How can neuromorphic hardware attain brain-like functional capabilities?

Research on neuromorphic computing is driven by the vision that we can emulate brain-like computing capability, learning capability, and energy-efficiency in novel hardware. Unfortunately, this vision has so far been pursued in a half-hearted manner. Most current neuromorphic hardware (NMHW) employs brain-like spiking neurons instead of standard artificial neurons. This is a good first step, which does improve the energy-efficiency of some computations, see \citep{rao2022long} for one of many examples. But current architectures and training methods for networks of spiking neurons in NMHW are largely copied from artificial neural networks. Hence it is not surprising that they inherit many deficiencies of artificial neural networks, rather than attaining brain-like functional capabilities. Of course, the brain is very complex, and we cannot implement all its details in NMHW. Instead, we need to focus on principles that are both easy to implement in NMHW and are likely to support brain-like functionality. The goal of this article is to highlight some of them.

Pairing Conceptual Modeling with Machine Learning

Both conceptual modeling and machine learning have long been recognized as important areas of research. With the increasing emphasis on digitizing and processing large amounts of data for business and other applications, it would be helpful to consider how these areas of research can complement each other. To understand how they can be paired, we provide an overview of machine learning foundations and development cycle. We then examine how conceptual modeling can be applied to machine learning and propose a framework for incorporating conceptual modeling into data science projects. The framework is illustrated by applying it to a healthcare application. For the inverse pairing, machine learning can impact conceptual modeling through text and rule mining, as well as knowledge graphs. The pairing of conceptual modeling and machine learning in this this way should help lay the foundations for future research.

A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware

In spite of intensive efforts it has remained an open problem to what extent current Artificial Intelligence (AI) methods that employ Deep Neural Networks (DNNs) can be implemented more energy-efficiently on spike-based neuromorphic hardware. This holds in particular for AI methods that solve sequence processing tasks, a primary application target for spike-based neuromorphic hardware. One difficulty is that DNNs for such tasks typically employ Long Short-Term Memory (LSTM) units. Yet an efficient emulation of these units in spike-based hardware has been missing. We present a biologically inspired solution that solves this problem. This solution enables us to implement a major class of DNNs for sequence processing tasks such as time series classification and question answering with substantial energy savings on neuromorphic hardware. In fact, the Relational Network for reasoning about relations between objects that we use for question answering is the first example of a large DNN that carries out a sequence processing task with substantial energy-saving on neuromorphic hardware.

CCN GAC Workshop: Issues with learning in biological recurrent neural networks

This perspective piece came about through the Generative Adversarial Collaboration (GAC) series of workshops organized by the Computational Cognitive Neuroscience (CCN) conference in 2020. We brought together a number of experts from the field of theoretical neuroscience to debate emerging issues in our understanding of how learning is implemented in biological recurrent neural networks. Here, we will give a brief review of the common assumptions about biological learning and the corresponding findings from experimental neuroscience and contrast them with the efficiency of gradient-based learning in recurrent neural networks commonly used in artificial intelligence. We will then outline the key issues discussed in the workshop: synaptic plasticity, neural circuits, theory-experiment divide, and objective functions. Finally, we conclude with recommendations for both theoretical and experimental neuroscientists when designing new studies that could help to bring clarity to these issues.

Online spatio-temporal learning in deep neural networks

Biological neural networks are equipped with an inherent capability to continuously adapt through online learning. This aspect remains in stark contrast to learning with error backpropagation through time (BPTT) applied to recurrent neural networks (RNNs), or recently even to biologically-inspired spiking neural networks (SNNs), because the unrolling through time of BPTT leads to system-locking problems. Online learning has recently regained the attention of the research community, focusing either on approaches that approximate BPTT or on biologically-plausible schemes applied in SNNs. Here we present an alternative perspective that is based on a clear separation of spatial and temporal gradient components. Combined with insights from biology, we derive from first principles a novel online learning algorithm, called online spatio-temporal learning (OSTL), which is gradient-equivalent to BPTT for shallow networks. We apply OSTL to SNNs allowing them for the first time to be trained online with BPTT-equivalent gradients. In addition, the proposed formulation uncovers a class of SNN architectures trainable online at low complexity. Moreover, we extend OSTL to deep networks while maintaining its key characteristics. Besides SNNs, the generic form of OSTL is applicable to a wide range of network architectures, including networks comprising long short-term memory (LSTM) and gated recurrent units (GRU). We demonstrate the operation of our algorithm on various tasks from language modelling to speech recognition, and obtain results on par with the BPTT baselines. The proposed algorithm provides a framework for developing succinct and efficient online training approaches for SNNs and in general deep RNNs.

Embodied Synaptic Plasticity with Online Reinforcement learning

The endeavor to understand the brain involves multiple collaborating research fields. Classically, synaptic plasticity rules derived by theoretical neuroscientists are evaluated in isolation on pattern classification tasks. This contrasts with the biological brain which purpose is to control a body in closed-loop. This paper contributes to bringing the fields of computational neuroscience and robotics closer together by integrating open-source software components from these two fields. The resulting framework allows to evaluate the validity of biologically-plausibe plasticity models in closed-loop robotics environments. We demonstrate this framework to evaluate Synaptic Plasticity with Online REinforcement learning (SPORE), a reward-learning rule based on synaptic sampling, on two visuomotor tasks: reaching and lane following. We show that SPORE is capable of learning to perform policies within the course of simulated hours for both tasks. Provisional parameter explorations indicate that the learning rate and the temperature driving the stochastic processes that govern synaptic learning dynamics need to be regulated for performance improvements to be retained. We conclude by discussing the recent deep reinforcement learning techniques which would be beneficial to increase the functionality of SPORE on visuomotor tasks.

Classifying Images with Few Spikes per Neuron

Spiking neural networks (SNNs) promise to provide AI implementations with a drastically reduced energy budget in comparison with standard artificial neural networks (ANNs). Besides recurrent SNN modules that can be efficiently trained on-chip, many AI applications require the use of feedforward convolutional neural networks (CNNs) as preprocessors for visual or other sensory inputs. The standard solution has been to train a CNN consisting of non-spiking neurons, typically using the rectified linear ReLU function as activation function, and then to translate these CNNs with ReLU neurons via rate coding into SNNs. However this produces SNNs with long latency and small throughput, since the number of spikes that a neuron has to emit is on the order of the number N of output values of the corresponding CNN gate which subsequent layers need to be able to distinguish. We introduce a new ANN-SNN conversion - called FS-conversion - that needs only log N many time steps for that, which is optimal from the perspective of information theory. This can be achieved with a simple variation of the spiking neuron model that has no membrane leak but an exponentially decreasing firing threshold. We show that for the classification of images from ImageNet and CIFAR10 this new conversion reduces latency and drastically increases the throughput compared with rate-based conversion, while achieving almost the same classification performance as the ANN.

Recognizing Images with at most one Spike per Neuron

In order to port the performance of trained artificial neural networks (ANNs) to spiking neural networks (SNNs), which can be implemented in neuromorphic hardware with a drastically reduced energy consumption, an efficient ANN to SNN conversion is needed. Previous conversion schemes focused on the representation of the analog output of a rectified linear (ReLU) gate in the ANN by the firing rate of a spiking neuron. But this is not possible for other commonly used ANN gates, and it reduces the throughput even for ReLU gates. We introduce a new conversion method where a gate in the ANN, which can basically be of any type, is emulated by a small circuit of spiking neurons, with At Most One Spike (AMOS) per neuron. We show that this AMOS conversion improves the accuracy of SNNs for ImageNet from 74.60% to 80.97%, thereby bringing it within reach of the best available ANN accuracy (85.0%). The Top5 accuracy of SNNs is raised to 95.82%, getting even closer to the best Top5 performance of 97.2% for ANNs. In addition, AMOS conversion improves latency and throughput of spike-based image classification by several orders of magnitude. Hence these results suggest that SNNs provide a viable direction for developing highly energy efficient hardware for AI that combines high performance with versatility of applications.