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Synaptic function and experience-dependent plasticity across multiple synapses are dependent on the types of neurons interacting as well as the intricate mechanisms that operate at the molecular level of the synapse. To understand the complexity of information processing at synaptic networks will rely in part on effective computational models. Such models should also evaluate disruptions to synaptic function by multiple mechanisms. By co-development of algorithms alongside hardware, real time analysis metrics can be co-prioritized along with biological complexity. The hippocampus is implicated in autism spectrum disorders (ASD) and within this region glutamatergic neurons constitute 90% of the neurons integral to the functioning of neuronal networks. Here we generate a computational model referred to as ASD interrogator (ASDint) and corresponding hardware to enable in silicon analysis of multiple ASD mechanisms affecting glutamatergic neuron synapses. The hardware architecture Synaptic Neuronal Circuit, SyNC, is a novel GPU accelerator or neural net, that extends discovery by acting as a biologically relevant realistic neuron synapse in real time. Co-developed ASDint and SyNC expand spiking neural network models of plasticity to comparative analysis of retrograde messengers. The SyNC model is realized in an ASIC architecture, which enables the ability to compute increasingly complex scenarios without sacrificing area efficiency of the model. Here we apply the ASDint model to analyse neuronal circuitry dysfunctions associated with autism spectral disorder (ASD) synaptopathies and their effects on the synaptic learning parameter and demonstrate SyNC on an ideal ASDint scenario. Our work highlights the value of secondary pathways in regard to evaluating complex ASD synaptopathy mechanisms. By comparing the degree of variation in the synaptic learning parameter to the response obtained from simulations of the ideal scenario we determine the potency and time of the effect of a particular evaluated mechanism. Hence simulations of such scenarios in even a small neuronal network now allows us to identify relative impacts of changed parameters and their effect on synaptic function. Based on this, we can estimate the minimum fraction of a neuron exhibiting a particular dysfunction scenario required to lead to complete failure of a neural network to coordinate pre-synaptic and post-synaptic outputs.

Neurodevelopment, plasticity, and cognition are integral with functional directional transport in neuronal axons that occurs along a unique network of discontinuous polar microtubule (MT) bundles. Axonopathies are caused by brain trauma and genetic diseases that perturb or disrupt the axon MT infrastructure and, with it, the dynamic interplay of motor proteins and cargo essential for axonal maintenance and neuronal signaling. The inability to visualize and quantify normal and altered nanoscale spatio-temporal dynamic transport events prevents a full mechanistic understanding of injury, disease progression, and recovery. To address this gap, we generated DyNAMO, a Dynamic Nanoscale Axonal MT Organization model, which is a biologically realistic theoretical axon framework. We use DyNAMO to experimentally simulate multi-kinesin traffic response to focused or distributed tractable injury parameters, which are MT network perturbations affecting MT lengths and multi-MT staggering. We track kinesins with different motility and processivity, as well as their influx rates, in-transit dissociation and reassociation from inter-MT reservoirs, progression, and quantify and spatially represent motor output ratios. DyNAMO demonstrates, in detail, the complex interplay of mixed motor types, crowding, kinesin off/on dissociation and reassociation, and injury consequences of forced intermingling. Stalled forward progression with different injury states is seen as persistent dynamicity of kinesins transiting between MTs and inter-MT reservoirs. DyNAMO analysis provides novel insights and quantification of axonal injury scenarios, including local injury-affected ATP levels, as well as relates these to influences on signaling outputs, including patterns of gating, waves, and pattern switching. The DyNAMO model significantly expands the network of heuristic and mathematical analysis of neuronal functions relevant to axonopathies, diagnostics, and treatment strategies.