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Hardware has become more prone to faults as a result of geometric scaling, wear‐out and faults caused during the manufacturing process, therefore, the reliability of hardware is reliant on the need to continually adapt to faults. A computational model of biological self‐repair in the brain, derived from observing the role of astrocytes (a glial cell found in the mammalian brain), has captured self‐repair within models of neural networks known as neuro‐glia networks. This astrocyte‐driven repair process can address the issues of faulty synapse connections between neurons. These astrocyte cells are distributed throughout a neuro‐glia network and regulate synaptic activity, and it has been observed in computational models that this can result in a fine‐grained self‐repair process. Therefore, mapping neuro‐glia networks to hardware provides a strategy for achieving self‐repair in hardware. The internal interconnecting of these networks in hardware is a challenge. Previous work has focused on addressing neuron to astrocyte communication (local), however, the global self‐repair process is dependent on the communication infrastructure between astrocyte‐to‐astrocyte; e.g. astrocyte network. This study addresses the key challenge of providing a scalable communication interconnect for global astrocyte network requirements and how it integrates with existing local communication mechanism. Area/power results demonstrate scalable implementations with the ring topology while meeting timing requirements. [ABSTRACT FROM AUTHOR]

In recent years research suggests that astrocyte networks, in addition to nutrient and waste processing functions, regulate both structural and synaptic plasticity. To understand the biological mechanisms that underpin such plasticity requires the development of cell level models that capture the mutual interaction between astrocytes and neurons. This paper presents a detailed model of bidirectional signaling between astrocytes and neurons (the astrocyte-neuron model or AN model) which yields new insights into the computational role of astrocyte-neuronal coupling. From a set of modeling studies we demonstrate two significant findings. Firstly, that spatial signaling via astrocytes can relay a ''learning signal'' to remote synaptic sites. Results show that slow inward currents cause synchronized postsynaptic activity in remote neurons and subsequently allow Spike-Timing-Dependent Plasticity based learning to occur at the associated synapses. Secondly, that bidirectional communication between neurons and astrocytes underpins dynamic coordination between neuron clusters. Although our composite AN model is presently applied to simplified neural structures and limited to coordination between localized neurons, the principle (which embodies structural, functional and dynamic complexity), and the modeling strategy may be extended to coordination among remote neuron clusters. [ABSTRACT FROM AUTHOR]

This paper presents a synaptic weight association training (SWAT) algorithm for spiking neural networks (SNNs). SWAT merges the Bienenstock–Cooper—Munro (BCM) learning rule with spike timing dependent plasticity (STDP). The STDP/BCM rule yields a unimodal weight distribution where the height of the plasticity window associated with STDP is modulated causing stability after a period of training. The SNN uses a single training neuron in the training phase where data associated with all classes is passed to this neuron. The rule then maps weights to the classifying output neurons to reflect similarities in the data across the classes. The SNN also includes both excitatory and inhibitory facilitating synapses which create a frequency routing capability allowing the information presented to the network to be routed to different hidden layer neurons. A variable neuron threshold level simulates the refractory period. SWAT is initially benchmarked against the nonlinearly separable Iris and Wisconsin Breast Cancer datasets. Results presented show that the proposed training algorithm exhibits a convergence accuracy of 95.5% and 96.2% for the Iris and Wisconsin training sets, respectively, and 95.3% and 96.7% for the testing sets, noise experiments show that SWAT has a good generalization capability. SWAT is also benchmarked using an isolated digit automatic speech recognition (ASR) system where a subset of the TI46 speech corpus is used. Results show that with SWAT as the classifier, the ASR system provides an accuracy of 98.875% for training and 95.25% for testing. [ABSTRACT FROM PUBLISHER]

The article discusses various papers published in this issue including one by Gordleeva et al. on the physiological functions of tripartite synapses and astrocytic regulation, one by Sushmita et al. on glial transporters modulate synaptic transmission, and one by LeMeur et al. on the use of whole-cell recordings in rat acute brain slices.