findings indicate a potential for biologically motivated, computationally efficient neuronal models to enhance performance in challenging machine learning tasks.Comment: 23 pages, 10 figures, 9 tables, submitted to NeurIPS 202