Implementation of Olfactory Bulb Glomerular-Layer Computations in a Digital Neurosynaptic Core

Abstract

We present a biomimetic system that captures essential functional properties of the glomerular layer of the mammalian olfactory bulb, specifically including its capacity to decorrelate similar odor representations without foreknowledge of the statistical distributions of analyte features. Our system is based on a digital neuromorphic chip consisting of 256 leaky-integrate-and-fire neurons, 1024 × 256 crossbar synapses, and address-event representation communication circuits. The neural circuits configured in the chip reflect established connections among mitral cells, periglomerular cells, external tufted cells, and superficial short-axon cells within the olfactory bulb, and accept input from convergent sets of sensors configured as olfactory sensory neurons. This configuration generates functional transformations comparable to those observed in the glomerular layer of the mammalian olfactory bulb. Our circuits, consuming only 45 pJ of active power per spike with a power supply of 0.85 V, can be used as the first stage of processing in low-power artificial chemical sensing devices inspired by natural olfactory systems.

Role of intraglomerular circuits in shaping temporally structured responses to naturalistic inhalation-driven sensory input to the olfactory bulb

“…Our strategy to evaluate model performance was to assess how accurately the OB network generated output parameters, in particular, spiking dynamics, matching those recorded experimentally given biologically realistic inputs. This strategy differs from other models which have sought to identify circuit mechanisms capable of mediating particular neural computations, such as concentration-invariant odor discrimination (Brody and Hopfield 2003;Cleland et al 2007Margrie and Schaefer 2003), contrast enhancement and pattern decorrelation (Cleland and Linster 2012;Cleland and Sethupathy 2006;Imam et al 2012;Luo et al 2010;Wiechert et al 2010), and representation sparsening (Assisi et al 2007;Finelli et al 2008), among others. Our approach does not presume that any particular computation might be performed by a specific circuit component (nor does it investigate OB computations), but rather seeks to identify how specific network components influence experimentally measured output features, in this case, inhalation-driven MC spiking patterns.…”

Six Networks on a Universal Neuromorphic Computing Substrate

“…The function of the AL has been described to decorrelate the inputs from sensory neurons, potentially enabling more efficient memory formation and retrieval (Linster and Smith, 1997; Stopfer et al, 1997; Perez-Orive et al, 2004; Wilson and Laurent, 2005; Schmuker et al, 2011b). The mammalian analog of the AL (the olfactory bulb) has been the target of a recent neuromorphic modeling study (Imam et al, 2012b). …”

A neuromorphic network for generic multivariate data classification

“…To overcome this limitation, several groups around the world have started to develop hardware realizations of spiking neuron models and neuronal networks (2)(3)(4)(5)(6)(7)(8)(9)(10) for studying the behavior of biological networks (11). The approach of the Spikey hardware system used in the present study is to enable high-throughput network simulations by speeding up computation by a factor of 10 4 compared with biological real time (12,13).…”