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A model of neurobiologically plausible least-squares learning in visual cortex | IEEE Conference Publication | IEEE Xplore
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A model of neurobiologically plausible least-squares learning in visual cortex


Abstract:

We show mathematically how a pseudo-inverse approach to training an artificial neural network for classification using a mean square error loss function and large batches...Show More

Abstract:

We show mathematically how a pseudo-inverse approach to training an artificial neural network for classification using a mean square error loss function and large batches of training data can be recast as an iterative method that circuits of real neurons in visual cortex could plausibly learn in an online manner. We argue the case for neurobiological plausibility based on our illustration that the iterative method can be reformulated as an unsupervised stage that learns to decorrelate using an anti-Hebbian learning rule, and a supervised stage that follows simple Hebbian learning while retaining mean square error optimality. Importantly, we modify the baseline method to ensure the learning rules rely on information that would be available locally at synapses if the method were instantiated in a network of cortical neurons. We demonstrate results comparable on par or better than similar iterative methods applied to the MNIST hand-written digits image classification benchmark, and the related but more challenging EMNIST database. The proposed learning algorithm learns quickly, reaching good accuracy with a small amount of training data.
Date of Conference: 08-13 July 2018
Date Added to IEEE Xplore: 14 October 2018
ISBN Information:
Electronic ISSN: 2161-4407
Conference Location: Rio de Janeiro, Brazil

References

References is not available for this document.