Neuromorphic encoding of tactile stimuli to provide naturalistic sensory feedback in upper limb prostheses

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PROJECT SUMMARY This research project is motivated by the goal of improving the lives of amputees through naturalistic sensory feedback of tactile stimuli. Today, prostheses rely on decoding user intention through measurement of neural or electromyographic (EMG) signals. The full potential of these sophisticated robotic devices cannot be realized without the incorporation of sensors that evaluate the environment and a way to seamlessly communicate with the user. Neural prostheses can enable this seamless communication by interfacing directly with the nervous system of amputees and stimulating the nerves in order to elicit sensations corresponding to the interaction between the prosthesis and the environment. To do this naturalistically, the analog readings from sensors incorporated into the prosthesis must be encoded into the language of the nervous system: patterns of spiking activity. My goal is to improve sensory feedback for amputees by exploring how information from tactile sensors can be transformed into neuron-like (neuromorphic) spikes to be used for stimulation feedback. I will examine how tactile sensing is encoded in biology and then phenomenologically recreate the signal processing chain using a computational model that will be tested with a real-world texture dataset. The output of these models will be classified to verify and quantify the successful encoding of texture information as neuromorphic spiking activity. Texture serves as a good test case to develop these models because of its rich spatiotemporal structure. Specific Aim 1 – Neuromorphic Encoding and Processing of Tactile Stimuli – I will use the Izhikevich neuron model to recreate the spiking activity of SA and RA mechanoreceptors in response to texture stimuli applied to a tactile sensing array. I will develop new algorithms to transform the spiking patterns to account for scanning speed and applied force. This will result in a speed- and force-invariant representation of texture. Specific Aim 2 – Neuromorphic Compression of Tactile Information – Initially, a naïve channel selection algorithm that uses spike train distance to evaluate mutual information between different input channels will compress tactile information to select an optimal set of sensing channels to pass through to stimulation. A more advanced scheme will combine inputs together for more efficient information encoding and to enrich the information content of the final output spiking patterns. Artificial texture classification will be used to evaluate the capability of these methods to efficiently retain relevant texture information. Fundamentally, Aim 1 focuses on robust representations of tactile stimuli independent of exploratory conditions, while Aim 2 focuses on efficient representations of those stimuli. When completed, this work will provide the basis for more naturalistic sensory feedback to amputees through peripheral nerve stimulation which will result in better functional outcomes when using prostheses in their daily lives.