Capstone II, June-July, 2026
The real time simulation of biological neurons is a main, practical problem in computational neu-roscience and neuromorphic engineering, and is now increasingly common due to hardware cost, lab tools shortages, reduced research funding spending gazettes, etc. For example, one cortical column in a brain of a mammal includes approx. tens of thousands of neurons. (depending on the species.) And it includes hundreds of millions to billions of neural synaptic connections. In a biological brain, those neurons and connections have to update in that same clock cycle (refer Sievers et al., 2024). On the other hand, current computing hardware is unable to provide this par-allel process for an ordinary laboratory with a relatively small fund or budget. Information Theory, time complexity and computation in general, used an already existing method in computer sci-ence and telecommunication for achieving a remarkable efficiency in computational neuroscience, named as time multiplexing. Meaning that, one processing element cycles through the states of several simulated neurons in a sequence, in the sequential algorithmic order. Time multiplexing architecture does not allocate one separate physical processing element (or logic elements/LEs ) to every neuron in the simulation process. In this research, this combined processing element is named as a SuperNeuron. In addition to that, this is not solely an abstract concept. The University of Manchester’s SpiNNaker system uses several artificial neurons (hundreds of neurons) through each physical processor core, and this process enables real time biological neuron simulation at a greater scale (refer Furber et al., 2014). Loihi 1 and 2 by the Intel Corp. and the IBM TrueNorth also process a greater amount of simulated neurons by using the method called Time Division Multiplexing (or TDM) and event driven processes (refer Davies et al., 2018, Merolla et al., 2014). IBM Corp’s TrueNorth achieved about 16 million neurons by using the NS16e system with several 1 canvas IYNA Summer 2026 cores (refer DeBole et al., 2019) ,
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contact: n.desilva@fiaos.org