A Model for R(t) Elements and R(t) -Based Spike-Timing-Dependent Plasticity With Basic Circuit Examples.

Spike-timing-dependent plasticity (STDP) is a fundamental synaptic learning rule observed in biology that leads to numerous behavioral and cognitive outcomes. Emulating STDP in electronic spiking neural networks with high-density memristive synapses is, therefore, of significant interest. While one popular method involves pulse-shaping the spiking neuron output voltages, an alternative approach is outlined in this article.

Graphene Foam as a three-dimensional Platform for Myotube Growth.

This study demonstrates the growth and differentiation of C2C12 myoblasts into functional myotubes on 3-dimensional graphene foam bioscaffolds. Specifically, we establish both bare and laminin coated graphene foam as a biocompatible platform for muscle cells and identify that electrical coupling stimulates cell activity. Cell differentiation and functionality is determined by the expression of myotube heavy chain protein and Ca fluorescence, respectively.

Low-temperature fabrication of spiking soma circuits using nanocrystalline-silicon TFTs.

Spiking neuron circuits consisting of ambipolar nanocrystalline-silicon (nc-Si) thin-film transistors (TFTs) have been fabricated using low temperature processing conditions (maximum of 250 °C) that allow the use of flexible substrates. These circuits display behaviors commonly observed in biological neurons such as millisecond spike duration, nonlinear frequency-current relationship, and spike frequency adaptation. The maximum drive capacity of a simple soma circuit was estimated to be approximately 9200 synapses.

Neural learning circuits utilizing nano-crystalline silicon transistors and memristors.

Properties of neural circuits are demonstrated via SPICE simulations and their applications are discussed. The neuron and synapse subcircuits include ambipolar nano-crystalline silicon transistor and memristor device models based on measured data. Neuron circuit characteristics and the Hebbian synaptic learning rule are shown to be similar to biology.