Abstract
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 three “nodes,” where each node is the “basic building block” LGN model. This 420 neuron model is tested with synthetic periodic stimulus at 10 Hz to all the nodes. The model output is the average of the outputs from all nodes, and conforms to the above-mentioned predictions of each node. Power consumption for model simulation on SpiNNaker is ≪1 W.
Highlights
Designing and building brain-inspired computational models has made steady advances following the Nobel-prize winning work by Hodgkin and Huxley (Schwiening, 2012)
To emulate the behavior of the model under a real periodic visual stimulus, we have recorded the spikes generated by the electronic retina when placed in front of an LED that is driven by a periodic signal, producing a flashing light
Computational neurology and psychiatry provide an excellent means for in-depth investigations of vital structures such as the thalamus, which are otherwise hard to study in wetlaboratories (Sen-Bhattacharya et al, 2017)
Summary
Designing and building brain-inspired computational models has made steady advances following the Nobel-prize winning work by Hodgkin and Huxley (Schwiening, 2012). Neuromorphic hardware is an emerging trend that has the potential to overcome the constraints of conventional computers in modeling brain structures and functions. Our goal in this work is to design and develop a spiking neural network model of the Lateral Geniculate Nucleus (LGN; the thalamic nucleus in the visual pathway) on the novel platform that is SpiNNaker. The motivation is to test the feasibility of simulating biologically plausible behavior on the SpiNNaker machine; this will help in assessing its potential as a “tool” for building large-scale biologically plausible neural networks
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