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

Spiking neural models & machine learning for systems neuroscience: Learning, Cognition and Behavior.

Learning, cognition and the ability to navigate, interact and manipulate the world around us by performing appropriate behavior are hallmarks of artificial as well as biological intelligence. In order to understand how intelligent behavior can emerge from computations of neural systems, this thesis suggests to consider and study learning, cognition and behavior simultaneously to obtain an integrative understanding. This involves building detailed functional computational models of nervous systems that can cope with sensory processing, learning, memory and motor control to drive appropriate behavior. The work further considers how the biological computational substrate of neurons, dendrites and action potentials can be successfully used as an alternative to current artificial systems to solve machine learning problems. It challenges the simplification of currently used rate-based artificial neurons, where computational power is sacrificed by mathematical convenience and statistical learning. To this end, the thesis explores single spiking neuron computations for cognition and machine learning problems as well as detailed functional networks thereof that can solve the biologically relevant foraging behavior in flying insects. The obtained results and insights are new and relevant for machine learning, neuroscience and computational systems neuroscience. The thesis concludes by providing an outlook how application of current machine learning methods can be used to obtain a statistical understanding of larger scale brain systems. In particular, by investigating the functional role of the cerebellar-thalamo-cortical system for motor control in primates

From Bayesian principles to Bayesian processes

This thesis considers the free energy principle (FEP) and its corollary, active inference, which form an explanatory framework that prescribes a Bayesian interpretation of self-organizing systems. The FEP originated in the domain of neuroscience, where it underwrote a unified theory that described perception, action and learning as emerging from minimizing a single objective function - variational free energy. However, since its conception, the FEP has transcended into physics and pure mathematics. Here, it presents itself as a set of mathematical arguments culminating in an inferential interpretation of a specific class of systems. The result has fundamentally changed the epistemological status of the FEP, moving it from the world of empirical hypotheses to the unfalsifiable territory of mathematical equivalences and tautological constructions. While the FEP may present a historical development that further unravels the symmetries that govern the laws of (our own) physics, its growth has left a range of epistemological confusion. In the current thesis, we evaluate how to maneuver from the principles of the FEP to the processes it purportedly explains. We identify four key areas in which the FEP can inform empirical science: 1) The FEP can aid us in designing intelligent agents by providing novel functionals that respect inherent uncertainty in the environment. We demonstrate equivalences between active inference and reinforcement learning, offer a novel implementation of active inference that utilizes amortized inference, and show that the proposed algorithm enables efficient exploration while offering improved sample efficiency compared to modern reinforcement learning algorithms. 2) We describe how the FEP can help us understand the nature of representation in living systems. Specifically, we show how the normative aspects of the FEP promote learning representations oriented towards action rather than veridical reconstructions of the environment. 3) We show how the FEP provides a framework for modeling perception, action, and learning in systems that can be empirically measured. An eye-tracking study demonstrates that an active inference model best explains human information-seeking, offering insights into the underlying mechanisms of perception and action. 4) In the final section, we ask whether active inference can inform the development of novel process theories in computational neuroscience. A biologically-plausible learning algorithm is developed and verified on various computer vision and reinforcement learning tasks. The resulting model explains a range of empirical phenomena and offers a new perspective on the role of bottom-up information in perception. This thesis affirms the role of the FEP and active inference as a generative framework for developing testable scientific theories

Computational Optimizations for Machine Learning

The present book contains the 10 articles finally accepted for publication in the Special Issue “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity

Collision Avoidance on Unmanned Aerial Vehicles using Deep Neural Networks

Unmanned Aerial Vehicles (UAVs), although hardly a new technology, have recently gained a prominent role in many industries, being widely used not only among enthusiastic consumers but also in high demanding professional situations, and will have a massive societal impact over the coming years. However, the operation of UAVs is full of serious safety risks, such as collisions with dynamic obstacles (birds, other UAVs, or randomly thrown objects). These collision scenarios are complex to analyze in real-time, sometimes being computationally impossible to solve with existing State of the Art (SoA) algorithms, making the use of UAVs an operational hazard and therefore significantly reducing their commercial applicability in urban environments. In this work, a conceptual framework for both stand-alone and swarm (networked) UAVs is introduced, focusing on the architectural requirements of the collision avoidance subsystem to achieve acceptable levels of safety and reliability. First, the SoA principles for collision avoidance against stationary objects are reviewed. Afterward, a novel image processing approach that uses deep learning and optical flow is presented. This approach is capable of detecting and generating escape trajectories against potential collisions with dynamic objects. Finally, novel models and algorithms combinations were tested, providing a new approach for the collision avoidance of UAVs using Deep Neural Networks. The feasibility of the proposed approach was demonstrated through experimental tests using a UAV, created from scratch using the framework developed.Os veículos aéreos não tripulados (VANTs), embora dificilmente considerados uma nova tecnologia, ganharam recentemente um papel de destaque em muitas indústrias, sendo amplamente utilizados não apenas por amadores, mas também em situações profissionais de alta exigência, sendo expectável um impacto social massivo nos próximos anos. No entanto, a operação de VANTs está repleta de sérios riscos de segurança, como colisões com obstáculos dinâmicos (pássaros, outros VANTs ou objetos arremessados). Estes cenários de colisão são complexos para analisar em tempo real, às vezes sendo computacionalmente impossível de resolver com os algoritmos existentes, tornando o uso de VANTs um risco operacional e, portanto, reduzindo significativamente a sua aplicabilidade comercial em ambientes citadinos. Neste trabalho, uma arquitectura conceptual para VANTs autônomos e em rede é apresentada, com foco nos requisitos arquitetônicos do subsistema de prevenção de colisão para atingir níveis aceitáveis de segurança e confiabilidade. Os estudos presentes na literatura para prevenção de colisão contra objectos estacionários são revistos e uma nova abordagem é descrita. Esta tecnica usa técnicas de aprendizagem profunda e processamento de imagem, para realizar a prevenção de colisões em tempo real com objetos móveis. Por fim, novos modelos e combinações de algoritmos são propostos, fornecendo uma nova abordagem para evitar colisões de VANTs usando Redes Neurais Profundas. A viabilidade da abordagem foi demonstrada através de testes experimentais utilizando um VANT, desenvolvido a partir da arquitectura apresentada