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The state-of-the-art in machine learning has been achieved primarily by deep learning artificial neural networks. These networks are powerful but biologically implausible and energy intensive. In parallel, a new paradigm of neural network is being researched that can alleviate some of the computational and energy issues. These networks, spiking neural networks (SNNs), have transformative potential if the community is able to bridge the gap between deep learning and SNNs. However, SNNs are notoriously difficult to train and lack precision in their communication. In an effort to overcome these limitations and retain the benefits of the learning process in deep learning, we investigate novel ways to translate between them. We construct several network designs with varying degrees of biological plausibility. We then test our designs on an image classification task and demonstrate our designs allow for a customized tradeoff between biological plausibility, power efficiency, inference time, and accuracy. 

While deep learning continues to permeate through all fields of signal processing and machine learning, a critical exploit in these frameworks exists and remains unsolved. These exploits, or adversarial examples, are a type of signal attack that can change the output class of a classifier by perturbing the stimulus signal by an imperceptible amount. The attack takes advantage of statistical irregularities within the training data, where the added perturbations can move the image across deep learning decision boundaries. What is even more alarming is the transferability of these attacks to different deep learning models and architectures. This means a successful attack on one model has adversarial effects on other, unrelated models. In a general sense, adversarial attack through perturbations is not a machine learning vulnerability. Human and biological vision can also be fooled by various methods, i.e. mixing high and low frequency images together, by altering semantically related signals, or by sufficiently distorting the input signal. However, the amount and magnitude of such a distortion required to alter biological perception is at a much larger scale. In this work, we explored this gap through the lens of biology and neuroscience in order to understand the robustness exhibited in human perception. Our experiments show that by leveraging sparsity and modeling the biological mechanisms at a cellular level, we are able to mitigate the effect of adversarial alterations to the signal that have no perceptible meaning. Furthermore, we present and illustrate the effects of top-down functional processes that contribute to the inherent immunity in human perception in the context of exploiting these properties to make a more robust machine vision system. 

Adversarial images are a class of images that have been slightly altered by very specific noise to change the way a deep learning neural network classifies the image. In many cases, this particular noise is imperceptible to the human vision system and thus presents a vulnerability of significant concern to the machine learning and artificial intelligence community. Research towards mitigating this type of attack has taken many forms, one of which is to filter or post process the image before classifying the image with a deep neural network. Techniques such as smoothing, filtering, and compression have been used with varying levels of success. In our work, we explored the use of a neuromorphic software and hardware approach as a protection against adversarial image attack. The algorithm governing our neuromorphic approach is based upon sparse coding. Our sparse coding approach is solved using a dynamic system of equations that models biological low level vision. Our quantitative and qualitative results show that a sparse coding reconstruction is remarkably invariant to changes in sparsity and reconstruction error with respect to classification accuracy. Furthermore, our approach is able to maintain low reconstruction errors without sacrificing classification performance. 

Our brains are, “prediction machines”, where we are continuously comparing our surroundings with predictions from internal models generated by our brains. This is demonstrated by observing our basic low level sensory systems and how they predict environmental changes as we move through space and time. Indeed, even at higher cognitive levels, we are able to do prediction. We can predict how the laws of physics affect people, places, and things and even predict the end of someone’s sentence. In our work, we sought to create an artificial model that is able to mimic early, low level biological predictive behavior in a computer vision system. Our predictive vision model uses spatiotemporal sequence memories learned from deep sparse coding. This model is implemented using a biologically inspired architecture: one that utilizes sequence memories, lateral inhibition, and top-down feed- back in a generative framework. Our model learns the causes of the data in a completely unsupervised manner, by simply observing and learning about the world. Spatiotemporal features are learned by minimizing a reconstruction error convolved over space and time, and can subsequently be used for recognition, classification, and future video prediction. Our experiments show that we are able to accurately predict what will happen in the future; furthermore, we can use our predictions to detect anomalous, unexpected events in both synthetic and real video sequences. 

The optic nerve transmits visual information to the brain as trains of discrete events, a low-power, low-bandwidth communication channel also exploited by silicon retina cameras. Extracting highfidelity visual input from retinal event trains is thus a key challenge for both computational neuroscience and neuromorphic engineering. Here, we investigate whether sparse coding can enable the reconstruction of high-fidelity images and video from retinal event trains. Our approach is analogous to compressive sensing, in which only a random subset of pixels are transmitted and the missing information is estimated via inference. We employed a variant of the Locally Competitive Algorithm to infer sparse representations from retinal event trains, using a dictionary of convolutional features optimized via stochastic gradient descent and trained in an unsupervised manner using a local Hebbian learning rule with momentum. We used an anatomically realistic retinal model with stochastic graded release from cones and bipolar cells to encode thumbnail images as spike trains arising from ON and OFF retinal ganglion cells. The spikes from each model ganglion cell were summed over a 32 msec time window, yielding a noisy rate-coded image. Analogous to how the primary visual cortex is postulated to infer features from noisy spike trains arising from the optic nerve, we inferred a higher-fidelity sparse reconstruction from the noisy rate-coded image using a convolutional dictionary trained on the original CIFAR10 database. To investigate whether a similar approachworks on non-stochastic data, we demonstrate that the same procedure can be used to reconstruct high-frequency video from the asynchronous events arising from a silicon retina camera moving through a laboratory environment. 

A new class of neuromorphic processors promises to provide fast and power-efficient execution of spiking neural networks with on-chip synaptic plasticity. This efficiency derives in part from the fine-grained parallelism as well as event-driven communication mediated by spatially and temporally sparse spike messages. Another source of efficiency arises from the close spatial proximity between synapses and the sites where their weights are applied and updated. This proximity of compute and memory elements drastically reduces expensive data movements but imposes the constraint that only local operations can be efficiently performed, similar to constraints present in biological neural circuits. Efficient weight update operations should therefore only depend on information available locally at each synapse as non-local operations that involve copying, taking a transpose, or normalizing an entire weight matrix are not efficiently supported by present neuromorphic architectures. Moreover, spikes are typically non-negative events, which imposes additional constraints on how local weight update operations can be performed. The Locally Competitive Algorithm (LCA) is a dynamical sparse solver that uses only local computations between non-spiking leaky integrator neurons, allowing for massively parallel implementations on compatible neuromorphic architectures such as Intel's Loihi research chip. It has been previously demonstrated that non-spiking LCA can be used to learn dictionaries of convolutional kernels in an unsupervised manner from raw, unlabeled input, although only by employing non-local computation and signed non-spiking outputs. Here, we show how unsupervised dictionary learning with spiking LCA (S-LCA) can be implemented using only local computation and unsigned spike events, providing a promising strategy for constructing self-organizing neuromorphic chips. 

Interface‐type (IT) metal/oxide Schottky memristive devices have attracted considerable attention over filament‐type (FT) devices for neuromorphic computing because of their uniform, filament‐free, and analog resistive switching (RS) characteristics. The most recent IT devices are based on oxygen ions and vacancies movement to alter interfacial Schottky barrier parameters and thereby control RS properties. However, the reliability and stability of these devices have been significantly affected by the undesired diffusion of ionic species. Herein, a reliable interface‐dominated memristive device is demonstrated using a simple Au/Nb‐doped SrTiO₃(Nb:STO) Schottky structure. The Au/Nb:STO Schottky barrier modulation by charge trapping and detrapping is responsible for the analog resistive switching characteristics. Because of its interface‐controlled RS, the proposed device shows low device‐to‐device, cell‐to‐cell, and cycle‐to‐cycle variability while maintaining high repeatability and stability during endurance and retention tests. Furthermore, the Au/Nb:STO IT memristive device exhibits versatile synaptic functions with an excellent uniformity, programmability, and reliability. A simulated artificial neural network with Au/Nb:STO synapses achieves a high recognition accuracy of 94.72% for large digit recognition from MNIST database. These results suggest that IT resistive switching can be potentially used for artificial synapses to build next‐generation neuromorphic computing.