7,948 research outputs found

    Remembering Forward: Neural Correlates of Memory and Prediction in Human Motor Adaptation

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    We used functional MR imaging (FMRI), a robotic manipulandum and systems identification techniques to examine neural correlates of predictive compensation for spring-like loads during goal-directed wrist movements in neurologically-intact humans. Although load changed unpredictably from one trial to the next, subjects nevertheless used sensorimotor memories from recent movements to predict and compensate upcoming loads. Prediction enabled subjects to adapt performance so that the task was accomplished with minimum effort. Population analyses of functional images revealed a distributed, bilateral network of cortical and subcortical activity supporting predictive load compensation during visual target capture. Cortical regions – including prefrontal, parietal and hippocampal cortices – exhibited trial-by-trial fluctuations in BOLD signal consistent with the storage and recall of sensorimotor memories or “states” important for spatial working memory. Bilateral activations in associative regions of the striatum demonstrated temporal correlation with the magnitude of kinematic performance error (a signal that could drive reward-optimizing reinforcement learning and the prospective scaling of previously learned motor programs). BOLD signal correlations with load prediction were observed in the cerebellar cortex and red nuclei (consistent with the idea that these structures generate adaptive fusimotor signals facilitating cancelation of expected proprioceptive feedback, as required for conditional feedback adjustments to ongoing motor commands and feedback error learning). Analysis of single subject images revealed that predictive activity was at least as likely to be observed in more than one of these neural systems as in just one. We conclude therefore that motor adaptation is mediated by predictive compensations supported by multiple, distributed, cortical and subcortical structures

    Complementary Roles of Hippocampus and Medial Entorhinal Cortex in Episodic Memory

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    Spatial mapping and navigation are figured prominently in the extant literature that describes hippocampal function. The medial entorhinal cortex is likewise attracting increasing interest, insofar as evidence accumulates that this area also contributes to spatial information processing. Here, we discuss recent electrophysiological findings that offer an alternate view of hippocampal and medial entorhinal function. These findings suggest complementary contributions of the hippocampus and medial entorhinal cortex in support of episodic memory, wherein hippocampal networks encode sequences of events that compose temporally and spatially extended episodes, whereas medial entorhinal networks disambiguate overlapping episodes by binding sequential events into distinct memories.National Institute of Mental Health Grants (MH51570, MH071702); National Science Foundation (Science of Learning Center grant SBE-0354378

    Memory formation in matter

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    Memory formation in matter is a theme of broad intellectual relevance; it sits at the interdisciplinary crossroads of physics, biology, chemistry, and computer science. Memory connotes the ability to encode, access, and erase signatures of past history in the state of a system. Once the system has completely relaxed to thermal equilibrium, it is no longer able to recall aspects of its evolution. Memory of initial conditions or previous training protocols will be lost. Thus many forms of memory are intrinsically tied to far-from-equilibrium behavior and to transient response to a perturbation. This general behavior arises in diverse contexts in condensed matter physics and materials: phase change memory, shape memory, echoes, memory effects in glasses, return-point memory in disordered magnets, as well as related contexts in computer science. Yet, as opposed to the situation in biology, there is currently no common categorization and description of the memory behavior that appears to be prevalent throughout condensed-matter systems. Here we focus on material memories. We will describe the basic phenomenology of a few of the known behaviors that can be understood as constituting a memory. We hope that this will be a guide towards developing the unifying conceptual underpinnings for a broad understanding of memory effects that appear in materials

    Introduction to a system for implementing neural net connections on SIMD architectures

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    Neural networks have attracted much interest recently, and using parallel architectures to simulate neural networks is a natural and necessary application. The SIMD model of parallel computation is chosen, because systems of this type can be built with large numbers of processing elements. However, such systems are not naturally suited to generalized communication. A method is proposed that allows an implementation of neural network connections on massively parallel SIMD architectures. The key to this system is an algorithm permitting the formation of arbitrary connections between the neurons. A feature is the ability to add new connections quickly. It also has error recovery ability and is robust over a variety of network topologies. Simulations of the general connection system, and its implementation on the Connection Machine, indicate that the time and space requirements are proportional to the product of the average number of connections per neuron and the diameter of the interconnection network

    Automata theoretic aspects of temporal behaviour and computability in logical neural networks

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    Deciphering the Firing Patterns of Hippocampal Neurons During Sharp-Wave Ripples

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    The hippocampus is essential for learning and memory. Neurons in the rat hippocampus selectively fire when the animal is at specific locations - place fields - within an environment. Place fields corresponding to such place cells tile the entire environment, forming a stable spatial map supporting navigation and planning. Remarkably, the same place cells reactivate together outside of their place fields and in coincidence with sharp-wave ripples (SWRs) - dominant electrical field oscillations (150-250 Hz) in the hippocampus. These offline SWR events frequently occur during quiet wake periods in the middle of exploration and the follow-up slow-wave sleep and are associated with spatial memory performance and stabilization of spatial maps. Therefore, deciphering the firing patterns during these events is essential to understanding offline memory processing.I provide two novel methods to analyze the SWRs firing patterns in this dissertation project. The first method uses hidden Markov models (HMM), in which I model the dynamics of neural activity during SWRs in terms of transitions between distinct states of neuronal ensemble activity. This method detects consistent temporal structures over many instances of SWRs and, in contrast to standard approaches, relaxes the dependence on positional data during the behavior to interpret temporal patterns during SWRs. To validate this method, I applied the method to quiet wake SWRs. In a simple spatial memory task in which the animal ran on a linear track or in an open arena, the individual states corresponded to the activation of distinct group of neurons with inter-state transitions that resembled the animal’s trajectories during the exploration. In other words, this method enabled us to identify the topology and spatial map of the explored environment by dissecting the firings occurring during the quiescence periods’ SWRs. This result indicated that downstream brain regions may rely only on SWRs to uncover hippocampal code as a substrate for memory processing. I developed a second analysis method based on the principles of Bayesian learning. This method enabled us to track the spatial tunings over the sleep following exploration of an environment by taking neurons’ place fields in the environment as the prior belief and updating it using dynamic ensemble firing patterns unfolding over time. This method introduces a neuronal-ensemble-based approach that calculates tunings to the position encoded by ensemble firings during sleep rather than the animal’s actual position during exploration. When I applied this method to several datasets, I found that during the early slow-wave sleep after an experience, but not during late hours of sleep or sleep before the exploration, the spatial tunings highly resembled the place fields on the track. Furthermore, the fidelity of the spatial tunings to the place fields predicted the place fields’ stability when the animal was re-exposed to the same environment after ~ 9h. Moreover, even for neurons with shifted place fields during re-exposure, the spatial tunings during early sleep were predictive of the place fields during the re-exposure. These results indicated that early sleep actively maintains or retunes the place fields of neurons, explaining the representational drift of place fields across multiple exposures

    Complementary Roles of Hippocampus and Medial Entorhinal Cortex in Episodic Memory

    Get PDF
    Spatial mapping and navigation are figured prominently in the extant literature that describes hippocampal function. The medial entorhinal cortex is likewise attracting increasing interest, insofar as evidence accumulates that this area also contributes to spatial information processing. Here, we discuss recent electrophysiological findings that offer an alternate view of hippocampal and medial entorhinal function. These findings suggest complementary contributions of the hippocampus and medial entorhinal cortex in support of episodic memory, wherein hippocampal networks encode sequences of events that compose temporally and spatially extended episodes, whereas medial entorhinal networks disambiguate overlapping episodes by binding sequential events into distinct memories

    Dissipation and spontaneous symmetry breaking in brain dynamics

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    We compare the predictions of the dissipative quantum model of brain with neurophysiological data collected from electroencephalograms resulting from high-density arrays fixed on the surfaces of primary sensory and limbic areas of trained rabbits and cats. Functional brain imaging in relation to behavior reveals the formation of coherent domains of synchronized neuronal oscillatory activity and phase transitions predicted by the dissipative model.Comment: Restyled, slight changes in title and abstract, updated bibliography, J. Phys. A: Math. Theor. Vol. 41 (2008) in prin
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