Author:
Sengupta Abhronil,Panda Priyadarshini,Wijesinghe Parami,Kim Yusung,Roy Kaushik
Abstract
Abstract
Brain-inspired computing architectures attempt to mimic the computations performed in the neurons and the synapses in the human brain in order to achieve its efficiency in learning and cognitive tasks. In this work, we demonstrate the mapping of the probabilistic spiking nature of pyramidal neurons in the cortex to the stochastic switching behavior of a Magnetic Tunnel Junction in presence of thermal noise. We present results to illustrate the efficiency of neuromorphic systems based on such probabilistic neurons for pattern recognition tasks in presence of lateral inhibition and homeostasis. Such stochastic MTJ neurons can also potentially provide a direct mapping to the probabilistic computing elements in Belief Networks for performing regenerative tasks.
Publisher
Springer Science and Business Media LLC
Reference37 articles.
1. Ghosh-Dastidar, S. & Adeli, H. Spiking neural networks. International journal of neural systems 19, 295–308 (2009).
2. Rajendran, B. et al. Specifications of nanoscale devices and circuits for neuromorphic computational systems. Electron Devices, IEEE Transactions on 60, 246–253 (2013).
3. Indiveri, G. A low-power adaptive integrate-and-fire neuron circuit. In Circuits And Systems (ISCAS), 2003 International Symposium On, 820–823 (Bangkok, Thailand, May 25, 2003).
4. Sobie, C., Babul, A. & de Sousa, R. Neuron dynamics in the presence of 1/f noise. Physical Review E 83, 051912 (2011).
5. Nessler, B., Pfeiffer, M., Buesing, L. & Maass, W. Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity. PLoS Comput Biol 9, e1003037 (2013).
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