  We propose a neuroscience-inspired Solo Pass Embedded Learning Algorithm
(SPELA). SPELA is a prime candidate for training and inference applications in
Edge AI devices. At the same time, SPELA can optimally cater to the need for a
framework to study perceptual representation learning and formation. SPELA has
distinctive features such as neural priors (in the form of embedded vectors),
no weight transport, no update locking of weights, complete local Hebbian
learning, single forward pass with no storage of activations, and single weight
update per sample. Juxtaposed with traditional approaches, SPELA operates
without the need for backpropagation. We show that our algorithm can perform
nonlinear classification on a noisy boolean operation dataset. Additionally, we
exhibit high performance using SPELA across MNIST, KMNIST, and Fashion MNIST.
Lastly, we show the few-shot and 1-epoch learning capabilities of SPELA on
MNIST, KMNIST, and Fashion MNIST, where it consistently outperforms
backpropagation.
