Dictionary Learning with Accumulator Neurons

05/30/2022
by   Gavin Parpart, et al.
0

The Locally Competitive Algorithm (LCA) uses local competition between non-spiking leaky integrator neurons to infer sparse representations, allowing for potentially real-time execution on massively parallel neuromorphic architectures such as Intel's Loihi processor. Here, we focus on the problem of inferring sparse representations from streaming video using dictionaries of spatiotemporal features optimized in an unsupervised manner for sparse reconstruction. Non-spiking LCA has previously been used to achieve unsupervised learning of spatiotemporal dictionaries composed of convolutional kernels from raw, unlabeled video. We demonstrate how unsupervised dictionary learning with spiking LCA (S-LCA) can be efficiently implemented using accumulator neurons, which combine a conventional leaky-integrate-and-fire (LIF) spike generator with an additional state variable that is used to minimize the difference between the integrated input and the spiking output. We demonstrate dictionary learning across a wide range of dynamical regimes, from graded to intermittent spiking, for inferring sparse representations of both static images drawn from the CIFAR database as well as video frames captured from a DVS camera. On a classification task that requires identification of the suite from a deck of cards being rapidly flipped through as viewed by a DVS camera, we find essentially no degradation in performance as the LCA model used to infer sparse spatiotemporal representations migrates from graded to spiking. We conclude that accumulator neurons are likely to provide a powerful enabling component of future neuromorphic hardware for implementing online unsupervised learning of spatiotemporal dictionaries optimized for sparse reconstruction of streaming video from event based DVS cameras.

READ FULL TEXT

page 4

page 5

page 7

research
11/05/2021

Efficient Neuromorphic Signal Processing with Loihi 2

The biologically inspired spiking neurons used in neuromorphic computing...
research
11/16/2016

Training Spiking Deep Networks for Neuromorphic Hardware

We describe a method to train spiking deep networks that can be run usin...
research
11/05/2013

Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been ...
research
10/29/2015

Spiking Deep Networks with LIF Neurons

We train spiking deep networks using leaky integrate-and-fire (LIF) neur...
research
11/08/2022

Spiking sampling network for image sparse representation and dynamic vision sensor data compression

Sparse representation has attracted great attention because it can great...
research
02/08/2019

Learning spatially-correlated temporal dictionaries for calcium imaging

Calcium imaging has become a fundamental neural imaging technique, aimin...
research
05/23/2018

Dictionary Learning by Dynamical Neural Networks

A dynamical neural network consists of a set of interconnected neurons t...

Please sign up or login with your details

Forgot password? Click here to reset