Prostaglandins and Mucosal Defensive Mechanisms

The first line of mucosa! defence includes the juxtamucosal unstirred layer/pH gradient and the apical surface of the luminal epithelial cells. Many damaging agents, including nonsteroidal anti-inflammatory drugs (NSAIDs), can overwhelm these defences and destroy extensive regions of the luminal epithelium. This damage is readily tolerated in the normal mucosa. Furthermore, a combination of increased mucosal bloodflow, epithelial migration, mucus release, and efflux of bicarbonate- rich fluid usually allows rapid recovery of mucosa I integrity. In the presence of vascular damage and congestion, however, luminal acid can kill mucosal cells and destroy the substrate necessary for repair (by epithelial migration). Damage of this type results in the production of hemorrhagic erosions, which may then develop into chronic ulceroinflammatory disease if healing is prevented by excess luminal acid or by impaired mucosal immune response. Endogenous and exogenous prostaglandins could affect all aspects of the mucosal defensive responses, from the juxtamucosal unstirred layer/pH gradient (via effects on secretion of bicarbonate, acid and mucus, as well as stimulation of fluid efflux) to the function of the mucosal immune system. Protection against the acute damage produced by topically administered NSAIDs or concentrated ethanol can result from either administration of prostaglandins or topical application of'mild irritants'. This is referred to as 'adaptive cytoprotection'. Parenterally administered NSAIDs can also produce mucosa! erosions. Protection against this type of damage may depend on the effects of prostaglandins on neural and contractile elements in the mucosa. Studies on animal models also suggest that by preventing acute hemorrhagic erosions, prostaglandins may prevent the development of chronic ulcer in susceptible individuals

Neuromorphic Silicon Neuron Circuits

Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain–machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin–Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips

Transistor analogs of emergent iono-neuronal dynamics

Neuromorphic analog metal-oxide-silicon (MOS) transistor circuits promise compact, low-power, and high-speed emulations of iono-neuronal dynamics orders-of-magnitude faster than digital simulation. However, their inherently limited input voltage dynamic range vs power consumption and silicon die area tradeoffs makes them highly sensitive to transistor mismatch due to fabrication inaccuracy, device noise, and other nonidealities. This limitation precludes robust analog very-large-scale-integration (aVLSI) circuits implementation of emergent iono-neuronal dynamics computations beyond simple spiking with limited ion channel dynamics. Here we present versatile neuromorphic analog building-block circuits that afford near-maximum voltage dynamic range operating within the low-power MOS transistor weak-inversion regime which is ideal for aVLSI implementation or implantable biomimetic device applications. The fabricated microchip allowed robust realization of dynamic iono-neuronal computations such as coincidence detection of presynaptic spikes or pre- and postsynaptic activities. As a critical performance benchmark, the high-speed and highly interactive iono-neuronal simulation capability on-chip enabled our prompt discovery of a minimal model of chaotic pacemaker bursting, an emergent iono-neuronal behavior of fundamental biological significance which has hitherto defied experimental testing or computational exploration via conventional digital or analog simulations. These compact and power-efficient transistor analogs of emergent iono-neuronal dynamics open new avenues for next-generation neuromorphic, neuroprosthetic, and brain-machine interface applications