A Non-spiking Neuron Model With Dynamic Leak to Avoid Instability in Recurrent Networks.

Recurrent circuitry components are distributed widely within the brain, including both excitatory and inhibitory synaptic connections. Recurrent neuronal networks have potential stability problems, perhaps a predisposition to epilepsy. More generally, instability risks making internal representations of information unreliable.

Cuneate spiking neural network learning to classify naturalistic texture stimuli under varying sensing conditions.

We implemented a functional neuronal network that was able to learn and discriminate haptic features from biomimetic tactile sensor inputs using a two-layer spiking neuron model and homeostatic synaptic learning mechanism. The first order neuron model was used to emulate biological tactile afferents and the second order neuron model was used to emulate biological cuneate neurons. We have evaluated 10 naturalistic textures using a passive touch protocol, under varying sensing conditions.

Tactile Decoding of Edge Orientation With Artificial Cuneate Neurons in Dynamic Conditions.

Generalization ability in tactile sensing for robotic manipulation is a prerequisite to effectively perform tasks in ever-changing environments. In particular, performing dynamic tactile perception is currently beyond the ability of robotic devices. A biomimetic approach to achieve this dexterity is to develop machines combining compliant robotic manipulators with neuroinspired architectures displaying computational adaptation.

Intracellular Dynamics in Cuneate Nucleus Neurons Support Self-Stabilizing Learning of Generalizable Tactile Representations.

How the brain represents the external world is an unresolved issue for neuroscience, which could provide fundamental insights into brain circuitry operation and solutions for artificial intelligence and robotics. The neurons of the cuneate nucleus form the first interface for the sense of touch in the brain. They were previously shown to have a highly skewed synaptic weight distribution for tactile primary afferent inputs, suggesting that their connectivity is strongly shaped by learning.

Neuromorphic Artificial Touch for Categorization of Naturalistic Textures.

We implemented neuromorphic artificial touch and emulated the firing behavior of mechanoreceptors by injecting the raw outputs of a biomimetic tactile sensor into an Izhikevich neuronal model. Naturalistic textures were evaluated with a passive touch protocol. The resulting neuromorphic spike trains were able to classify ten naturalistic textures ranging from textiles to glass to BioSkin, with accuracy as high as 97%.

Piezoelectric energy harvesting solutions.

This paper reviews the state of the art in piezoelectric energy harvesting. It presents the basics of piezoelectricity and discusses materials choice. The work places emphasis on material operating modes and device configurations, from resonant to non-resonant devices and also to rotational solutions.