Unsupervised Visual Feature Learning with Spike-timing-dependent Plasticity: How Far are we from Traditional Feature Learning Approaches?

Spiking neural networks (SNNs) equipped with latency coding and spike-timing dependent plasticity rules offer an alternative to solve the data and energy bottlenecks of standard computer vision approaches: they can learn visual features without supervision and can be implemented by ultra-low power hardware architectures. However, their performance in image classification has never been evaluated on recent image datasets. In this paper, we compare SNNs to auto-encoders on three visual recognition datasets, and extend the use of SNNs to color images. The analysis of the results helps us identify some bottlenecks of SNNs: the limits of on-center/off-center coding, especially for color images, and the ineffectiveness of current inhibition mechanisms. These issues should be addressed to build effective SNNs for image recognition

Sparse Codes for Speech Predict Spectrotemporal Receptive Fields in the Inferior Colliculus

We have developed a sparse mathematical representation of speech that minimizes the number of active model neurons needed to represent typical speech sounds. The model learns several well-known acoustic features of speech such as harmonic stacks, formants, onsets and terminations, but we also find more exotic structures in the spectrogram representation of sound such as localized checkerboard patterns and frequency-modulated excitatory subregions flanked by suppressive sidebands. Moreover, several of these novel features resemble neuronal receptive fields reported in the Inferior Colliculus (IC), as well as auditory thalamus and cortex, and our model neurons exhibit the same tradeoff in spectrotemporal resolution as has been observed in IC. To our knowledge, this is the first demonstration that receptive fields of neurons in the ascending mammalian auditory pathway beyond the auditory nerve can be predicted based on coding principles and the statistical properties of recorded sounds.Comment: For Supporting Information, see PLoS website: http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.100259

Neuromorphic Computing Applications in Robotics

Deep learning achieves remarkable success through training using massively labeled datasets. However, the high demands on the datasets impede the feasibility of deep learning in edge computing scenarios and suffer from the data scarcity issue. Rather than relying on labeled data, animals learn by interacting with their surroundings and memorizing the relationships between events and objects. This learning paradigm is referred to as associative learning. The successful implementation of associative learning imitates self-learning schemes analogous to animals which resolve the challenges of deep learning. Current state-of-the-art implementations of associative memory are limited to simulations with small-scale and offline paradigms. Thus, this work implements associative memory with an Unmanned Ground Vehicle (UGV) and neuromorphic hardware, specifically Intel’s Loihi, for an online learning scenario. This system emulates the classic associative learning in rats using the UGV in place of the rats. In specific, it successfully reproduces the fear conditioning with no pretraining procedure or labeled datasets. The UGV is rendered capable of autonomously learning the cause-and-effect relationship of the light stimulus and vibration stimulus and exhibiting a movement response to demonstrate the memorization. Hebbian learning dynamics are used to update the synaptic weights during the associative learning process. The Intel Loihi chip is integrated with this online learning system for processing visual signals with a specialized neural assembly. While processing, the Loihi’s average power usages for computing logic and memory are 30 mW and 29 mW, respectively

Biologically plausible deep learning -- but how far can we go with shallow networks?

Training deep neural networks with the error backpropagation algorithm is considered implausible from a biological perspective. Numerous recent publications suggest elaborate models for biologically plausible variants of deep learning, typically defining success as reaching around 98% test accuracy on the MNIST data set. Here, we investigate how far we can go on digit (MNIST) and object (CIFAR10) classification with biologically plausible, local learning rules in a network with one hidden layer and a single readout layer. The hidden layer weights are either fixed (random or random Gabor filters) or trained with unsupervised methods (PCA, ICA or Sparse Coding) that can be implemented by local learning rules. The readout layer is trained with a supervised, local learning rule. We first implement these models with rate neurons. This comparison reveals, first, that unsupervised learning does not lead to better performance than fixed random projections or Gabor filters for large hidden layers. Second, networks with localized receptive fields perform significantly better than networks with all-to-all connectivity and can reach backpropagation performance on MNIST. We then implement two of the networks - fixed, localized, random & random Gabor filters in the hidden layer - with spiking leaky integrate-and-fire neurons and spike timing dependent plasticity to train the readout layer. These spiking models achieve > 98.2% test accuracy on MNIST, which is close to the performance of rate networks with one hidden layer trained with backpropagation. The performance of our shallow network models is comparable to most current biologically plausible models of deep learning. Furthermore, our results with a shallow spiking network provide an important reference and suggest the use of datasets other than MNIST for testing the performance of future models of biologically plausible deep learning.Comment: 14 pages, 4 figure

Fast and Accurate Sparse Coding of Visual Stimuli with a Simple, Ultra-Low-Energy Spiking Architecture

Memristive crossbars have become a popular means for realizing unsupervised and supervised learning techniques. Often, to preserve mathematical rigor, the crossbar itself is separated from the neuron capacitors. In this work, we sought to simplify the design, removing extraneous components to consume significantly lower power at a minimal cost of accuracy. This work provides derivations for the design of such a network, named the Simple Spiking Locally Competitive Algorithm, or SSLCA, as well as CMOS designs and results on the CIFAR and MNIST datasets. Compared to a non-spiking model which scored 33% on CIFAR-10 with a single-layer classifier, this hardware scored 32% accuracy. When used with a state-of-the-art deep learning classifier, the non-spiking model achieved 82% and our simplified, spiking model achieved 80%, while compressing the input data by 79%. Compared to a previously proposed spiking model, our proposed hardware consumed 99% less energy to do the same work at 21 times the throughput. Accuracy held out with online learning to a write variance of 3% and a read variance of 40%. The proposed architecture\u27s excellent accuracy and significantly lower energy usage demonstrate the utility of our innovations. This work provides a means for extremely low-energy sparse coding in mobile devices, such as cellular phones, or for very sparse coding as is needed by self-driving cars or robotics that must integrate data from multiple, high-resolution sensors

The Design of a Simple, Spiking Sparse Coding Algorithm for Memristive Hardware

Calculating a sparse code for signals with high dimensionality, such as high-resolution images, takes substantial time to compute on a traditional computer architecture. Memristors present the opportunity to combine storage and computing elements into a single, compact device, drastically reducing the area required to perform these calculations. This work focused on the analysis of two existing sparse coding architectures, one of which utilizes memristors, as well as the design of a new, third architecture that employs a memristive crossbar. These architectures implement either a non-spiking or spiking variety of sparse coding based on the Locally Competitive Algorithm (LCA) introduced by Rozell et al. in 2008. Each architecture receives an arbitrary number of input lines and drives an arbitrary number of output lines. Training of the dictionary used for the sparse code was implemented through external control signals that approximate Oja\u27s rule. The resulting designs were capable of representing input in real-time: no resets would be needed between frames of a video, for instance, though some settle time would be needed. The spiking architecture proposed is novel, emphasizing simplicity to achieve lower power than existing designs. The architectures presented were tested for their ability to encode and reconstruct 8 x 8 patches of natural images. The proposed network reconstructed patches with a normalized, root-mean-square error of 0.13, while a more complicated CMOS-only approach yielded 0.095, and a non-spiking approach yielded 0.074. Several outputs competing for representation of the input was shown to improve reconstruction quality and preserve more subtle components in the final encoding; the proposed algorithm lacks this feature. Steps to address this were proposed for future work by scaling input spikes according to the current expected residual, without adding much complexity. The architectures were also tested with the MNIST digit database, passing a sparse code onto a basic classifier. The proposed architecture scored 81% on this test, a CMOS-only spiking variant scored 76%, and the non-spiking algorithm scored 85%. Power calculations were made for each design and compared against other publications. The overall findings showed great promise for spiking memristor-based ASICs, consuming only 28% of the power used by non-spiking architectures and 6.6% as much power as a CMOS-only spiking architecture on this task. The spike-based nature of the novel design was also parameterized into several intuitive parameters that could be adjusted to prefer either performance or power efficiency. The design and analysis of architectures for sparse coding should greatly reduce the amount of future work needed to implement an end-to-end classification pipeline for images or other signal data. When lower power is a primary concern, the proposed architecture should be considered as it surpassed other published algorithms. These pipelines could be used to provide low-power visual assistance, highlighting objects within high-definition video frames in real-time. The technology could also be used to help self-driving cars identify hazards more quickly and efficiently

Brain-Inspired Computational Intelligence via Predictive Coding

Artificial intelligence (AI) is rapidly becoming one of the key technologies of this century. The majority of results in AI thus far have been achieved using deep neural networks trained with the error backpropagation learning algorithm. However, the ubiquitous adoption of this approach has highlighted some important limitations such as substantial computational cost, difficulty in quantifying uncertainty, lack of robustness, unreliability, and biological implausibility. It is possible that addressing these limitations may require schemes that are inspired and guided by neuroscience theories. One such theory, called predictive coding (PC), has shown promising performance in machine intelligence tasks, exhibiting exciting properties that make it potentially valuable for the machine learning community: PC can model information processing in different brain areas, can be used in cognitive control and robotics, and has a solid mathematical grounding in variational inference, offering a powerful inversion scheme for a specific class of continuous-state generative models. With the hope of foregrounding research in this direction, we survey the literature that has contributed to this perspective, highlighting the many ways that PC might play a role in the future of machine learning and computational intelligence at large.Comment: 37 Pages, 9 Figure

CSNNs: Unsupervised, Backpropagation-free Convolutional Neural Networks for Representation Learning

This work combines Convolutional Neural Networks (CNNs), clustering via Self-Organizing Maps (SOMs) and Hebbian Learning to propose the building blocks of Convolutional Self-Organizing Neural Networks (CSNNs), which learn representations in an unsupervised and Backpropagation-free manner. Our approach replaces the learning of traditional convolutional layers from CNNs with the competitive learning procedure of SOMs and simultaneously learns local masks between those layers with separate Hebbian-like learning rules to overcome the problem of disentangling factors of variation when filters are learned through clustering. We investigate the learned representation by designing two simple models with our building blocks, achieving comparable performance to many methods which use Backpropagation, while we reach comparable performance on Cifar10 and give baseline performances on Cifar100, Tiny ImageNet and a small subset of ImageNet for Backpropagation-free methods.Comment: 18 pages,18 figures, Author's extended version of the paper. Final version presented at 18th IEEE International Conference on Machine Learning and Applications (ICMLA). Boca Raton, Florida / USA. 201