| Summary: | We present a biologically-constrained neuromorphic spiking model of the insect antennal lobe macroglomerular complex (MGC) that encodes concentration ratios of chemical components existing within a blend, implemented using a set of programmable logic neuronal modelling cores. Depending upon the level of inhibition and symmetry in its inhibitory connections, the model exhibits two dynamical regimes: fixed point attractor (FPA: winner-takes-all type), and limit cycle attractor (LCA: winnerless competition type) dynamics. We show that, when driven by chemosensor input in real-time, the dynamical trajectories of the model’s projection neuron population activity accu/rately encode the concentration ratios of binary odour mixtures in both dynamical regimes. By deploying spike timing-dependent plasticity (STDP) in a subset of the synapses in the model, we demonstrate that a Hebbian-like associative learning rule is able to organise weights into a stable configuration after exposure to a randomised training set comprising a variety of input ratios. Examining the resulting local interneuron weights in the model shows that each inhibitory neuron competes to represent possible ratios as a population, forming a ratiometric representation via mutual inhibition. After training the resulting dynamical trajectories of the projection neuron population show amplification and better separation in their response to inputs of different ratios. Finally, we demonstrate that by using LCA dynamics within the model, it is possible to recover and classify blend ratio information from the early transient phases of chemosensor responses in real-time more rapidly and accurately compared to equivalent classification applied to the normalised chemosensor data. Our results demonstrate the potential of biologically-constrained neuromorphic spiking models in achieving rapid and efficient classification of early phase chemosensor array transients with execution times well beyond biological timescales.
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