fnins-07-00119.pdf (3.29 MB)
Download file

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

Download (3.29 MB)
journal contribution
posted on 14.05.2014, 14:53 by Timothy C. Pearce, Salah Karout, Zoltan Rácz, Alberto Capurro, Julian W. Gardner, Marina Cole
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.

History

Citation

Frontiers in Neuroscience, 2013, 7 : 119

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Engineering

Version

VoR (Version of Record)

Published in

Frontiers in Neuroscience

Publisher

Frontiers

issn

1662-4548

eissn

1662-453X

Copyright date

2013

Available date

14/05/2014

Publisher version

http://journal.frontiersin.org/Journal/10.3389/fnins.2013.00119/full

Language

en