Deep Learning in Neuronal and Neuromorphic Systems

The ever-increasing compute and energy requirements in the field of deep learning have caused a rising interest in the development of novel, more energy-efficient computing paradigms to support the advancement of artificial intelligence systems. Neuromorphic architectures are promising candidates, as they aim to mimic the functional mechanisms, and thereby inherit the efficiency, of their archetype: the brain. However, even though neuromorphics and deep learning are, at their roots, inspired by the brain, they are not directly compatible with each other. In this thesis, we aim at bridging this gap by realizing error backpropagation, the central algorithm behind deep learning, on neuromorphic platforms. We start by introducing the Yin-Yang classification dataset, a tool for neuromorphic and algorithmic prototyping, as a prerequisite for the other work presented. This novel dataset is designed to not require excessive hardware or computing resources to be solved. At the same time, it is challenging enough to be useful for debugging and testing by revealing potential algorithmic or implementation flaws. We then explore two different approaches of implementing error backpropagation on neuromorphic systems. Our first solution provides an exact algorithm for error backpropagation on the first spike times of leaky integrate-andfire neurons, one of the most common neuron models implemented in neuromorphic chips. The neuromorphic feasibility is demonstrated by the deployment on the BrainScaleS-2 chip and yields competitive results both with respect to task performance as well as efficiency. The second approach is based on a biologically plausible variant of error backpropagation realized by a dendritc microcircuit model. We assess this model with respect to its practical feasibility, extend it to improve learning performance and address the obstacles for neuromorphic implementation: We introduce the Latent Equilibrium mechanism to solve the relaxation problem introduced by slow neuron dynamics. Our Phaseless Alignment Learning method allows us to learn feedback weights in the network and thus avoid the weight transport problem. And finally, we explore two methods to port the rate-based model onto an event-based neuromorphic system. The presented work showcases two ways of uniting the powerful and flexible learning mechanisms of deep learning with energy-efficient neuromorphic systems, thus illustrating the potential of a convergence of artificial intelligence and neuromorphic engineering research

A Biologically Plausible Learning Rule for Deep Learning in the Brain

Researchers have proposed that deep learning, which is providing important progress in a wide range of high complexity tasks, might inspire new insights into learning in the brain. However, the methods used for deep learning by artificial neural networks are biologically unrealistic and would need to be replaced by biologically realistic counterparts. Previous biologically plausible reinforcement learning rules, like AGREL and AuGMEnT, showed promising results but focused on shallow networks with three layers. Will these learning rules also generalize to networks with more layers and can they handle tasks of higher complexity? We demonstrate the learning scheme on classical and hard image-classification benchmarks, namely MNIST, CIFAR10 and CIFAR100, cast as direct reward tasks, both for fully connected, convolutional and locally connected architectures. We show that our learning rule - Q-AGREL - performs comparably to supervised learning via error-backpropagation, with this type of trial-and-error reinforcement learning requiring only 1.5-2.5 times more epochs, even when classifying 100 different classes as in CIFAR100. Our results provide new insights into how deep learning may be implemented in the brain

Learning cortical hierarchies with temporal Hebbian updates

A key driver of mammalian intelligence is the ability to represent incoming sensory information across multiple abstraction levels. For example, in the visual ventral stream, incoming signals are first represented as low-level edge filters and then transformed into high-level object representations. Similar hierarchical structures routinely emerge in artificial neural networks (ANNs) trained for object recognition tasks, suggesting that similar structures may underlie biological neural networks. However, the classical ANN training algorithm, backpropagation, is considered biologically implausible, and thus alternative biologically plausible training methods have been developed such as Equilibrium Propagation, Deep Feedback Control, Supervised Predictive Coding, and Dendritic Error Backpropagation. Several of those models propose that local errors are calculated for each neuron by comparing apical and somatic activities. Notwithstanding, from a neuroscience perspective, it is not clear how a neuron could compare compartmental signals. Here, we propose a solution to this problem in that we let the apical feedback signal change the postsynaptic firing rate and combine this with a differential Hebbian update, a rate-based version of classical spiking time-dependent plasticity (STDP). We prove that weight updates of this form minimize two alternative loss functions that we prove to be equivalent to the error-based losses used in machine learning: the inference latency and the amount of top-down feedback necessary. Moreover, we show that the use of differential Hebbian updates works similarly well in other feedback-based deep learning frameworks such as Predictive Coding or Equilibrium Propagation. Finally, our work removes a key requirement of biologically plausible models for deep learning and proposes a learning mechanism that would explain how temporal Hebbian learning rules can implement supervised hierarchical learning

Harnessing function from form: towards bio-inspired artificial intelligence in neuronal substrates

Despite the recent success of deep learning, the mammalian brain is still unrivaled when it comes to interpreting complex, high-dimensional data streams like visual, auditory and somatosensory stimuli. However, the underlying computational principles allowing the brain to deal with unreliable, high-dimensional and often incomplete data while having a power consumption on the order of a few watt are still mostly unknown. In this work, we investigate how specific functionalities emerge from simple structures observed in the mammalian cortex, and how these might be utilized in non-von Neumann devices like “neuromorphic hardware”. Firstly, we show that an ensemble of deterministic, spiking neural networks can be shaped by a simple, local learning rule to perform sampling-based Bayesian inference. This suggests a coding scheme where spikes (or “action potentials”) represent samples of a posterior distribution, constrained by sensory input, without the need for any source of stochasticity. Secondly, we introduce a top-down framework where neuronal and synaptic dynamics are derived using a least action principle and gradient-based minimization. Combined, neurosynaptic dynamics approximate real-time error backpropagation, mappable to mechanistic components of cortical networks, whose dynamics can again be described within the proposed framework. The presented models narrow the gap between well-defined, functional algorithms and their biophysical implementation, improving our understanding of the computational principles the brain might employ. Furthermore, such models are naturally translated to hardware mimicking the vastly parallel neural structure of the brain, promising a strongly accelerated and energy-efficient implementation of powerful learning and inference algorithms, which we demonstrate for the physical model system “BrainScaleS–1”

Laminar-specific cortico-cortical loops in mouse visual cortex

"Muitas teorias propõem interacções recorrentes através da hierarquia cortical, mas não é claro se os circuitos corticais são selectivamente ligados para implementar cálculos em ciclo. Usando o mapeamento de circuitos subcelulares do método de canal de rodopsina 2 assistido no córtex visual do rato, comparamos a entrada sináptica de alimentação direta (feedforward, FF) ou retroalimentação (feedback, FB) cortico-cortical (CC) às células que se projectam de volta à fonte de entrada (neurónios em ciclo) com células que se projectam para uma área cortical ou subcortical diferente.(...)