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Exploring Neuromodulatory Systems for Dynamic Learning

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Exploring Neuromodulatory Systems for Dynamic Learning Rochester Institute of Technology RIT Scholar Works Theses 5-2019 Exploring Neuromodulatory Systems for Dynamic Learning Anurag Daram [email protected] Follow this and additional works at: https://scholarworks.rit.edu/theses Recommended Citation Daram, Anurag, "Exploring Neuromodulatory Systems for Dynamic Learning" (2019). Thesis. Rochester Institute of Technology. Accessed from This Thesis is brought to you for free and open access by RIT Scholar Works. It has been accepted for inclusion in Theses by an authorized administrator of RIT Scholar Works. For more information, please contact [email protected]. Exploring Neuromodulatory Systems for Dynamic Learning Anurag Daram Exploring Neuromodulatory Systems for Dynamic Learning Anurag Daram May 2019 A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in Computer Engineering Department of Computer Engineering Exploring Neuromodulatory Systems for Dynamic Learning Anurag Daram Committee Approval: Dr. Dhireesha Kudithipudi Advisor Date Professor, RIT, Department of Computer Engineering Dr. Angel Yanguas-Gil Date Principal Materials Scientist, Argonne National Labs Dr. Cory Merkel Date Assistant Professor, RIT, Department of Computer Engineering i Acknowledgments I am grateful to Dr. Dhireesha Kudithipudi for her unwavering guidance and for be- ing patient with me during my work. I would like to acknowledge Dr. Angel Yanguas Gil, my committee member for his constant support and guidance. I would like to acknowledge the members of the Neuromorphic AI Lab at the Rochester Institute of Technology for helping make this work possible. I am thankful to my family and friends for constantly supporting me during this journey. I would also like to acknowl- edge DARPA/MTO's L2M(Lifelong Learning Machines) program for sponsoring this work. ii I would like to dedicate this work to the Neuromorphic Artificial Intelligence Lab for nurturing me throughout this time. iii Abstract In a continual learning system, the network has to dynamically learn new tasks from few samples throughout its lifetime. It is observed that neuromodulation acts as a key factor in continual and dynamic learning in the central nervous system. In this work, the neuromodulatory plasticity is embedded with dynamic learning architec- tures. The network has an inbuilt modulatory unit that regulates learning depending on the context and the internal state of the system, thus rendering the networks with the ability to self modify their weights. In one of the proposed architectures, Mod- Net, a modulatory layer is introduced in a random projection framework. This layer modulates the weights of the output layer neurons in tandem with hebbian learning. Moreover, to explore modulatory mechanisms in conjunction with backpropagation in deeper networks, a modulatory trace learning rule is introduced. The proposed learning rule, uses a time dependent trace to automatically modify the synaptic con- nections as a function of ongoing states and activations. The trace itself is updated via simple plasticity rules thus reducing the demand on resources. A digital architecture is proposed for ModNet, with on-device learning and resource sharing, to facilitate the efficacy of dynamic learning on the edge. The proposed modulatory learning architecture and learning rules demonstrate the ability to learn from few samples, train quickly, and perform one shot image clas- sification in a computationally efficient manner. The ModNet architecture achieves an accuracy of ∼91% for image classification on the MNIST dataset while training for just 2 epochs. The deeper network with modulatory trace achieves an average accuracy of 98.8%±1.16 on the omniglot dataset for five-way one-shot image clas- sification task. In general, incorporating neuromodulation in deep neural networks shows promise for energy and resource efficient lifelong learning systems. iv Contents Signature Sheet i Acknowledgments ii Dedication iii Abstract iv Table of Contents v List of Figures vii List of Tables x Acronyms xi 1 Motivation 1 1.1 Thesis Statement . .2 1.2 Thesis Contributions . .4 2 Related Work 5 2.1 Machine Learning . .5 2.2 Rate based feedforward neural networks . .6 2.3 Neuromodulation in brain . .8 2.4 Lifelong Learning Systems . 11 2.5 Biologically plausible models . 14 2.6 One shot learning . 15 2.7 Other works . 16 2.7.1 Extreme Learning Machine . 16 2.7.2 Autoencoders . 18 2.8 Role of dimensionality . 19 3 Methodology 21 3.1 Region based Attention Mechanism . 21 3.1.1 Algorithm . 22 3.2 Modulatory context based processing . 26 v CONTENTS 3.3 Proposed Neuromodulatory Architecture . 28 3.3.1 Algorithm/Learning Rule . 30 3.3.2 Modulatory plasticity in deep networks . 36 4 RTL Design 41 4.0.1 Low level modules . 42 4.0.2 Hidden Activation Unit . 43 4.0.3 Error Compute Unit . 47 4.0.4 Weight Update Unit . 48 5 Results & Discussion 50 5.1 Experimental Setup and benchmarks ............... 50 5.1.1 One shot pattern classification: Omniglot task . 52 5.1.2 ModNet Algorithm Evaluation . 54 5.1.3 Digital Architecture Results . 58 5.1.4 Results on Omniglot dataset . 64 6 Conclusions and Future Work 70 6.1 Conclusion . 70 6.2 Future Work . 71 Bibliography 73 6.3 Appendix . 80 Glossary 82 vi List of Figures 2.1 A single rate based neuron . .6 2.2 A single fully connected layer . .6 2.3 Example of neuromodulation in the brain [59]. The neuromodula- tors affect and regulate the connectivity between the post and the pre synaptic neurons. .9 2.4 Example of dopaminergic modulation in an insect brain [14] . 10 2.5 Example of incorporating modulatory dynamics in the conventional neural network [74] . 13 2.6 ELM architecture for handwritten digits classification. 17 2.7 Architecture of an Autoencoder . 18 3.1 The hidden layer is divided into different regions with each region hav- ing the same but modified activation function. The activity factor of each region determines the strength of the region in learning and selectively filters out the inactive regions. 23 3.2 The input is passed through a convolutional embedding and at the final layer, based on the activations from the feature layer and the output layer, the network determines the context and modulates the plasticity of those connections. 27 3.3 The neural circuit formed between the antennal lobe projection neu- rons, Kenyon cells and lateral horn MBONs(left) in the mushroom body of insects is akin to the ModNet network architecture proposed. 30 3.4 The training procedure in ModNet involves calculation of the activa- tions in the hidden and output layers. This is followed by calculation of error and then the standard and the modulatory weights are updated. 31 3.5 The output neuron has two sets of activations being calculated. The standard activations perform thee main processing and have a direct effect on the output of the network while the modulatory activations are used for the weight update. 32 vii LIST OF FIGURES 4.1 High-level RTL block diagram of the ModNet. The inputs are broad- cast to the hidden layer from an external memory. The activation values of the output of the hidden layer neurons are computed. The output layer neurons are measured and the computed error is passed to the modulatory layer via a one-to-one mapping, which in turn updates the hidden layer's synaptic weights. 41 4.2 RTL diagram of the hidden activation unit. It broadcasts the input pixels to the hidden layer neurons and calculates the hidden layer neu- rons' output activation values. 44 4.3 Topologies for hidden layer. (a) Baseline (b) Folding . 46 4.4 The RTL of error compute unit, which calculates the output layer neu- rons' activation values, the modulatory inputs (error) and the modu- latory layer's activation values. 47 4.5 The weight update unit's RTL diagram. This unit updates the stan- dard weights according to the learning rule. 49 5.1 The types of characters and data in the omniglot dataset. 53 5.2 The different alphabets in the omniglot dataset. 54 5.3 The sample N way, K-shot problem set. In this figure N is 20 and K is 1. The network has to be able to classify the unlabelled example from the N character classes. 55 5.4 The convolutional architecture used as the baseline.The modulatory trace is applied to the weights connecting the fully connected feature vector to the N-way softmax layer. 55 5.5 The test accuracy of the network trained for 2 epochs (averaged over 5 runs each) on the MNIST dataset with respect to the number of hidden layer neurons and the initialized value of the adaptive learning parameter. 56 5.6 The test accuracy of the network with respect to the number of epochs the network is trained for. 57 5.7 The accuracy (blue) with respect to the number of samples trained, and the change in absolute modulatory activation values (red) while training on (a) MNIST (b) Fashion-MNIST. 58 5.8 The accuracy of the network with respect to the number of samples trained for ModNet and ELM. 59 viii LIST OF FIGURES 5.9 Resource utilization on the Virtex-7 (xc7vx690t) FPGA with varying folding factor in the hidden layer with 1,000 neurons. 61 5.10 (a) Power vs. folding factor at 16-bit precision for 1,000 hidden neu- rons(b) Power vs. bit precision with 200 neurons in the hidden layer. 61 5.11 The maximum operating frequency of the design with respect to the bit precision. 63 5.12 The inter-quartile range loss curves for the network with a constant modulatory context term (α) and same initial modulatory learning factor term (δ) for all neurons (yellow) and learning rule with trained α and independent δ terms for all neurons.
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