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Current advances in neuromorphic engineering have made it possible to emulate complex neuronal ion channel and intracellular ionic dynamics in real time using highly compact and power-efficient complementary metal-oxide-semiconductor (CMOS) analog very-large-scale-integrated circuit technology. Recently, there has been growing interest in the neuromorphic emulation of the spike-timing-dependent plasticity (STDP) Hebbian learning rule by phenomenological modeling using CMOS, memristor or other analog devices. Here, we propose a CMOS circuit implementation of a biophysically grounded neuromorphic (iono-neuromorphic) model of synaptic plasticity that is capable of capturing both the spike rate-dependent plasticity (SRDP, of the Bienenstock-Cooper-Munro or BCM type) and STDP rules. The iono-neuromorphic model reproduces bidirectional synaptic changes with NMDA receptor-dependent and intracellular calcium-mediated long-term potentiation or long-term depression assuming retrograde endocannabinoid signaling as a second coincidence detector. Changes in excitatory or inhibitory synaptic weights are registered and stored in a nonvolatile and compact digital format analogous to the discrete insertion and removal of AMPA or GABA receptor channels. The versatile Hebbian synapse device is applicable to a variety of neuroprosthesis, brain-machine interface, neurorobotics, neuromimetic computation, machine learning, and neural-inspired adaptive control problems.

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Detection and analysis of biological and biochemical signals via compact sensor systems require low-power and compact analog-to-digital converter (ADC) systems. Here we present a highly sensitive flash current-mode ADC (IADC) design with resolution down to 15pA. The IADC's small-size and low-power capabilities allow integration for stand-alone biological or chemical microsensor applications.

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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.

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Neuron-machine interfaces such as dynamic clamp and brain-implantable neuroprosthetic devices require real-time simulations of neuronal ion channel dynamics. Field-programmable gate array (FPGA) has emerged as a high-speed digital platform ideal for such application-specific computations. We propose an efficient and flexible component-based FPGA design framework for neuronal ion channel dynamics simulations, which overcomes certain limitations of the recently proposed memory-based approach. A parallel processing strategy is used to minimize computational delay, and a hardware-efficient factoring approach for calculating exponential and division functions in neuronal ion channel models is used to conserve resource consumption. Performances of the various FPGA design approaches are compared theoretically and experimentally in corresponding implementations of the alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) and N-methyl-D-aspartate (NMDA) synaptic ion channel models. Our results suggest that the component-based design framework provides a more memory economic solution, as well as more efficient logic utilization for large word lengths, whereas the memory-based approach may be suitable for time-critical applications where a higher throughput rate is desired.

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RATIONALE AND OBJECTIVES: Ventilation with high oxygen (O2) concentrations has been shown to decrease T1 in blood and tissues of patients. This study aims to assess the effect of hyperoxygenation on the T1 relaxation time of blood and other physiologic solutions. MATERIALS AND METHODS: Varied gaseous mixtures of O2 and air between 21% and 100% O2 were created using an experimental circuit at room temperature, and used to saturate human blood, plasma, or normal saline. The samples were studied using an 8.45-Tesla magnetic resonance (MR) system and a 1.5-Tesla clinical MR scanner. RESULTS: MR spectroscopy at 8.45 Tesla showed that the percentage of O2 chosen for saturation correlated negatively with T1 (R2 = 1.00 for blood, 0.99 for plasma, and 1.00 for normal saline). The reduction in T1 between solutions saturated with 21% and 100% O2 was 487 milliseconds (22% of the baseline T1 value) for blood, 391 milliseconds (15%) for plasma and 622 milliseconds (19%) for saline. Similarly, MR measurements at 1.5 Tesla showed T1 reduction with increasing O2 concentration. Conclusion. The decreasing T1 in blood depends strongly on the fraction of dissolved O2 in solution and is largely independent of the hemoglobin content.

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