A neuromorphic model of olfactory processing and sparse coding in the Drosophila larva brain

Abstract

Animal nervous systems are highly efficient in processing sensory input. The neuromorphic computing paradigm aims at the hardware implementation of neural network computations to support novel solutions for building brain-inspired computing systems. Here, we take inspiration from sensory processing in the nervous system of the fruit fly larva. With its strongly limited computational resources of <200 neurons and <1.000 synapses the larval olfactory pathway employs fundamental computations to transform broadly tuned receptor input at the periphery into an energy efficient sparse code in the central brain. We show how this approach allows us to achieve sparse coding and increased separability of stimulus patterns in a spiking neural network, validated with both software simulation and hardware emulation on mixed-signal real-time neuromorphic hardware. We verify that feedback inhibition is the central motif to support sparseness in the spatial domain, across the neuron population, while the combination of spike frequency adaptation and feedback inhibition determines sparseness in the temporal domain. Our experiments demonstrate that such small-sized, biologically realistic neural networks, efficiently implemented on neuromorphic hardware, can achieve parallel processing and efficient encoding of sensory input at full temporal resolution.

Prediction error drives associative olfactory learning and conditioned behavior in a spiking model ofDrosophila larva

“…On the level of the ORNs 3-octanol merely activated a subset of the amylacetate-activated neurons. The uniqueness of the odor identities is enhaced in the KC population [57].…”

“…An additional spike-triggered adaptation conductance was implemented for ORNs, KCs, MBONs and DANs (eqn. 12, [57]). With every spike this conductance g Ia is increased, where τ Ia is the decay time.…”

“…We implemented four mechanisms supporting population- and temporal sparseness in the MB odor representation [57]. Population sparseness is defined as activation of only a small subset of neurons by any given input [100].…”

“…Population sparseness is defined as activation of only a small subset of neurons by any given input [100]. In this circuit population sparseness is enhanced through lateral inhibition (via LNs), inhibitory APL feedback and the divergent connectivity from PNs to a larger number of KCs [57]. Temporal sparseness indicates that an individual neuron responds with only a few spikes to a specific stimulus configuration [101–103], which supports encoding dynamic changes in the sensory environment [104, 105].…”

Superconducting Bio-Inspired Au-Nanowire-Based Neurons

“…Modeling the processes in the brain of living beings is a complex and urgent task [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ]. One of the main problems in this area is an insufficient number of neurons and synapses in modern Complementary-Metal-Oxide-Semiconductor (CMOS) neuromorphic processors.…”