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. The reviewed literature is compared based on power density and bandwidth. Lastly, the question of power conversion is addressed by reviewing various circuit solutions.

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%. Remarkably, rather than on firing rate features calculated over the stimulation window, the highest achieved decoding performance was based on the precise spike timing of the neuromorphic output as captured by Victor Purpura distance. We also systematically varied the sliding velocity and the contact force to investigate the role of sensing conditions in categorizing the stimuli via the artificial sensory system. We found that the decoding performance based on the timing of neuromorphic spike events was robust for a broad range of sensing conditions. Being able to categorize naturalistic textures in different sensing conditions, these neurorobotic results pave the way to the use of neuromorphic tactile sensors in future real-life neuroprosthetic applications.

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. Here we first characterized the intracellular dynamics and inhibitory synaptic inputs of cuneate neurons in vivo and modeled their integration of tactile sensory inputs. We then replaced the tactile inputs with input from a sensorized bionic fingertip and modeled the learning-induced representations that emerged from varied sensory experiences. The model reproduced both the intrinsic membrane dynamics and the synaptic weight distributions observed in cuneate neurons in vivo. In terms of higher level model properties, individual cuneate neurons learnt to identify specific sets of correlated sensors, which at the population level resulted in a decomposition of the sensor space into its recurring high-dimensional components. Such vector components could be applied to identify both past and novel sensory experiences and likely correspond to the fundamental haptic input features these neurons encode in vivo. In addition, we show that the cuneate learning architecture is robust to a wide range of intrinsic parameter settings due to the neuronal intrinsic dynamics. Therefore, the architecture is a potentially generic solution for forming versatile representations of the external world in different sensor systems.

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