Rapid online learning and robust recall in a neuromorphic olfactory circuit

Abstract

We present a neural algorithm for the rapid online learning and identification of odourant samples under noise, based on the architecture of the mammalian olfactory bulb and implemented on the Intel Loihi neuromorphic system. As with biological olfaction, the spike timing-based algorithm utilizes distributed, event-driven computations and rapid (one-shot) online learning. Spike timing-dependent plasticity rules operate iteratively over sequential gamma-frequency packets to construct odour representations from the activity of chemosensor arrays mounted in a wind tunnel. Learned odourants then are reliably identified despite strong destructive interference. Noise resistance is further enhanced by neuromodulation and contextual priming. Lifelong learning capabilities are enabled by adult neurogenesis. The algorithm is applicable to any signal identification problem in which high-dimensional signals are embedded in unknown backgrounds.

Figure 1.

Model structure and signal encoding. a, Architecture of the neuromorphic model. Sensor input is delivered to the apical dendrite (AD) of each mitral cell (MC), which in turn excites its corresponding soma (S). MC activity propagates via lateral dendrites (purple) to excite the dendrites of granule cells (GC, orange). The distribution of excitatory connections (open circles) is sparse and independent of spatial proximity. In contrast, GC inhibitory connections are local. b, Architecture of the Intel Loihi neuromorphic chip. Neuromorphic cores (blue squares) operate in parallel and communicate through a mesh of spike routers (grey circles). c, Illustration of odourant delivery to a 72-element chemosensor array within a wind tunnel. d, Presentation of acetone (top) or toluene (bottom) to the chemosensor array resulted in characteristic patterns of spiking activity across the 72 MCs (ordinate). Stronger sensor activation led to correspondingly earlier MC spikes within each gamma cycle. Inhibitory epochs are denoted by black bars; permissive epochs are denoted by white bars. The fifth gamma cycle is expanded in time (rightmost panels) to illustrate the distribution of MC spike times. ts, timesteps.

Figure 2.

Plasticity rules. a, During training, coincident MC spikes converging onto a given GC activated that GC, and developed strong excitatory synaptic weights thereon, whereas other inputs to that GC were weakened and ultimately eliminated. b, Excitatory plasticity rendered GCs selective to higher-order features of odour representations. After training on toluene (left panel) or acetone (right panel), a number of GCs became responsive to specific combinations of activated MCs. Spike times highlighted with green spots denote those MC spikes that activated a specific GC in a network trained on toluene. Red spots denote a second such GC, responsive to toluene via a different set of activated MCs. Yellow and grey spots denote analogous MC spike populations that activate two GCs responsive to acetone in the same network. c, Illustration of the inhibitory plasticity rule. During training, the duration of spike-mediated GC inhibition onto its cocolumnar MC (red bar) increased until the release of this inhibition (green) coincided with spike initiation in the MC apical dendrite. The learned inhibitory weight corresponded to a blocking period ΔB during which spike propagation in the MC soma was suppressed. d, Illustration of the iterative denoising of an occluded test sample. Partially-correct representations in MCs evoke responses in some of the correct GCs, which deliver inhibition that draws MC ensemble activity iteratively closer to the learned representation. Three permissive epochs interspersed with two inhibitory epochs are depicted.

Figure 3.

Odourant-evoked MC activity patterns are attracted to learned representations. a, Presentation of an occluded instance of toluene to an untrained network. Blue dot rasters denote spike times evoked by occluded toluene (impulse noise P = 0.6). The untrained network does not update the response to occluded toluene over the five gamma cycles depicted. For comparison, open circle rasters denote the spike times evoked by non-occluded toluene. ts, timesteps. b, Presentation of the same occluded instance of toluene to a plastic network trained on (non-occluded) toluene. The activity profile evoked by the occluded sample was attracted to the learned toluene representation over successive gamma cycles. c, Presentation of the same occluded instance of toluene to a network trained on non-occluded toluene with excitatory, but not inhibitory, plasticity enabled. The omission of inhibitory plasticity rendered the network unable to denoise MC representations during testing. d, The Jaccard similarity between the response to occluded toluene and the learned representation of toluene systematically increased over five gamma cycles in the trained network (panel b), but not in the untrained network (panel a) or the network with inhibitory plasticity disabled (panel c). e, The Jaccard similarity increased reliably over five gamma cycles when averaged over 100 independently generated instances of occluded toluene (impulse noise P = 0.6). Error bars denote standard deviation. f, During learning, the number of GCs tuned to toluene increased over the five successive gamma cycles of training. g, Mean Jaccard similarity in the fifth gamma cycle as a function of the number of undifferentiated GCs per column. Mean similarity is averaged across 100 occluded instances of toluene (impulse noise P = 0.6); error bars denote standard deviation. Five GCs per column were utilized for all other simulations described herein.

Figure 4.

Multi-odour learning. a, Spike raster plot depicting attractor dynamics after training the network on all ten odourants. The representation generated by a sample of occluded toluene (P = 0.6; black dots) was progressively drawn towards the learned representation of toluene (open blue circles) and away from the learned representations of acetone (open red circles) and the other eight odourants (not shown). ts, timesteps. b, The Jaccard similarity to toluene that was evoked by the occluded-toluene stimulus increased over five successive gamma cycles until the stimulus was classified as toluene (similarity > 0.8). For clarity, only five odourants are depicted. c, The number of toluene-tuned GCs activated by the occluded-toluene stimulus progressively increased over five gamma cycles as the MC spiking activity pattern was attracted towards the learned toluene representation. GCs tuned to the other nine odourants were negligibly recruited by the evolving stimulus representation. d, Network activity evoked by presentation of occluded instances of each of the ten learned odours following one-shot learning. Left panels, spike raster plots over five gamma cycles (200 timesteps). Right panels, Jaccard similarity between the activity pattern generated by each occluded odourant stimulus and the learned representation of the corresponding odourant. The same network can reliably recognize all ten odourants from substantially occluded examples (P = 0.6). e, Mean classification performance across all ten odourants under increasing levels of sensory occlusion (100 impulse noise instantiations per odourant per noise level). The abscissa denotes the level of impulse noise – i.e., the proportion of MC inputs for which the sensory activation level was replaced with a random value. Red curve, proportion of correct classifications by an untrained network. Green curve, proportion of correct classifications by a network trained on all ten odourants. Blue curve, proportion of correct classifications by a trained network with the aid of a neuromodulation-dependent dynamic state trajectory. f, Effects of GC priming on classification performance under extreme occlusion. One hundred independently generated samples of occluded toluene with impulse noise P = 0.9 were presented to the fully-trained network. The putative effects of priming arising from piriform cortical projections onto bulbar GCs were modeled by lowering the spike thresholds of a fraction of toluene-tuned GCs. As the fraction of toluene-tuned GCs so activated was increased, classification performance increased from near zero to over 80% correct.

Figure 5.

Odour learning with plume dynamics. a, Ten sniffs of toluene drawn from randomly-selected timepoints within the dataset illustrate sampling variance arising from plume dynamics. Ordinate denotes MC index, ordered according to sensor locations across the wind tunnel. b, Higher-resolution depictions of sniffs #1, 4, 7, and 10 from panel a. c, Jaccard similarities between the learned representation of toluene and the activity patterns generated by plume-varying toluene stimuli across the 5 gamma cycles of each of the four sniffs depicted in panel b. d, Ten sniffs of toluene drawn from randomly-selected timepoints within the dataset and also occluded with impulse noise (P = 0.4). e, Higher-resolution depictions of sniffs #1, 4, 7, and 10 from panel d. f, Jaccard similarities between the learned representation of toluene and the activity patterns generated by plume-varying, occluded toluene stimuli across the 5 gamma cycles of each of the four sniffs depicted in panel e. g, Network activity evoked by presentation of plume-varying and occluded instances of each of the ten learned odours following one-shot learning. Left panels, spike raster plots over five gamma cycles. Right panels, Jaccard similarities between the activity pattern generated by each occluded odourant stimulus and the learned representation of the corresponding odourant. The same network reliably recognized all ten odourants from plume-varying and occluded examples. h, The Jaccard similarity to toluene that was evoked by the occluded, plume-varying toluene stimulus increased over five successive gamma cycles until the stimulus was classified as toluene (similarity > 0.8). For clarity, only five odourants are depicted. i, Mean classification performance across all ten odourants, with plume dynamics, under increasing levels of sensory occlusion (100 impulse noise instantiations per odourant per noise level). The abscissa denotes the level of impulse noise. Green curve, proportion of correct classifications by a network trained on all ten odourants.

Figure 6.

Performance evaluation. a, Classification performance of the EPL network in comparison to four other signal processing techniques. Raw, classification of unprocessed sensor signals. MF, median filter. TVF, total variation filter. PCA, principal components analysis. DAE, a seven-layer deep autoencoder. EPL, the neuromorphic EPL model. Each of the 10 odourants was presented with 100 independent instantiations of impulse noise, yielding 1000 total test samples. b, The performance of the DAE improved when it was explicitly trained to map a variety of occluded instances of each odour to a common representation. To achieve performance superior to the one-shot-trained EPL network, the DAE required 3000 occluded training samples per odourant. Abscissa, number of training samples per odourant (s/o). Ordinate, classification performance (%). c, Online learning. After training naïve EPL and DAE networks with toluene, both recognized toluene with 100% accuracy. After then training the same network with acetone, the DAE learned to recognize acetone with 100% accuracy, but was no longer able to recognize toluene (catastrophic forgetting). In contrast, the EPL network retained the ability to recognize toluene after subsequent training on acetone. d, Gradual loss of the toluene representation in the DAE during subsequent training with acetone. The ordinate denotes the similarity of the toluene-evoked activity pattern to the original toluene representation as a function of the number of training epochs for acetone. Values are the means of 100 test samples. Inset, Similarity between the toluene-evoked activity pattern and the original toluene representation in the EPL network before training with acetone (left) and after the completion of acetone training (right). e, Similarity between the toluene-evoked activity pattern and the original toluene representation as the EPL network is sequentially trained on all 10 odourants of the dataset. Values are the means of 100 test samples. f, The execution time to solution is not significantly affected as the EPL network size is expanded, reflecting the fine granularity of parallelism of the Loihi architecture. In the present implementation, one Loihi core corresponds to one OB column. g, The total energy consumed increases only modestly as the EPL network size is expanded.