Biological Mechanisms for Learning: A Computational Model of Olfactory Learning in the Manduca sexta Moth, With Applications to Neural Nets

Abstract

The insect olfactory system, which includes the antennal lobe (AL), mushroom body (MB), and ancillary structures, is a relatively simple neural system capable of learning. Its structural features, which are widespread in biological neural systems, process olfactory stimuli through a cascade of networks where large dimension shifts occur from stage to stage and where sparsity and randomness play a critical role in coding. Learning is partly enabled by a neuromodulatory reward mechanism of octopamine stimulation of the AL, whose increased activity induces synaptic weight updates in the MB through Hebbian plasticity. Enforced sparsity in the MB focuses Hebbian growth on neurons that are the most important for the representation of the learned odor. Based upon current biophysical knowledge, we have constructed an end-to-end computational firing-rate model of the Manduca sexta moth olfactory system which includes the interaction of the AL and MB under octopamine stimulation. Our model is able to robustly learn new odors, and neural firing rates in our simulations match the statistical features of in vivo firing rate data. From a biological perspective, the model provides a valuable tool for examining the role of neuromodulators, like octopamine, in learning, and gives insight into critical interactions between sparsity, Hebbian growth, and stimulation during learning. Our simulations also inform predictions about structural details of the olfactory system that are not currently well-characterized. From a machine learning perspective, the model yields bio-inspired mechanisms that are potentially useful in constructing neural nets for rapid learning from very few samples. These mechanisms include high-noise layers, sparse layers as noise filters, and a biologically-plausible optimization method to train the network based on octopamine stimulation, sparse layers, and Hebbian growth.

Putting a bug in ML: The moth olfactory network learns to read MNIST

“…where x(t) = firing rate (FR) for a neuron; w = connection weights; u = upstream neuron FRs; S() is a sigmoid function or similar; and W (t) = a brownian motion process. In MothNet this equation is modified by an inserted term to model the stimulative effect of octopamine on the AL during learning, since this effect is central to learning in the actual moth [Delahunt et al (2018b)]. The model uses a simple model of Hebbian plasticity for synaptic weight updates [Hebb (1949); Dayan and Abbott (2005); Roelfsema and Holtmaat (2018)…”

“…Sparse, high-dimensional layers are a widespread motif in biological neural systems, especially in networks related to memory and plasticity [Ganguli and Sompolinsky (2012)]. In the MothNet model, the sparse layer (MB) plays a vital role in learning because all the plastic synapses connect into or out of the sparse layer, allowing it to modulate the Hebbian updates to the synaptic connections by taking advantage of the fact that Hebbian growth is an AND gate ("fire-together, wire-together") [Delahunt et al (2018b)]. This ensures that learning boosts the important signal (i.e.…”

A Spike Time-Dependent Online Learning Algorithm Derived From Biological Olfaction

“…Models of olfactory bulb and piriform cortical activity have been applied to analyze chemosensor array data (Raman and Gutierrez-Osuna, 2005; Raman et al, 2006). Algorithms based on the insect olfactory system have been employed to learn and identify odor-like inputs (Diamond et al, 2016; Delahunt et al, 2018) as well as to identify handwritten digits—visual inputs incorporating additional low-dimensional structure (Huerta and Nowotny, 2009; Delahunt and Kutz, 2018; Diamond et al, 2019). More broadly, insect mushroom bodies in particular have been deeply studied in terms of both their pattern separation and associative learning capacities (Hige, 2018; Cayco-Gajic and Silver, 2019).…”

Built to Last: Functional and Structural Mechanisms in the Moth Olfactory Network Mitigate Effects of Neural Injury

“…It is thus an ideal model organism to investigate the injury mitigation effects of these elements. MothNet is a computational model of this olfactory network which incorporates known biophysical parameters and which was calibrated to firing rate data recorded during in vivo learning tasks [ 7 ]. MothNet models the food odor-responsive part of the moth’s olfactory network, not the sex pheromone-responsive macroglomerular complex, since the MGC has substantially distinct targets and dynamics (despite small overlaps [ 8 ]).…”