Rapid processing of chemosensor transients in a neuromorphic implementation of the insect macroglomerular complex

Abstract

We present a biologically-constrained neuromorphic spiking model of the insect antennal lobe macroglomerular complex that encodes concentration ratios of chemical components existing within a blend, implemented using a set of programmable logic neuronal modeling cores. Depending upon the level of inhibition and symmetry in its inhibitory connections, the model exhibits two dynamical regimes: fixed point attractor (winner-takes-all type), and limit cycle attractor (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 accurately encode the concentration ratios of binary odor mixtures in both dynamical regimes. By deploying spike timing-dependent plasticity in a subset of the synapses in the model, we demonstrate that a Hebbian-like associative learning rule is able to organize weights into a stable configuration after exposure to a randomized 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 across the population, forming a ratiometric representation via mutual inhibition. After training the resulting dynamical trajectories of the projection neuron population activity show amplification and better separation in their response to inputs of different ratios. Finally, we demonstrate that by using limit cycle attractor dynamics, 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 a nearest-neighbor classifier applied to the normalized 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.

Comparing Neuromorphic Solutions in Action: Implementing a Bio-Inspired Solution to a Benchmark Classification Task on Three Parallel-Computing Platforms

“…To meet the challenge of efficient neuromorphic computing a number of neuromorphic platforms have been—and continue to be—developed. Some of these systems employ analog neuron circuits (see Indiveri et al, 2011 , for a review), others are implemented in FPGAs (e.g., Pearce et al, 2013 ), or employ specialized digital hardware, such as the SpiNNaker system (Khan et al, 2008 ; Furber et al, 2013 ). Moreover, GPU-based simulators have recently become popular for neuromorphic computing because they can provide a considerable speedup over CPU-based simulators on desktop systems, whilst retaining a manageable power budget and are fully programmable using high-level languages (Fidjeland and Shanahan, 2010 ; Nowotny et al, 2014 ).…”

A Hardware-Deployable Neuromorphic Solution for Encoding and Classification of Electronic Nose Data

“…Generally, bio-inspired electronic nose systems have focused on implementing rate coding or rank-order coding to encode olfaction information into spiking data [11,13]. The idea of an AER-based event-driven approach for neuromorphic olfactory systems has only been applied in studies focused on the neuro-modelling of the biological counterpart [30] and, therefore, its utilization for real-world applications has been limited.…”

Event-Based Sensing and Signal Processing in the Visual, Auditory, and Olfactory Domain: A Review

“…In Pearce et al (2013Pearce et al ( , 2014, a biologically-constrained neuromorphic spiking model of the insect antennal lobe is presented that detects the concentration of chemical components of a material. The system is dynamic and uses winner-takesall or winnerless competition depending on the inhibition and symmetry of its connections.…”

“…Vertebrate olfactory system (White et al, 1998;White and Kauer, 1999), mammals (Jing et al, 2016) Insect olfactory Insect antennal lobe (Pearce et al, 2013(Pearce et al, , 2014Diamond et al, 2016) Honeybee olfactory Honeybee's olfactory pathway (Hausler et al, 2011;Schmuker et al, 2011), honeybee antennal lobe (Kasap and Schmuker, 2013) Stereo olfaction Stereo olfaction (Rochel et al, 2002) Hardware systems VLSI VLSI spiking neuromorphic system (Koickalb et al, 2004;Pearce et al, 2005;Hsieh and Tang, 2012), adaptive neuromorphic VLSI olfaction (Koickal et al, 2006(Koickal et al, , 2007 Hardware classifier Sampling spiking neural networks (Abdel-Aty-Zohdy et al, 2010), CMOS gas recognition chip (Ng et al, 2011), gas recognition (Al Yamani et al, 2012a, logarithmic time encoding model (Hassan et al, 2015) Modeling and algorithms…”