مورد إلكتروني
A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine
| العنوان: | A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine |
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| بيانات النشر: | Frontiers Media 2017 |
| تفاصيل مُضافة: | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores Sen-Bhattacharya, Basabdatta Serrano Gotarredona, María Teresa Balassa, Lorinc Bhattacharya, Akash Stokes, Alan B. Rowley, Andrew Sugiarto, Indar Furber, Steve B. |
| نوع الوثيقة: | Electronic Resource |
| مستخلص: | We present a spiking neural network model of the thalamic Lateral Geniculate Nucleus (LGN) developed on SpiNNaker, which is a state-of-the-art digital neuromorphic hardware built with very-low-power ARM processors. The parallel, event-based data processing in SpiNNaker makes it viable for building massively parallel neuro-computational frameworks. The LGN model has 140 neurons representing a “basic building block” for larger modular architectures. The motivation of this work is to simulate biologically plausible LGN dynamics on SpiNNaker. Synaptic layout of the model is consistent with biology. The model response is validated with existing literature reporting entrainment in steady state visually evoked potentials (SSVEP)—brain oscillations corresponding to periodic visual stimuli recorded via electroencephalography (EEG). Periodic stimulus to the model is provided by: a synthetic spike-train with inter-spike-intervals in the range 10–50 Hz at a resolution of 1 Hz; and spike-train output from a state-of-the-art electronic retina subjected to a light emitting diode flashing at 10, 20, and 40 Hz, simulating real-world visual stimulus to the model. The resolution of simulation is 0.1 ms to ensure solution accuracy for the underlying differential equations defining Izhikevichs neuron model. Under this constraint, 1 s of model simulation time is executed in 10 s real time on SpiNNaker; this is because simulations on SpiNNaker work in real time for time-steps dt > 1 ms. The model output shows entrainment with both sets of input and contains harmonic components of the fundamental frequency. However, suppressing the feed-forward inhibition in the circuit produces subharmonics within the gamma band (>30 Hz) implying a reduced information transmission fidelity. These model predictions agree with recent lumped-parameter computational model-based predictions, using conventional computers. Scalability of the framework is demonstrated by a multi-node architecture consisting of th |
| مصطلحات الفهرس: | Lateral geniculate nucleus, SpiNNaker machine, sPyNNaker, Steady state visually evoked potentials, LGN interneurons, Entrainment, Electronic retina, Multi-node models, info:eu-repo/semantics/article |
| URL: | Frontiers in Neuroscience, 11, 454-. EP/D07908X/1 EP/G015740/1 FP7-604102 FP7-320689 H2020-644096 H2020-687299 PRX16/00248 TIC-6091 TIC-6091 TEC2015-63884- C2-1-P htpp://dx.doi.org/10.3389/fnins.2017.00454 |
| الإتاحة: | Open access content. Open access content http://creativecommons.org/licenses/by-nc-nd/4.0 info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
| ملاحظة: | English |
| أرقام أخرى: | SUE oai:idus.us.es:11441/73359 Sen-Bhattacharya, B., Serrano Gotarredona, M.T., Balassa, L., Bhattacharya, A., Stokes, A.B., Rowley, A.,...,Furber, S. B. (2017). A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine. Frontiers in Neuroscience, 11, 454-. 1662-4548 (impreso) 1662-453X (electrónico) 10.3389/fnins.2017.00454 1367078793 |
| المصدر المساهم: | UNIV DE SEVILLA From OAIster®, provided by the OCLC Cooperative. |
| رقم الأكسشن: | edsoai.on1367078793 |
| قاعدة البيانات: | OAIster |
| الوصف غير متاح. |