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https://github.com/jegp/thesis

My master's thesis on modelling learning systems in artificial and spiking neural networks
https://github.com/jegp/thesis

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My master's thesis on modelling learning systems in artificial and spiking neural networks

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# Modelling learning systems in artificial and spiking neural networks

Building on research from cognitive and computational neuroscience, deep

learning is evolving rapidly and has even surpassed humans in some recognition

tasks [1]. Contemporary theories from cognitive neuroscience however, tell us

that learning in the biological brain occurs in spiking neural networks (SNN)

instead of the layered artificial neural networks (ANN) traditionally used in machine learning [2].



Because of this biological similarity SNN are of great interest to neuroscientists [7].

They are unfortunately difficult to program, and the experiments that are being executed in spite of this

have a low external validity, because of the heterogeneous and unstable software underlying the simulations.

The expertise needed to overcome these obstacles are inaccessible for most neuroscientists.



A further challenge for SNNs is the relatively poor understanding of learning mechanisms compared to ANN [4, 5].

Several different techniques have been applied to understand learning in SNNs [6, 7], but they are difficult to test because of the diverse implementations.



This theses sets out to explore SNNs and their potential for the 

field of machine learning, focusing on two major challenges: homogeneous modelling and learning.

To achieve this, a unified domain specific language (DSL), Volr, is developed to model both ANNs and SNNs. 

The DSL aims to provide a concise description of neural networks and to ensure the correct translation into executable neural network models.



The thesis is built around the hypothetico-deductive model, in which falsifiable hypotheses are formulated, tested and evaluated.



## Hypotheses



This thesis examines two hypotheses:



1. *The Volr DSL can translate into spiking and non-spiking neural networks such that the network topologies are retained.*

2. *Using training it is possible for spiking and non-spiking models to solve an MNIST recognition task.*



The topology of a network is understood as typed declarations of neural network structure, defined as a collection of nodes, edges and connectivity descriptions.



The first hypothesis tests that the neural networks generated by the DSL are modelled correctly, and translated---without significant deviations---to ANN and SNN.

Consistent translations are important to ensure correct and reproducible experiments,

but are also vital to further the understanding of spiking neural networks:

correct rendition bridges the semantics of artificial and spiking neural networks.

This indirectly allows users of the DSL to draw on the vast literature of ANN.



Second generation NNs are generally capable of learning pattern-recognition

problems [2].

It is known that learning also occurs within neural systems, so a

similar behaviour is expected in third generation \gls{NN}s.

The second hypothesis verifies that this property exists in both spiking and

non-spiking neural networks.

Additionally, the hypothesis provides a mean of comparison between the two paradigms.



## Experimental setup

The thesis presents three experiments to validate assumptions about the DSL: two NAND and XOR experiments that tests 

that the model is capable of learning simple logic gates, and an MNIST task that tests that the network is capable of encoding

larger classification structures.

The experiments will be performed on DSL models that will be translated into a spiking and non-spiking model, but the spiking

model will be executed twice: once with randomly initialised weights and once with weights that are transferred from the optimised Futhark model.



Both the Futhark and NEST simulations will be trained using supervised backpropagation learning.



For the layered non-spiking ANN model, code will be generated to run on Futhark and will be executed through OpenCL [10].

For the spiking model the neural simulation framework PyNN will act as an

interface to the simulated backend NEST, and possibly analog backends like BrainScaleS [8].



The DSL itself will be implemented in Haskell.



## Tasks

The tasks involved in this thesis are as follows:



* Implement the Volr DSL in Haskell

* Implement a translation from the DSL to an executable and trainable Futhark ANN

* Implement a translation from the DSL to an executable and trainable NEST simulation

* Implement encoders and decoders for the spiking neural networks to correctly translate stimuli and outputs between spiking and non-spiking network models

* Build a model that can solve an MNIST experiment in the DSL

* Translate the MNIST model to Futhark and NEST

* Train the Futhark and NEST programs, and emulate the BrainScaleS program using the weights from the NEST model

* Compare the performance of the experiments, with a focus on learning rate and accuracy.



## Learning objectives



* Survey models for learning through backpropagation in spiking and non-spiking neural networks

* Implement backpropagation learning in spiking and non-spiking neural networks

* Implement ANNs in the data-parallel functional language Futhark

* Implement SNNs in NEST

* Analyse and compare the SNN model performance through learning rate and accuracy scores



## Additional objectives



Due to the extension of the thesis several additions have been made:



* An extra hypothesis that tests the semantic translation

  between the DSL and the backends

* An implementation for the backpropagation algorithm for temporal SNNs

* Additional tasks for the backend implementations (see tasks)



## Thesis milestones

The thesis has been extended to the

23rd of January. The remaining process is divided into four miletones with associated

deadlines:



| Milestone | Date of completion |

| ---------------------------------------------- | ------------------ |

| Complete the translations to the three backends| 10th of November |

| Construct and train the MNIST model | 3rd of December |

| Execute, analyse and compare experiments | 10th of December |

| Describe and evaluate the Futhark, NEST and BrainScales implementations | 17th of December | 

| Finish writing | 31st of December |



## Sources



1. J. Schmidhuber: "Deep Learning in Neural Networks: An Overview",

Neural Networks, pp. "85-117, Volume 61, 2015.

2. Peter Dayan and L. F. Abbot: "Theoretical neuroscience - Computational and Mathematical Modeling of Neural Systems", MIT Press 2001.

3.  Thomas Pfeil, Andreas Grübl, Sebastian Jeltsch, Eric Müller, Paul Müller, Mihai A. Petrovici, Michael Schmuker, Daniel Brüderle, Johannes Schemmel and Karlheinz Meier: "Six networks on a universan neuromorphic computing substrate", Frontiers in Neuroscience, 2013.

4. A. Tavanei and Anthony S. Maida: "A Minimal Spiking Neural Network to Rapidly Train and Classify Handwritten Digits in Binary and 10-Digit Tasks", International Journal of Advanced Research in Artificial Intelligence, Vol. 4, No. 7, 2015.

5. Florian Walter, Florian Röhrbein and Alois Knoll: "Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks", Neural Networks,

Volume 72, pp. 152-167, 2015.

6. Sander M. Bohte, Joost N. Kok and Han La Poutr: "Error-backpropagation in temporally encoded networks of spiking neurons", Neurocomputing, Elsevier 2002.

7. Daniel Brüderle, Mihai A. Petrovici, Bernhard Vogginger, Matthias Ehrlich, Thomas Pfeil, Sebastian Millner, Andreas Grübl, Karsten Wendt, Eric Müller, Marc-Olivier Schwartz, Dan Husmann de Oliveira, Sebastian Jeltsch, Johannes Fieres, Moritz Schilling, Paul Müller, Oliver Breitwieser, Venelin Petkov, Lyle Muller, Andrew P. Davison, Pradeep Krishnamurthy, Jens Kremkow, Mikael Lundqvist, Eilif Muller, Johannes Partzsch, Stefan Scholze, Lukas Zühl, Christian Mayr, Alain Destexhe, Markus Diesmann, Tobias C. Potjans, Anders Lansner, René Schüffny, Johannes Schemmel and Karlheinz Meier: "A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems", Biol Cybern. 2011 May;104(4-5):263-96.

8. Jesper Mogensen and Morten Overgaard: Reorganization of the Connectivity between Elementary Functions - A Model Relating Conscious States to Neural Connections, Fronties in Psychology, 8:652, 2017.

9. Kunkel Susanne, Schmidt Maximilian, Eppler Jochen M., Plesser Hans E., Masumoto Gen, Igarashi Jun, Ishii Shin, Fukai Tomoki, Morrison Abigail, Diesmann Markus, Helias Moritz: "Spiking network simulation code for petascale computers", Frontiers in Neuroinformatics, Vol. 8, p. 78, 2014.

10. Henriken, Troels: "Design and Implementation of the Futhark Programming Language", Ph.D. thesis, Faculty of Science, Copenhagen University 2017.