A carbon nanotube spiking cortical neuron with tunable refractory period and spiking duration

This paper describes an analog tunable carbon nanotube axon hillock that exhibits spiking with control over spiking frequency and spiking duration. Experiments with the axon hillock circuit embedded in a neuron circuit demonstrate spiking patterns similar to a biological fast-spiking neuron. The circuit design is biomimetic and changes in control voltages lead to changes in key spike parameters such as spike refractory period and spiking duration. The effect of change in output spiking frequency is tested with synapses that temporally summate, and their effect on neural firing is observed. The experiments are demonstrated with SPICE simulations using carbon nanotube transistor simulation models.

[1]  Hicham Chaoui.  CMOS analogue adder , 1995 .

[2]  R. Yuste,et al.  Thalamocortical Bursts Trigger Recurrent Activity in Neocortical Networks: Layer 4 as a Frequency-Dependent Gate , 2002, The Journal of Neuroscience.

[3]  Kwabena Boahen,et al.  Silicon neurons that burst when primed , 2007, 2007 IEEE International Symposium on Circuits and Systems.

[4]  M. Alexander,et al.  Principles of Neural Science , 1981 .

[5]  H.-S. Philip Wong,et al.  Design Methods for Misaligned and Mispositioned Carbon-Nanotube Immune Circuits , 2008, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[6]  Alice C. Parker,et al.  A carbon nanotube cortical neuron with excitatory and inhibitory dendritic computations , 2009, 2009 IEEE/NIH Life Science Systems and Applications Workshop.

[7]  Giacomo Indiveri,et al.  A low-power adaptive integrate-and-fire neuron circuit , 2003, Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03..

[8]  Bo Liu,et al.  A CMOS neuron for VLSI circuit implementation of pulsed neural networks , 2002, IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02.

[9]  H. Wong,et al.  Impact of a Process Variation on Nanowire and Nanotube Device Performance , 2007, IEEE Transactions on Electron Devices.

[10]  Alice C. Parker,et al.  Towards a Nanoscale Artificial Cortex , 2006, CDES.

[11]  Y. Amemiya,et al.  Analog CMOS implementation of a bursting oscillator with depressing synapse , 2004, Proceedings of the 2004 Intelligent Sensors, Sensor Networks and Information Processing Conference, 2004..

[12]  Chongwu Zhou,et al.  Novel nanotube-on-insulator (NOI) approach toward single-walled carbon nanotube devices. , 2006, Nano letters.

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

[14]  Yu-Ming Chang,et al.  Increased action potential firing rates of layer 2/3 pyramidal cells in the prefrontal cortex are significantly related to cognitive performance in aged monkeys. , 2005, Cerebral cortex.

[15]  Hong Zhou,et al.  Representation of stereoscopic edges in monkey visual cortex , 2000, Vision Research.

[16]  H. Robinson,et al.  Threshold firing frequency-current relationships of neurons in rat somatosensory cortex: type 1 and type 2 dynamics. , 2004, Journal of neurophysiology.

[17]  Kwabena Boahen,et al.  Optic nerve signals in a neuromorphic chip II: testing and results , 2004, IEEE Transactions on Biomedical Engineering.

[18]  Paul E. Hasler,et al.  A bio-physically inspired silicon neuron , 2004, IEEE Transactions on Circuits and Systems I: Regular Papers.

[19]  Chih-Chieh Hsu,et al.  A carbon nanotube implementation of temporal and spatial dendritic computations , 2008, 2008 51st Midwest Symposium on Circuits and Systems.

[20]  M. Prato,et al.  Biomedical applications of functionalised carbon nanotubes. , 2005, Chemical communications.

[21]  H. Wong,et al.  A Circuit-Compatible SPICE model for Enhancement Mode Carbon Nanotube Field Effect Transistors , 2006, 2006 International Conference on Simulation of Semiconductor Processes and Devices.