DEGREE PROJECT IN COMPUTER SCIENCE AND ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2020 Attractor Neural Network modelling of the Lifespan Retrieval Curve PATRÍCIA PEREIRA KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE 0 KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Master Programme in Systems, Control and Robotics June 2020 Author: Patrícia Pereira, [email protected] Supervisors: Pawel Herman, [email protected] Anders Lansner, [email protected] Examiner: Erik Fransén, [email protected] 1 Abstract Human capability to recall episodic memories depends on how much time has passed since the memory was encoded. This dependency is described by a memory retrieval curve that reflects an interesting phenomenon referred to as a reminiscence bump - a tendency for older people to recall more memories formed during their young adulthood than in other periods of life. This phenomenon can be modelled with an attractor neural network, for example, the firing-rate Bayesian Confidence Propagation Neural Network (BCPNN) with incremental learning. In this work, the mechanisms underlying the reminiscence bump in the neural network model are systematically studied. The effects of synaptic plasticity, network architecture and other relevant parameters on the characteristics of the reminiscence bump are systematically investigated. The most influential factors turn out to be the magnitude of dopamine-linked plasticity at birth and the time constant of exponential plasticity decay with age that set the position of the bump. The other parameters mainly influence the general amplitude of the lifespan retrieval curve. Furthermore, the recency phenomenon, i.e. the tendency to remember the most recent memories, can also be parameterized by adding a constant to the exponentially decaying plasticity function representing the decrease in the level of dopamine neurotransmitters. Keywords: reminiscence bump, attractor neural network, Bayesian Confidence Propagation Neural Network (BCPNN), recency, synaptic plasticity, episodic memory 2 Sammanfattning Människans förmåga att återkalla episodiska minnen beror på hur lång tid som gått sedan minnena inkodades. Detta beroende beskrivs av en sk glömskekurva vilken uppvisar ett intressant fenomen som kallas ”reminiscence bump”. Detta är en tendens hos äldre att återkalla fler minnen från ungdoms- och tidiga vuxenår än från andra perioder i livet. Detta fenomen kan modelleras med ett neuralt nätverk, sk attraktornät, t ex ett icke spikande Bayesian Confidence Propagation Neural Network (BCPNN) med inkrementell inlärning. I detta arbete studeras systematiskt mekanismerna bakom ”reminiscence bump” med hjälp av denna neuronnätsmodell. Exempelvis belyses betydelsen av synaptisk plasticitet, nätverksarkitektur och andra relavanta parameterar för uppkomsten av och karaktären hos detta fenomen. De mest inflytelserika faktorerna för bumpens position befanns var initial dopaminberoende plasticitet vid födseln samt tidskonstanten för plasticitetens avtagande med åldern. De andra parametrarna påverkade huvudsakligen den generella amplituden hos kurvan för ihågkomst under livet. Dessutom kan den s k nysseffekten (”recency effect”), dvs tendensen att bäst komma ihåg saker som hänt nyligen, också parametriseras av en konstant adderad till den annars exponentiellt avtagande plasticiteten, som kan representera densiteten av dopaminreceptorer. Nyckelord: ”reminiscence bump”, attraktorneuronnät, Bayesian Confidence Propagation Neural Network (BCPNN), nysseffekt, synaptisk plasticitet, episodiskt mine. 3 Acknowledgements I would like to thank Professors Pawel Herman and Anders Lansner for their enthusiastic supervision throughout the project. A warm thanks to my friends and colleagues with whom I shared my academic journey. My most grateful thanks to my parents for their love, care and support. 4 To my dear parents. 5 “Thus, our knowledge of the world, including ourselves, is incomplete as to space and indefinite as to time. This ignorance, implicit in all our brains, is the counterpart of the abstraction which renders our knowledge useful” - McCulloch and Pitts 6 Contents 1. Introduction ..................................................................................................................... 8 1.1 Research question ....................................................................................................... 9 1.2 Aim and scope ............................................................................................................. 9 1.3 Thesis Outline ............................................................................................................ 10 2. Background ................................................................................................................... 11 2.1. Reminiscence bump .................................................................................................. 11 2.1.1. Psychological and biological hypotheses ............................................................. 12 2.1.2. Different ways of cuing lead to different bumps ................................................... 14 2.2. Neuronal computational models ............................................................................... 15 2.2.1. Abstract Models ...................................................................................................... 15 2.2.2. Detailed Models ....................................................................................................... 16 2.2.3. Attractor neural network memory modelling ........................................................ 18 2.2.4. Other models ........................................................................................................... 19 3. Methods ......................................................................................................................... 24 3.1. Attractor Memory Network Model ............................................................................. 24 3.1.1. Modularity ................................................................................................................ 24 3.1.2. BCPNN learning and network dynamics ............................................................... 25 3.1.3. Meaning of model parameters ................................................................................ 26 3.2. Simulation protocol ................................................................................................... 27 3.3. Analysis and evaluation ............................................................................................ 29 4. Results ........................................................................................................................... 30 4.1. Reminiscence bump .................................................................................................. 31 4.2. Recency ...................................................................................................................... 44 5. Discussion ..................................................................................................................... 48 5.1. Summary of findings.................................................................................................. 48 5.2. Interpretation of the results and their impact ........................................................... 48 5.3. Limitations .................................................................................................................. 49 5.4. Social, ethical and sustainability aspects ................................................................ 49 6. Conclusion and Future Work ....................................................................................... 51 Bibliography ...................................................................................................................... 52 7 Chapter 1 Introduction The advancement of neuroscience is beneficial to the humankind in many ways. There are however two main directions that have been tangibly capitalized on in recent times. The first one is that an improved understanding of neurological and psychological mechanisms enables the development of better medical treatments and therapies for neurological illnesses. It can also empower society as is illustrated by the example of headphones that maximize motor learning by applying a small electric current to the area of the brain that controls movement1. The other way in which a deepened understanding of the brain is beneficial is that it is a source of inspiration for algorithms that have useful applications such as deep learning algorithms used in computer vision and speech processing. In this direction, it contributes to the development of more “human-like” and powerful artificial agents. Within neuroscience, memory plays a key role. Studying memory is important because there has been an increasing interest in tackling brain diseases of which memory deficits are common symptoms such as Alzheimer’s disease and other types of dementia [1]. Memory is also a key aspect of cognition fundamental for intelligent behavior, namely in learning and decision processes [2]. In another perspective, we can consider life as a sum of memories important to keep our identity and mental health. The focus of this project is on long-term memory, precisely episodic memory. Episodic memory is a category of long-term memory which concerns events that occurred throughout one’s life. Important personal