Abstract
In classical computational neuroscience, analytical model descriptions are derived from neuronal recordings to mimic the underlying biological system. These neuronal models are typically slow to compute and cannot be integrated within large-scale neuronal simulation frameworks. We present a hybrid, machine-learning and computational-neuroscience approach that transforms analytical models of sensory neurons and synapses into deep-neural-network (DNN) neuronal units with the same biophysical properties. Our DNN-model architecture comprises parallel and differentiable equations that can be used for backpropagation in neuro-engineering applications, and offers a simulation run-time improvement factor of 70 and 280 on CPU or GPU systems respectively. We focussed our development on auditory neurons and synapses, and show that our DNN-model architecture can be extended to a variety of existing analytical models. We describe how our approach for auditory models can be applied to other neuron and synapse types to help accelerate the development of large-scale brain networks and DNN-based treatments of the pathological system.
Highlights
In classical computational neuroscience, analytical model descriptions are derived from neuronal recordings to mimic the underlying biological system
Acoustic speech material is given as input to the analytical descriptions of cochlear and IHC–auditory-nerve fiber (ANF) processing, after which simulated ANF firing rates are used as training material to determine the CoNNearANFL parameters
CoNNear presents a new method for projecting complex mathematical descriptions of auditory neuron and synapse models to Deep neural networks (DNNs) architectures, while providing a differentiable solution and accelerated run-time
Summary
Analytical model descriptions are derived from neuronal recordings to mimic the underlying biological system. Transfer functions between stimulation and recorded neural activity are derived and approximated analytically This approach resulted in a variety of stimulus-driven models of neuronal firing and was successful in describing the non-linear and adaptation properties of sensory systems[3,4,5,6]. Such mechanistic models have substantially improved our understanding of how individual neurons function, but even the most basic models use coupled sets of ordinary differential equations (ODEs) in their descriptions This computational complexity hinders their further development to simulate more complex behaviour, limits their integration within large-scale neuronal simulation platforms[14,15], and their uptake in neuro-engineering applications that require real-time, closed-loop neuron model units[16,17]. Progressive insight into the function of IHC–ANF synapses over the past decades has inspired numerous analytical model descriptions of the IHC, IHC–ANF synapse, and ANF neuron complex[11,12,13,52,53,54,55,56,57,58,59,60,61,62]
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