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Richard G. M. Morris

Bio: Richard G. M. Morris is an academic researcher from University of New South Wales. The author has contributed to research in topics: Long-term potentiation & Memory consolidation. The author has an hindex of 74, co-authored 210 publications receiving 44439 citations. Previous affiliations of Richard G. M. Morris include University of Warwick & University of St Andrews.


Papers
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Journal ArticleDOI
TL;DR: Developments of an open-field water-maze procedure in which rats learn to escape from opaque water onto a hidden platform are described, suggesting that they may lend themselves to a variety of behavioural investigations, including pharmacological work and studies of cerebral function.

6,609 citations

Journal ArticleDOI
24 Jun 1982-Nature
TL;DR: It is reported that, in addition to a spatial discrimination impairment, total hippocampal lesions also cause a profound and lasting placenavigational impairment that can be dissociated from correlated motor, motivational and reinforcement aspects of the procedure.
Abstract: Electrophysiological studies have shown that single cells in the hippocampus respond during spatial learning and exploration1–4, some firing only when animals enter specific and restricted areas of a familiar environment. Deficits in spatial learning and memory are found after lesions of the hippocampus and its extrinsic fibre connections5,6 following damage to the medial septal nucleus which successfully disrupts the hippocampal theta rhythm7, and in senescent rats which also show a correlated reduction in synaptic enhancement on the perforant path input to the hippocampus8. We now report, using a novel behavioural procedure requiring search for a hidden goal, that, in addition to a spatial discrimination impairment, total hippocampal lesions also cause a profound and lasting placenavigational impairment that can be dissociated from correlated motor, motivational and reinforcement aspects of the procedure.

6,143 citations

Journal ArticleDOI
01 Feb 1986-Nature
TL;DR: This article showed that chronic intraventricular infusion of D,L-AP5 causes a selective impairment of place learning, which is highly sensitive to hippocampal damage, without affecting visual discrimination learning.
Abstract: Recent work has shown that the hippocampus contains a class of receptors for the excitatory amino acid glutamate that are activated by N-methyl-D-aspartate (NMDA) and that exhibit a peculiar dependency on membrane voltage in becoming active only on depolarization. Blockade of these sites with the drug aminophosphonovaleric acid (AP5) does not detectably affect synaptic transmission in the hippocampus, but prevents the induction of hippocampal long-term potentiation (LTP) following brief high-frequency stimulation. We now report that chronic intraventricular infusion of D,L-AP5 causes a selective impairment of place learning, which is highly sensitive to hippocampal damage, without affecting visual discrimination learning, which is not. The L-isomer of AP5 did not produce behavioural effects. AP5 treatment also suppressed LTP in vivo. These results suggest that NMDA receptors are involved in spatial learning, and add support to the hypothesis that LTP is involved in some, but not all, forms of learning.

3,488 citations

Journal ArticleDOI
TL;DR: It is concluded that a wealth of data support the notion that synaptic plasticity is necessary for learning and memory, but that little data currently supports the notion of sufficiency.
Abstract: Changing the strength of connections between neurons is widely assumed to be the mechanism by which memory traces are encoded and stored in the central nervous system. In its most general form, the synaptic plasticity and memory hypothesis states that "activity-dependent synaptic plasticity is induced at appropriate synapses during memory formation and is both necessary and sufficient for the infor- mation storage underlying the type of memory mediated by the brain area in which that plasticity is observed." We outline a set of criteria by which this hypothesis can be judged and describe a range of experimental strategies used to investigate it. We review both classical and newly discovered properties of synaptic plasticity and stress the importance of the neural architecture and synaptic learning rules of the network in which it is embedded. The greater part of the article focuses on types of memory mediated by the hippocampus, amygdala, and cortex. We conclude that a wealth of data supports the notion that synaptic plasticity is necessary for learning and memory, but that little data currently supports the notion of sufficiency.

2,610 citations

Journal ArticleDOI
TL;DR: It is demonstrated that rats can rapidly learn to locate an object that they can never see, hear, or smell provided it remains in a fixed spatial location relative to distal room cues.

2,496 citations


Cited by
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Book
01 Jan 1988
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Abstract: Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

37,989 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal ArticleDOI
07 Jan 1993-Nature
TL;DR: The best understood form of long-term potentiation is induced by the activation of the N-methyl-d-aspartate receptor complex, which allows electrical events at the postsynaptic membrane to be transduced into chemical signals which, in turn, are thought to activate both pre- and post Synaptic mechanisms to generate a persistent increase in synaptic strength.
Abstract: Long-term potentiation of synaptic transmission in the hippocampus is the primary experimental model for investigating the synaptic basis of learning and memory in vertebrates. The best understood form of long-term potentiation is induced by the activation of the N-methyl-D-aspartate receptor complex. This subtype of glutamate receptor endows long-term potentiation with Hebbian characteristics, and allows electrical events at the postsynaptic membrane to be transduced into chemical signals which, in turn, are thought to activate both pre- and postsynaptic mechanisms to generate a persistent increase in synaptic strength.

11,123 citations

Journal ArticleDOI
14 Mar 1997-Science
TL;DR: Findings in this work indicate that dopaminergic neurons in the primate whose fluctuating output apparently signals changes or errors in the predictions of future salient and rewarding events can be understood through quantitative theories of adaptive optimizing control.
Abstract: The capacity to predict future events permits a creature to detect, model, and manipulate the causal structure of its interactions with its environment. Behavioral experiments suggest that learning is driven by changes in the expectations about future salient events such as rewards and punishments. Physiological work has recently complemented these studies by identifying dopaminergic neurons in the primate whose fluctuating output apparently signals changes or errors in the predictions of future salient and rewarding events. Taken together, these findings can be understood through quantitative theories of adaptive optimizing control.

8,163 citations