Neuromorphic Engineering Editors' Pick 2021

This collection showcases well-received spontaneous articles from the past couple of years, which have been specially handpicked by our Chief Editors, Profs. André van Schaik and Bernabé Linares-Barranco. The work presented here highlights the broad diversity of research performed across the section and aims to put a spotlight on the main areas of interest. All research presented here displays strong advances in theory, experiment, and methodology with applications to compelling problems. This collection aims to further support Frontiers’ strong community by recognizing highly deserving authors

A Review of Findings from Neuroscience and Cognitive Psychology as Possible Inspiration for the Path to Artificial General Intelligence

This review aims to contribute to the quest for artificial general intelligence by examining neuroscience and cognitive psychology methods for potential inspiration. Despite the impressive advancements achieved by deep learning models in various domains, they still have shortcomings in abstract reasoning and causal understanding. Such capabilities should be ultimately integrated into artificial intelligence systems in order to surpass data-driven limitations and support decision making in a way more similar to human intelligence. This work is a vertical review that attempts a wide-ranging exploration of brain function, spanning from lower-level biological neurons, spiking neural networks, and neuronal ensembles to higher-level concepts such as brain anatomy, vector symbolic architectures, cognitive and categorization models, and cognitive architectures. The hope is that these concepts may offer insights for solutions in artificial general intelligence.Comment: 143 pages, 49 figures, 244 reference

An Oscillatory Neural Autoencoder Based on Frequency Modulation and Multiplexing

Oscillatory phenomena are ubiquitous in the brain. Although there are oscillator-based models of brain dynamics, their universal computational properties have not been explored much unlike in the case of rate-coded and spiking neuron network models. Use of oscillator-based models is often limited to special phenomena like locomotor rhythms and oscillatory attractor-based memories. If neuronal ensembles are taken to be the basic functional units of brain dynamics, it is desirable to develop oscillator-based models that can explain a wide variety of neural phenomena. Autoencoders are a special type of feed forward networks that have been used for construction of large-scale deep networks. Although autoencoders based on rate-coded and spiking neuron networks have been proposed, there are no autoencoders based on oscillators. We propose here an oscillatory neural network model that performs the function of an autoencoder. The model is a hybrid of rate-coded neurons and neural oscillators. Input signals modulate the frequency of the neural encoder oscillators. These signals are then multiplexed using a network of rate-code neurons that has afferent Hebbian and lateral anti-Hebbian connectivity, termed as Lateral Anti Hebbian Network (LAHN). Finally the LAHN output is de-multiplexed using an output neural layer which is a combination of adaptive Hopf and Kuramoto oscillators for the signal reconstruction. The Kuramoto-Hopf combination performing demodulation is a novel way of describing a neural phase-locked loop. The proposed model is tested using both synthetic signals and real world EEG signals. The proposed model arises out of the general motivation to construct biologically inspired, oscillatory versions of some of the standard neural network models, and presents itself as an autoencoder network based on oscillatory neurons applicable to time series signals. As a demonstration, the model is applied to compression of EEG signals

Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry

The backpropagation algorithm has experienced remarkable success in training large-scale artificial neural networks; however, its biological plausibility has been strongly criticized, and it remains an open question whether the brain employs supervised learning mechanisms akin to it. Here, we propose correlative information maximization between layer activations as an alternative normative approach to describe the signal propagation in biological neural networks in both forward and backward directions. This new framework addresses many concerns about the biological-plausibility of conventional artificial neural networks and the backpropagation algorithm. The coordinate descent-based optimization of the corresponding objective, combined with the mean square error loss function for fitting labeled supervision data, gives rise to a neural network structure that emulates a more biologically realistic network of multi-compartment pyramidal neurons with dendritic processing and lateral inhibitory neurons. Furthermore, our approach provides a natural resolution to the weight symmetry problem between forward and backward signal propagation paths, a significant critique against the plausibility of the conventional backpropagation algorithm. This is achieved by leveraging two alternative, yet equivalent forms of the correlative mutual information objective. These alternatives intrinsically lead to forward and backward prediction networks without weight symmetry issues, providing a compelling solution to this long-standing challenge

Rapid Bayesian learning in the mammalian olfactory system

Many experimental studies suggest that animals can rapidly learn to identify odors and predict the rewards associated with them. However, the underlying plasticity mechanism remains elusive. In particular, it is not clear how olfactory circuits achieve rapid, data efficient learning with local synaptic plasticity. Here, we formulate olfactory learning as a Bayesian optimization process, then map the learning rules into a computational model of the mammalian olfactory circuit. The model is capable of odor identification from a small number of observations, while reproducing cellular plasticity commonly observed during development. We extend the framework to reward-based learning, and show that the circuit is able to rapidly learn odor-reward association with a plausible neural architecture. These results deepen our theoretical understanding of unsupervised learning in the mammalian brain

A HIERARCHY BASED ACOUSTIC FRAMEWORK FOR AUDITORY SCENE ANALYSIS

The acoustic environment surrounding us is extremely dynamic and unstructured in nature. Humans exhibit a great ability at navigating these complex acoustic environments, and can parse a complex acoustic scene into its perceptually meaningful objects, referred to as ``auditory scene analysis". Current neuro-computational strategies developed for auditory scene analysis related tasks are primarily based on prior knowledge of acoustic environment and hence, fail to match human performance under realistic settings, i.e. the acoustic environment being dynamic in nature and presence of multiple competing auditory objects in the same scene. In this thesis, we explore hierarchy based computational frameworks that not only solve different auditory scene analysis related paradigms but also explain the processes driving these paradigms from physiological, psychophysical and computational viewpoint. In the first part of the thesis, we explore computational strategies that can extract varying degree of details from complex acoustic scene with an aim to capture non-trivial commonalities within a sound class as well as differences across sound classes. We specifically demonstrate that a rich feature space of spectro-temporal modulation representation complimented with markovian based temporal dynamics information captures the fine and subtle changes in the spectral and temporal structure of sound events in a complex and dynamic acoustic environment. We further extend this computational model to incorporate a biologically plausible network capable of learning a rich hierarchy of localized spectro-temporal bases and their corresponding long term temporal regularities from natural soundscape in a data driven fashion. We demonstrate that the unsupervised nature of the network yields physiologically and perceptually meaningful tuning functions that drive the organization of acoustic scene into distinct auditory objects. Next, we explore computational models based on hierarchical acoustic representation in the context of bottom-up salient event detection. We demonstrate that a rich hierarchy of local and global cues capture the salient details upon which the bottom-up saliency mechanisms operate to make a "new" event pop out in a complex acoustic scene. We further show that a top-down event specific knowledge gathered by scene classification framework biases bottom-up computational resources towards events of "interest" rather than any new event. We further extend the top-down framework in the context of modeling a broad and heterogeneous acoustic class. We demonstrate that when an acoustic scene comprises of multiple events, modeling the global details in the hierarchy as a mixture of temporal trajectories help to capture its semantic categorization and provide a detailed understanding of the scene. Overall, the results of this thesis improve our understanding of how a rich hierarchy of acoustic representation drives various auditory scene analysis paradigms and how to integrate multiple theories of scene analysis into a unified strategy, hence providing a platform for further development of computational scene analysis research