Memory As Prediction

Theoretical reflections and analytical observations on memory and prediction, linking these concepts to the role of the cerebellum in higher cognition. What is memory? What is memory for? Where is memory in the brain? Although memory is probably the most studied function in cognition, these fundamental questions remain challenging. We can try to answer the question of memory's purpose by defining the function of memory as remembering the past. And yet this definition is not consistent with the many errors that characterize our memory, or with the phylogenetic and ontogenetic origin of memory. In this book, Tomaso Vecchi and Daniele Gatti argue that the purpose of memory is not to remember the past but to predict the future.

"An argument to view memory as predicting the future, rather than merely archiving the past. Based on experimental evidence from psychology and neuroscience"--

When one is immersed in the fascinating world of neuroscience findings, the brain might start to seem like a collection of "modules," each specializes in a specific mental feat. But just like in other domains of Nature, it is possible that much of the brain and mind's operation can be explained with a small set of universal principles. Given exciting recent developments in theory, empirical findings and computational studies, it seems that the generation of predictions might be one strong candidate for such a universal principle. This is the focus of Predictions in the brain. From the predictions required when a rat navigates a maze to food-caching in scrub-jays; from predictions essential in decision-making to social interactions; from predictions in the retina to the prefrontal cortex; and from predictions in early development to foresight in non-humans. The perspectives represented in this collection span a spectrum from the cellular underpinnings to the computational principles underlying future-related mental processes, and from systems neuroscience to cognition and emotion. In spite of this diversity, they share some core elements. Memory, for instance, is critical in any framework that explains predictions. In asking "what is next?" our brains have to refer to memory and experience on the way to simulating our mental future. But as much as this collection offers answers to important questions, it raises and emphasizes outstanding ones. How are experiences coded optimally to afford using them for predictions? How do we construct a new simulation from separate memories? How specific in detail are future-oriented thoughts, and when do they rely on imagery, concepts or language? Therefore, in addition to presenting the state-of-the-art of research and ideas about predictions as a universal principle in mind and brain, it is hoped that this collection will stimulate important new research into the foundations of our mental lives.

An accessible and engaging account of the mind and its connection to the brain. The mind encompasses everything we experience, and these experiences are created by the brain--often without our awareness. Experience is private; we can't know the minds of others. But we also don't know what is happening in our own minds. In this book, E. Bruce Goldstein offers an accessible and engaging account of the mind and its connection to the brain. He takes as his starting point two central questions--what is the mind? and what is consciousness?--and leads readers through topics that range from conceptions of the mind in popular culture to the wiring system of the brain. Throughout, he draws on the latest research, explaining its significance and relevance.

From the inventor of the PalmPilot comes a new and compelling theory of intelligence, brain function, and the future of intelligent machines Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one stroke, with a new understanding of intelligence itself. Hawkins develops a powerful theory of how the human brain works, explaining why computers are not intelligent and how, based on this new theory, we can finally build intelligent machines. The brain is not a computer, but a memory system that stores experiences in a way that reflects the true structure of the world, remembering sequences of events and their nested relationships and making predictions based on those memories. It is this memory-prediction system that forms the basis of intelligence, perception, creativity, and even consciousness. In an engaging style that will captivate audiences from the merely curious to the professional scientist, Hawkins shows how a clear understanding of how the brain works will make it possible for us to build intelligent machines, in silicon, that will exceed our human ability in surprising ways. Written with acclaimed science writer Sandra Blakeslee, On Intelligence promises to completely transfigure the possibilities of the technology age. It is a landmark book in its scope and clarity.

The ability to remember prior experience is a core aspect of cognition. Memory can be comprised of information about individual items or contexts, but information about the temporal relationships between entities in the environment can provide not only a record of what happened in the past but also facilitate predictions about what might happen in the future. To address how temporal relationships are learned and used to facilitate predictions, the current work includes three investigations, two experiments using functional magnetic resonance imaging (fMRI) and one experiment using behavior. The reported fMRI experiments tested neural predictions from a recent model positing the existence of dissociable posterior-medial (PM) and anterior-temporal (AT) cortical networks that are thought to represent spatio-temporal relationships and item information, respectively, in the service of memory-guided behavior (Ranganath and Ritchey, 2012). In Chapter 2, representational similarity analysis was used to test for evidence of PM network involvement in supporting representations of shared temporal structure across event sequences. In Chapter 3, computational model-based fMRI was employed to assess evidence for the involvement of the AT network in representing the shared meaning of objects in a task where shared meaning could be discerned by learning to predict outcomes associated with sequentially presented pairs of object cues. In Chapter 4, we tested for behavioral evidence of increased flexibility of predictions about temporally associated stimuli following a period of memory trace stabilization. Together, the results across these investigations extend our understanding of the neural and behavioral correlates of learning and using temporal information to guide expectations and predictions about upcoming information.

The Long Short-Term Memory network, or LSTM for short, is a type of recurrent neural network that achieves state-of-the-art results on challenging prediction problems. In this laser-focused Ebook, finally cut through the math, research papers and patchwork descriptions about LSTMs. Using clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what LSTMs are, and how to develop a suite of LSTM models to get the most out of the method on your sequence prediction problems.

This title brings together work on embodiment, action, and the predictive mind. At the core is the vision of human minds as prediction machines - devices that constantly try to stay one step ahead of the breaking waves of sensory stimulation, by actively predicting the incoming flow. In every situation we encounter, that complex prediction machinery is already buzzing, proactively trying to anticipate the sensory barrage. The book shows in detail how this strange but potent strategy of self-anticipation ushers perception, understanding, and imagination simultaneously onto the cognitive stage.