A Digital Hardware System for Spiking Network of Tactile Afferents

In the present research, we explore the possibility of utilizing a hardware-based neuromorphic approach to develop a tactile sensory system at the level of first-order afferents, which are slowly adapting type 1 (SA-I) and fast adapting type 1 (FA-I) afferents. Four spiking models are used to mimic neural signals of both SA-I and FA-I primary afferents. Next, a digital circuit is designed for each spiking model for both afferents to be implemented on the field-programmable gate array (FPGA). The four different digital circuits are then compared from source utilization point of view to find the minimum cost circuit for creating a population of digital afferents. In this way, the firing responses of both SA-I and FA-I afferents are physically measured in hardware. Finally, a population of 243 afferents consisting of 90 SA-I and 153 FA-I digital neuromorphic circuits are implemented on the FPGA. The FPGA also receives nine inputs from the force sensors through an interfacing board. Therefore, the data of multiple inputs are processed by the spiking network of tactile afferents, simultaneously. Benefiting from parallel processing capabilities of FPGA, the proposed architecture offers a low-cost neuromorphic structure for tactile information processing. Applying machine learning algorithms on the artificial spiking patterns collected from FPGA, we successfully classified three different objects based on the firing rate paradigm. Consequently, the proposed neuromorphic system provides the opportunity for development of new tactile processing component for robotic and prosthetic applications.

[1]  N. V. Thakor,et al.  Bio-mimetic strategies for tactile sensing , 2013, 2013 IEEE SENSORS.

[2]  Amir Zjajo,et al.  A Real-Time Reconfigurable Multichip Architecture for Large-Scale Biophysically Accurate Neuron Simulation , 2018, IEEE Transactions on Biomedical Circuits and Systems.

[3]  Nicola Vitiello,et al.  Synthetic and Bio-Artificial Tactile Sensing: A Review , 2013, Sensors.

[4]  Giulio Sandini,et al.  Tactile Sensing—From Humans to Humanoids , 2010, IEEE Transactions on Robotics.

[5]  Andrew S. Cassidy,et al.  Design of a one million neuron single FPGA neuromorphic system for real-time multimodal scene analysis , 2011, 2011 45th Annual Conference on Information Sciences and Systems.

[6]  Alberto Mazzoni,et al.  Neuromorphic Artificial Touch for Categorization of Naturalistic Textures , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[7]  Zhengkun Yi,et al.  Bio-inspired tactile FA-I spiking generation under sinusoidal stimuli , 2016 .

[8]  P. Rossini,et al.  Intraneural stimulation elicits discrimination of textural features by artificial fingertip in intact and amputee humans , 2016, eLife.

[9]  Gordon Cheng,et al.  Directions Toward Effective Utilization of Tactile Skin: A Review , 2013, IEEE Sensors Journal.

[10]  V. Hayward,et al.  Segregation of Tactile Input Features in Neurons of the Cuneate Nucleus , 2014, Neuron.

[11]  Silvestro Micera,et al.  Artificial spatiotemporal touch inputs reveal complementary decoding in neocortical neurons , 2017, Scientific Reports.

[12]  Nitish V. Thakor,et al.  Prosthesis with neuromorphic multilayered e-dermis perceives touch and pain , 2018, Science Robotics.

[13]  Ravinder Dahiya,et al.  Robotic Tactile Sensing: Technologies and System , 2012 .

[14]  J. M. Ritchie,et al.  A quantitative description of membrane currents in rabbit myelinated nerve. , 1979, The Journal of physiology.

[15]  Angelika Peer,et al.  Human-Inspired Neurorobotic System for Classifying Surface Textures by Touch , 2016, IEEE Robotics and Automation Letters.

[16]  Rodrigo Alvarez-Icaza,et al.  Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations , 2014, Proceedings of the IEEE.

[17]  Yi Zhengkun,et al.  Recognizing tactile surface roughness with a biomimetic fingertip: A soft neuromorphic approach , 2017 .

[18]  Maurizio Valle,et al.  Approximate FPGA Implementation of CORDIC for Tactile Data Processing Using Speculative Adders , 2017, 2017 New Generation of CAS (NGCAS).

[19]  Angelo Arleo,et al.  Encoding/decoding of first and second order tactile afferents in a neurorobotic application , 2011, Journal of Physiology-Paris.

[20]  Nitish V. Thakor,et al.  Live demonstration: Prosthesis grip force modulation using neuromorphic tactile sensing , 2017, 2017 IEEE International Symposium on Circuits and Systems (ISCAS).

[21]  Andrew S. Cassidy,et al.  Building block of a programmable neuromorphic substrate: A digital neurosynaptic core , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[22]  Jilles Vreeken,et al.  Spiking neural networks, an introduction , 2003 .

[23]  T. Prescott,et al.  Biomimetic vibrissal sensing for robots , 2011, Philosophical Transactions of the Royal Society B: Biological Sciences.

[24]  Wulfram Gerstner,et al.  Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. , 2005, Journal of neurophysiology.

[25]  Philip Holmes,et al.  Neural Dynamics, Bifurcations, and Firing Rates in a Quadratic Integrate-and-Fire Model with a Recovery Variable. I: Deterministic Behavior , 2012, Neural Computation.

[26]  F. Grassia,et al.  Digital hardware implementation of a stochastic two-dimensional neuron model , 2016, Journal of Physiology-Paris.

[27]  R. Johansson,et al.  Tactile sensibility in the human hand: relative and absolute densities of four types of mechanoreceptive units in glabrous skin. , 1979, The Journal of physiology.

[28]  Sunghwa Lee,et al.  Flexon: A Flexible Digital Neuron for Efficient Spiking Neural Network Simulations , 2018, 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA).

[29]  Thomas Stieglitz,et al.  Paradigms for restoration of somatosensory feedback via stimulation of the peripheral nervous system , 2017, Clinical Neurophysiology.

[30]  Hannes P. Saal,et al.  Biomimetic approaches to bionic touch through a peripheral nerve interface , 2015, Neuropsychologia.

[31]  F. McGlone,et al.  The cutaneous sensory system , 2010, Neuroscience & Biobehavioral Reviews.

[32]  Nigel H. Lovell,et al.  A review of tactile sensing technologies with applications in biomedical engineering , 2012 .

[33]  Indranil Saha,et al.  journal homepage: www.elsevier.com/locate/neucom , 2022 .

[34]  L L Bologna,et al.  A closed-loop neurobotic system for fine touch sensing , 2013, Journal of neural engineering.

[35]  Benoit P. Delhaye,et al.  Simulating tactile signals from the whole hand with millisecond precision , 2017, Proceedings of the National Academy of Sciences.

[36]  Yann Roudaut,et al.  Touch sense , 2012, Channels.

[37]  Nitish V. Thakor,et al.  Gait event detection through neuromorphic spike sequence learning , 2014, 5th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics.

[38]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1990 .

[39]  André van Schaik,et al.  An FPGA-Based Massively Parallel Neuromorphic Cortex Simulator , 2018, Front. Neurosci..

[40]  Anthony G. Pipe,et al.  Implementing Spiking Neural Networks for Real-Time Signal-Processing and Control Applications: A Model-Validated FPGA Approach , 2007, IEEE Transactions on Neural Networks.

[41]  Cecilia Laschi,et al.  A Digital Hardware Realization for Spiking Model of Cutaneous Mechanoreceptor , 2018, Front. Neurosci..

[42]  Yannick Bornat,et al.  Biomimetic neural network for modifying biological dynamics during hybrid experiments , 2017, Artificial Life and Robotics.

[43]  Rodolphe Sepulchre,et al.  Robust Modulation of Integrate-and-Fire Models , 2017, Neural Computation.

[44]  Javad Dargahi,et al.  Application of artificial neural networks for the estimation of tumour characteristics in biological tissues , 2007, The international journal of medical robotics + computer assisted surgery : MRCAS.

[45]  M. C. Carrozza,et al.  Soft-neuromorphic artificial touch for applications in neuro-robotics , 2012, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[46]  Anthony G. Pipe,et al.  A Hardware Based Implementation of a Tactile Sensory System For Neuromorphic Signal Processing Applications , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[47]  Takashi Kohno,et al.  Simple Cortical and Thalamic Neuron Models for Digital Arithmetic Circuit Implementation , 2016, Front. Neurosci..

[48]  Arash Ahmadi,et al.  Biologically Inspired Spiking Neurons: Piecewise Linear Models and Digital Implementation , 2012, IEEE Transactions on Circuits and Systems I: Regular Papers.

[49]  Sung Soo Kim,et al.  Conveying Tactile Feedback in Sensorized Hand Neuroprostheses Using a Biofidelic Model of Mechanotransduction , 2009, IEEE Transactions on Biomedical Circuits and Systems.

[50]  Alberto Mazzoni,et al.  Neuromorphic Artificial Sense of Touch: Bridging Robotics and Neuroscience , 2015, ISRR.

[51]  Eugene M. Izhikevich,et al.  Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.

[52]  J. Randall Flanagan,et al.  Coding and use of tactile signals from the fingertips in object manipulation tasks , 2009, Nature Reviews Neuroscience.