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.

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.

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.