Hardware implementation of backpropagation using progressive gradient descent for in situ training of multilayer neural networks.

Neural network training can be slow and energy-expensive due to the frequent transfer of weight data between digital memory and processing units. Neuromorphic systems can accelerate neural networks by performing multiply-accumulate operations in parallel using nonvolatile analog memory. However, executing the widely used backpropagation training algorithm in multilayer neural networks requires information-and therefore storage-of the partial derivatives of the weight values preventing suitable and scalable implementation in hardware.

Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics.

Biological systems interact directly with the environment and learn by receiving multimodal feedback via sensory stimuli that shape the formation of internal neuronal representations. Drawing inspiration from biological concepts such as exploration and sensory processing that eventually lead to behavioral conditioning, we present a robotic system handling objects through multimodal learning. A small-scale organic neuromorphic circuit locally integrates and adaptively processes multimodal sensory stimuli, enabling the robot to interact intelligently with its surroundings.

Over 60 h of Stable Water-Operation for N-Type Organic Electrochemical Transistors with Fast Response and Ambipolarity.

Organic electrochemical transistors (OECTs) are of great interest in low-power bioelectronics and neuromorphic computing, as they utilize organic mixed ionic-electronic conductors (OMIECs) to transduce ionic signals into electrical signals. However, the poor environmental stability of OMIEC materials significantly restricts the practical application of OECTs. Therefore, the non-fused planar naphthalenediimide (NDI)-dialkoxybithiazole (2Tz) copolymers are fine-tuned through varying ethylene glycol (EG) side chain lengths from tri(ethylene glycol) to hexa(ethylene glycol) (namely P-XO, X = 3-6) to achieve OECTs with high-stability and low threshold voltage.

Predicting Blood Glucose Levels with Organic Neuromorphic Micro-Networks.

Accurate glucose prediction is vital for diabetes management. Artificial intelligence and artificial neural networks (ANNs) are showing promising results for reliable glucose predictions, offering timely warnings for glucose fluctuations. The translation of these software-based ANNs into dedicated computing hardware opens a route toward automated insulin delivery systems ultimately enhancing the quality of life for diabetic patients.

A modular organic neuromorphic spiking circuit for retina-inspired sensory coding and neurotransmitter-mediated neural pathways.

Signal communication mechanisms within the human body rely on the transmission and modulation of action potentials. Replicating the interdependent functions of receptors, neurons and synapses with organic artificial neurons and biohybrid synapses is an essential first step towards merging neuromorphic circuits and biological systems, crucial for computing at the biological interface. However, most organic neuromorphic systems are based on simple circuits which exhibit limited adaptability to both external and internal biological cues, and are restricted to emulate only specific the functions of an individual neuron/synapse.

Adaptive Biosensing and Neuromorphic Classification Based on an Ambipolar Organic Mixed Ionic-Electronic Conductor.

Organic mixed ionic-electronic conductors (OMIECs) are central to bioelectronic applications such as biosensors, health-monitoring devices, and neural interfaces, and have facilitated efficient next-generation brain-inspired computing and biohybrid systems. Despite these examples, smart and adaptive circuits that can locally process and optimize biosignals have not yet been realized. Here, a tunable sensing circuit is shown that can locally modulate biologically relevant signals like electromyograms (EMGs) and electrocardiograms (ECGs), that is based on a complementary logic inverter combined with a neuromorphic memory element, and that is constructed from a single polymer mixed conductor.

Organic neuromorphic electronics for sensorimotor integration and learning in robotics.

In living organisms, sensory and motor processes are distributed, locally merged, and capable of forming dynamic sensorimotor associations. We introduce a simple and efficient organic neuromorphic circuit for local sensorimotor merging and processing on a robot that is placed in a maze. While the robot is exposed to external environmental stimuli, visuomotor associations are formed on the adaptable neuromorphic circuit.

Enhanced Microfluidic Sample Homogeneity and Improved Antibody-Based Assay Kinetics Due to Magnetic Mixing.

Recent global events have distinctly demonstrated the need for fast diagnostic analysis of targets in a liquid sample. However, microfluidic lab-on-a-chip devices for point-of-care diagnostics can suffer from slow analysis due to poor mixing. Here, we experimentally explore the mixing effect within a microfluidic chamber, as obtained from superparamagnetic beads exposed to an out-of-plane (vertical) rotating magnetic field.

A biohybrid synapse with neurotransmitter-mediated plasticity.

Brain-inspired computing paradigms have led to substantial advances in the automation of visual and linguistic tasks by emulating the distributed information processing of biological systems. The similarity between artificial neural networks (ANNs) and biological systems has inspired ANN implementation in biomedical interfaces including prosthetics and brain-machine interfaces. While promising, these implementations rely on software to run ANN algorithms.

Enhancement-Mode PEDOT:PSS Organic Electrochemical Transistors Using Molecular De-Doping.

Organic electrochemical transistors (OECTs) show great promise for flexible, low-cost, and low-voltage sensors for aqueous solutions. The majority of OECT devices are made using the polymer blend poly(ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS), in which PEDOT is intrinsically doped due to inclusion of PSS. Because of this intrinsic doping, PEDOT:PSS OECTs generally operate in depletion mode, which results in a higher power consumption and limits stability.

Microfluidic Magnetic Mixing at Low Reynolds Numbers and in Stagnant Fluids.

Microfluidic mixing becomes a necessity when thorough sample homogenization is required in small volumes of fluid, such as in lab-on-a-chip devices. For example, efficient mixing is extraordinarily challenging in capillary-filling microfluidic devices and in microchambers with stagnant fluids. To address this issue, specifically designed geometrical features can enhance the effect of diffusion and provide efficient mixing by inducing chaotic fluid flow.

The Mechanism of Dedoping PEDOT:PSS by Aliphatic Polyamines.

Poly(3,4-ethylenedioxythiophene) blended with polystyrenesulfonate and poly(styrenesulfonic acid), PEDOT:PSS, has found widespread use in organic electronics. Although PEDOT:PSS is commonly used in its doped electrically conducting state, the ability to efficiently convert PEDOT:PSS to its undoped nonconducting state is of interest for a wide variety of applications ranging from biosensors to organic neuromorphic devices. Exposure to aliphatic monoamines, acting as an electron donor and Brønsted-Lowry base, has been reported to be partly successful, but monoamines are unable to fully dedope PEDOT:PSS.

Redefining near-unity luminescence in quantum dots with photothermal threshold quantum yield.

A variety of optical applications rely on the absorption and reemission of light. The quantum yield of this process often plays an essential role. When the quantum yield deviates from unity by significantly less than 1%, applications such as luminescent concentrators and optical refrigerators become possible.

Enhanced Cell-Chip Coupling by Rapid Femtosecond Laser Patterning of Soft PEDOT:PSS Biointerfaces.

Interfacing soft materials with biological systems holds considerable promise for both biosensors and recording live cells. However, the interface between cells and organic substrates is not well studied, despite its crucial role in the effectiveness of the device. Furthermore, well-known cell adhesion enhancers, such as microgrooves, have not been implemented on these surfaces.

Revealing the Cell-Material Interface with Nanometer Resolution by Focused Ion Beam/Scanning Electron Microscopy.

The interface between cells and nonbiological surfaces regulates cell attachment, chronic tissue responses, and ultimately the success of medical implants or biosensors. Clinical and laboratory studies show that topological features of the surface profoundly influence cellular responses; for example, titanium surfaces with nano- and microtopographical structures enhance osteoblast attachment and host-implant integration as compared to a smooth surface. To understand how cells and tissues respond to different topographical features, it is of critical importance to directly visualize the cell-material interface at the relevant nanometer length scale.

A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing.

The brain is capable of massively parallel information processing while consuming only ∼1-100 fJ per synaptic event. Inspired by the efficiency of the brain, CMOS-based neural architectures and memristors are being developed for pattern recognition and machine learning. However, the volatility, design complexity and high supply voltages for CMOS architectures, and the stochastic and energy-costly switching of memristors complicate the path to achieve the interconnectivity, information density, and energy efficiency of the brain using either approach.

In situ monitoring of single-wall carbon nanotube laser assisted growth.

Laser assisted catalytic chemical vapor deposition has recently emerged as an attractive method for locally growing carbon nanotubes (CNTs) in a cold wall reactor. So far, reported laser assisted CNT growth has been carried out without in situ process monitoring. This has made it difficult to control the growth process and limits the applicability of the method.