Volume 0, Issue 1, PP: 24-30, 2019 | Cite this article as | XML | | Html PDF
Shubha Mishra 1 *
Proficient design division in dentate gyrus plays an imperative part in putting away data within the hippocampus. Current information of the structure and work of the hippocampus, entorhinal cortex, and dentate gyrus, in design partition are joined in this work. A three-layer feed-forward spiking neural network inspired by the rodent hippocampus an equipped with simplified synaptic and molecular mechanisms is developed. The aim of the study is to make a spiking neural network capable of pattern separation in imbalanced excitation/inhibition ratios caused by different levels of stimulations or network damage. This work presents a novel theory on the cellular mechanisms of robustness to damages to synapses and connectivity of neurons in dentate gyrus that results in imbalanced excitation-inhibition activity of neurons. This spiking neural network uses simplified molecular and cellular hypothetical mechanisms and demonstrates efficient storing of information in different levels of stimulation and can be implemented in cognitive robotics.
spiking neural networks , memory , dentate gyrus , pattern separation , imbalanced network , back-propagation , hippocampus
,
[1] GoertzelBen, LianRuiting,Arel Itamar,deGaris Hugo, ChenShuo: A world survey of
artificial brain projects, Part ii. Biologically inspired cognitive architectures.
Neurocomputing 2010,74:30-49.
[2] Hassabis, Demis, Dharshan Kumaran, Christopher Summerfield, and Matthew Botvinick.
"Neuroscience-inspired artificial intelligence." Neuron 95, no. 2 (2017): 245-258..
[3] Blundell, C., Uria, B., Pritzel, A., Yazhe, L., Ruderman, A., Leibo, J.Z., Rae, J., Wierstra,
D., and Hassabis, D. (2016). Model-free episodic control. arXiv, arXiv:160604460.
[4] Botvinick, M.M., and Plaut, D.C. (2006). Short-term memory for serial order: a recurrent
neural network model. Psychol. Rev. 113, 201–233.
[5] Ferbinteanu, Janina, Pamela J. Kennedy, and Matthew L. Shapiro. "Episodic memory—
from brain to mind." Hippocampus 16, no. 9 (2006): 691-703.
[6] Tulving, E. (2002). Episodic memory: from mind to brain. Annu. Rev. Psychol. 53, 1–25.
[7] Squire, L.R., Stark, C.E., and Clark, R.E. (2004). The medial temporal lobe. Annu. Rev.
Neurosci. 27, 279–306.
[8] Lavenex, Pierre, and David G. Amaral. "Hippocampal‐neocortical interaction: A
hierarchy of associativity." Hippocampus 10, no. 4 (2000): 420-430.
[9] Moser, E. I. & Moser, M. B. One-shot memory in hippocampal CA3 networks. Neuron
38, 147–148 (2003).
[10] Weaver, Janelle. "How one-shot learning unfolds in the brain." PLoS biology 13, no. 4
(2015): e1002138.
[11] Bannerman, David M., Rolf Sprengel, David J. Sanderson, Stephen B. McHugh, J.
Nicholas P. Rawlins, Hannah Monyer, and Peter H. Seeburg. "Hippocampal synaptic
plasticity, spatial memory and anxiety." Nature Reviews Neuroscience15, no. 3 (2014):
181.
[12] Guderian, Sebastian, Anna M. Dzieciol, David G. Gadian, Sebastian Jentschke, Christian
F. Doeller, Neil Burgess, Mortimer Mishkin, and Faraneh Vargha-Khadem.
"Hippocampal volume reduction in humans predicts impaired allocentric spatial memory
in virtual-reality navigation." Journal of Neuroscience 35, no. 42 (2015): 14123-14131.
[13] Spellman, Timothy, Mattia Rigotti, Susanne E. Ahmari, Stefano Fusi, Joseph A. Gogos,
and Joshua A. Gordon. "Hippocampal–prefrontal input supports spatial encoding in
working memory." Nature 522, no. 7556 (2015): 309.
[14] Rothschild, Gideon, Elad Eban, and Loren M. Frank. "A cortical–hippocampal–cortical
loop of information processing during memory consolidation." Nature neuroscience 20,
no. 2 (2017): 251.
[15] Schliebs, Stefan, Haza Nuzly Abdull Hamed, and Nikola Kasabov. "Reservoir-based
evolving spiking neural network for spatio-temporal pattern recognition." In International
Conference on Neural Information Processing, pp. 160-168. Springer, Berlin, Heidelberg,
2011.
[16] Iakymchuk, Taras, Alfredo Rosado-Muñoz, Juan F. Guerrero-Martínez, Manuel Bataller-
Mompeán, and Jose V. Francés-Víllora. "Simplified spiking neural network architecture
and STDP learning algorithm applied to image classification." EURASIP Journal on
Image and Video Processing 2015, no. 1 (2015): 4.
[17] Kasabov, Nikola, and Elisa Capecci. "Spiking neural network methodology for
modelling, classification and understanding of EEG spatio-temporal data measuring
cognitive processes." Information Sciences 294 (2015): 565-575.
[18] Tandon, Pulkit, Yash H. Malviya, and Bipin Rajendran. "Efficient and robust spiking
neural circuit for navigation inspired by echolocating bats." In Advances in Neural
Information Processing Systems, pp. 938-946. 2016.
[19] Stromatias, Evangelos, Miguel Soto, Teresa Serrano-Gotarredona, and Bernabé Linares-
Barranco. "An event-driven classifier for spiking neural networks fed with synthetic or
dynamic vision sensor data." Frontiers in neuroscience 11 (2017) 350.
