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Matthew A Wilson

Publications and source records attributed to Matthew A Wilson.

At least 19 recordsLinked to original sources
PubMed2007

Spatial selectivity and theta phase precession in CA1 interneurons.

Traditionally, most of the information processing of neural networks is thought to be carried out by excitatory cells. Likewise, recent evidence for temporal coding comes from the study of the detailed firing patterns of excitatory neurons. In the CA1 region of the rat hippocampus, pyramidal cells discharge selectively when the animal is in specific locations in its environment, and exhibit a precise relationship with the ongoing rhythmic activity of the network (phase precession). We demonstrate that during a spatial exploratory behavior on a linear track, inhibitory interneurons also show spatial selectivity and phase precession dynamics. We found that the firing rate of interneurons is modulated reliably up and down around an ongoing baseline activity level for specific locations in the environment, producing robust place-specific increases or decreases in discharge. On some sections of the track, the range of theta phases shifts progressively to earlier parts of the theta cycle as the rat advances, so that a negative correlation between phase and position could be demonstrated. Unlike pyramidal cells, phase and rate were not strongly correlated. We discuss the influence of the intrinsic firing properties of interneurons on a model of phase precession, as well as the influence of the detailed shape of the inhibitory oscillation. These results indicate that spatial selectivity and phase precession in CA1 are not properties restricted to pyramidal cells. Rather, they may be a more general expression of a common interaction between the different inputs impinging on both excitatory and inhibitory cells in CA1 and the intrinsic characteristics of those cells. Furthermore, they suggest that the role of interneurons may extend beyond a global damping of the network by participating in a finely-tuned local processing with the pyramidal cells.

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PubMed2006

Coordinated memory replay in the visual cortex and hippocampus during sleep.

Sleep replay of awake experience in the cortex and hippocampus has been proposed to be involved in memory consolidation. However, whether temporally structured replay occurs in the cortex and whether the replay events in the two areas are related are unknown. Here we studied multicell spiking patterns in both the visual cortex and hippocampus during slow-wave sleep in rats. We found that spiking patterns not only in the cortex but also in the hippocampus were organized into frames, defined as periods of stepwise increase in neuronal population activity. The multicell firing sequences evoked by awake experience were replayed during these frames in both regions. Furthermore, replay events in the sensory cortex and hippocampus were coordinated to reflect the same experience. These results imply simultaneous reactivation of coherent memory traces in the cortex and hippocampus during sleep that may contribute to or reflect the result of the memory consolidation process.

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PubMed2006

Neuroscience and architecture: seeking common ground.

As these paired Commentaries discuss, neuroscientists and architects are just beginning to collaborate, each bringing what they know about their respective fields to the task of improving the environment of research buildings and laboratories.

Architecture↗
PubMed2006

Reverse replay of behavioural sequences in hippocampal place cells during the awake state.

The hippocampus has long been known to be involved in spatial navigational learning in rodents, and in memory for events in rodents, primates and humans. A unifying property of both navigation and event memory is a requirement for dealing with temporally sequenced information. Reactivation of temporally sequenced memories for previous behavioural experiences has been reported in sleep in rats. Here we report that sequential replay occurs in the rat hippocampus during awake periods immediately after spatial experience. This replay has a unique form, in which recent episodes of spatial experience are replayed in a temporally reversed order. This replay is suggestive of a role in the evaluation of event sequences in the manner of reinforcement learning models. We propose that such replay might constitute a general mechanism of learning and memory.

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PubMed2005

Theta rhythms coordinate hippocampal-prefrontal interactions in a spatial memory task.

Decision-making requires the coordinated activity of diverse brain structures. For example, in maze-based tasks, the prefrontal cortex must integrate spatial information encoded in the hippocampus with mnemonic information concerning route and task rules in order to direct behavior appropriately. Using simultaneous tetrode recordings from CA1 of the rat hippocampus and medial prefrontal cortex, we show that correlated firing in the two structures is selectively enhanced during behavior that recruits spatial working memory, allowing the integration of hippocampal spatial information into a broader, decision-making network. The increased correlations are paralleled by enhanced coupling of the two structures in the 4- to 12-Hz theta-frequency range. Thus the coordination of theta rhythms may constitute a general mechanism through which the relative timing of disparate neural activities can be controlled, allowing specialized brain structures to both encode information independently and to interact selectively according to current behavioral demands.

Animals↗
PubMed2005

Prefrontal phase locking to hippocampal theta oscillations.

The interactions between cortical and hippocampal circuits are critical for memory formation, yet their basic organization at the neuronal network level is not well understood. Here, we demonstrate that a significant portion of neurons in the medial prefrontal cortex of freely behaving rats are phase locked to the hippocampal theta rhythm. In addition, we show that prefrontal neurons phase lock best to theta oscillations delayed by approximately 50 ms and confirm this hippocampo-prefrontal directionality and timing at the level of correlations between single cells. Finally, we find that phase locking of prefrontal cells is predicted by the presence of significant correlations with hippocampal cells at positive delays up to 150 ms. The theta-entrained activity across cortico-hippocampal circuits described here may be important for gating information flow and guiding the plastic changes that are believed to underlie the storage of information across these networks.

Animals↗
PubMed2005

Phase precession of medial prefrontal cortical activity relative to the hippocampal theta rhythm.

Theta phase-locking and phase precession are two related phenomena reflecting coordination of hippocampal place cell firing with the local, ongoing theta rhythm. The mechanisms and functions of both the phenomena remain unclear, though the robust correlation between firing phase and location of the animal has lead to the suggestion that this phase relationship constitutes a temporal code for spatial information. Recent work has described theta phase-locking in the rat medial prefrontal cortex (mPFC), a structure with direct anatomical and functional links to the hippocampus. Here, we describe an initial characterization of phase precession in the mPFC relative to the CA1 theta rhythm. mPFC phase precession was most robust during behavioral epochs known to be associated with enhanced theta-frequency coordination of CA1 and mPFC activities. Precession was coherent across the mPFC population, with multiple neurons precessing in parallel as a function of location of the animal. The existence of phase precession beyond the hippocampus implies a more global role for this phenomenon during theta rhythm-mediated coordination of neural activity.

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PubMed2005

Jugular foramen schwannomas: diagnosis, management, and outcomes.

OBJECTIVES/HYPOTHESIS: To describe the presentation, radiographic findings, and surgical management of seven patients who have been diagnosed and treated with jugular foramen schwannomas at the University of Utah. STUDY DESIGN: Retrospective chart review. METHODS: The charts of seven patients diagnosed with jugular foramen schwannomas were reviewed for presentation symptoms, radiographic findings, and physical examination findings. For the six who underwent surgical excision, the surgical procedure used, cranial nerve function results, audiometric results, perioperative complications, and other follow-up data are presented. RESULTS: Seven patients were identified from ages 24 to 69 years. Six of the seven underwent surgical excision. Primary presentation symptoms included dizziness, hearing loss, dysphagia, diplopia, tongue paresis, and hoarseness. The choice of surgical approach was based on the size and location of the tumor. All patients had complete excision of their tumors. The nerve of origin included the glossopharyngeal, vagus, and spinal accessory nerves. Preoperative cranial nerve dysfunction continued postoperatively for lower cranial nerves but resolved in patients who were noted to have preoperative dysfunction of cranial nerve V and VI. The rate of new lower cranial nerve injury was 15% and was only seen in the cranial nerves that were determined to be the nerve of origin. In two cases, a temporary feeding tube was required. No recurrences have been noted to date. CONCLUSIONS: Jugular foramen schwannomas can be successfully diagnosed preoperatively with computed tomography and magnetic resonance imaging. These tumors can be successfully managed with surgery and low morbidity.

Adult↗
PubMed2005

An analysis of hippocampal spatio-temporal representations using a Bayesian algorithm for neural spike train decoding.

Neural spike train decoding algorithms are important tools for characterizing how ensembles of neurons represent biological signals. We present a Bayesian neural spike train decoding algorithm based on a point process model of individual neurons, a linear stochastic state-space model of the biological signal, and a temporal latency parameter. The latency parameter represents the temporal lead or lag between the biological signal and the ensemble spiking activity. We use the algorithm to study whether the representation of position by the ensemble spiking activity of pyramidal neurons in the CA1 region of the rat hippocampus is more consistent with prospective coding, i.e., future position, or retrospective coding, past position. Using 44 simultaneously recorded neurons and an ensemble delay latency of 400 ms, the median decoding error was 5.1 cm during 10 min of foraging in an open circular environment. The true coverage probability for the algorithm's 0.95 confidence regions was 0.71. These results illustrate how the Bayesian neural spike train decoding paradigm may be used to investigate spatio-temporal representations of position by an ensemble of hippocampal neurons.

Algorithms↗
PubMed2005

Analyzing functional connectivity using a network likelihood model of ensemble neural spiking activity.

Analyzing the dependencies between spike trains is an important step in understanding how neurons work in concert to represent biological signals. Usually this is done for pairs of neurons at a time using correlation-based techniques. Chornoboy, Schramm, and Karr (1988) proposed maximum likelihood methods for the simultaneous analysis of multiple pair-wise interactions among an ensemble of neurons. One of these methods is an iterative, continuous-time estimation algorithm for a network likelihood model formulated in terms of multiplicative conditional intensity functions. We devised a discrete-time version of this algorithm that includes a new, efficient computational strategy, a principled method to compute starting values, and a principled stopping criterion. In an analysis of simulated neural spike trains from ensembles of interacting neurons, the algorithm recovered the correct connectivity matrices and interaction parameters. In the analysis of spike trains from an ensemble of rat hippocampal place cells, the algorithm identified a connectivity matrix and interaction parameters consistent with the pattern of conjoined firing predicted by the overlap of the neurons' spatial receptive fields. These results suggest that the network likelihood model can be an efficient tool for the analysis of ensemble spiking activity.

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PubMed2004

A combinatorial method for analyzing sequential firing patterns involving an arbitrary number of neurons based on relative time order.

Information processing in the brain is believed to require coordinated activity across many neurons. With the recent development of techniques for simultaneously recording the spiking activity of large numbers of individual neurons, the search for complex multicell firing patterns that could help reveal this neural code has become possible. Here we develop a new approach for analyzing sequential firing patterns involving an arbitrary number of neurons based on relative firing order. Specifically, we develop a combinatorial method for quantifying the degree of matching between a "reference sequence" of N distinct "letters" (representing a particular target order of firing by N cells) and an arbitrarily long "word" composed of any subset of those letters including repeats (representing the relative time order of spikes in an arbitrary firing pattern). The method involves computing the probability that a random permutation of the word's letters would by chance alone match the reference sequence as well as or better than the actual word does, assuming all permutations were equally likely. Lower probabilities thus indicate better matching. The overall degree and statistical significance of sequence matching across a heterogeneous set of words (such as those produced during the course of an experiment) can be computed from the corresponding set of probabilities. This approach can reduce the sample size problem associated with analyzing complex firing patterns. The approach is general and thus applicable to other types of neural data beyond multiple spike trains, such as EEG events or imaging signals from multiple locations. We have recently applied this method to quantify memory traces of sequential experience in the rodent hippocampus during slow wave sleep.

Algorithms↗
PubMed2004

Dynamic analyses of information encoding in neural ensembles.

Neural spike train decoding algorithms and techniques to compute Shannon mutual information are important methods for analyzing how neural systems represent biological signals. Decoding algorithms are also one of several strategies being used to design controls for brain-machine interfaces. Developing optimal strategies to design decoding algorithms and compute mutual information are therefore important problems in computational neuroscience. We present a general recursive filter decoding algorithm based on a point process model of individual neuron spiking activity and a linear stochastic state-space model of the biological signal. We derive from the algorithm new instantaneous estimates of the entropy, entropy rate, and the mutual information between the signal and the ensemble spiking activity. We assess the accuracy of the algorithm by computing, along with the decoding error, the true coverage probability of the approximate 0.95 confidence regions for the individual signal estimates. We illustrate the new algorithm by reanalyzing the position and ensemble neural spiking activity of CA1 hippocampal neurons from two rats foraging in an open circular environment. We compare the performance of this algorithm with a linear filter constructed by the widely used reverse correlation method. The median decoding error for Animal 1 (2) during 10 minutes of open foraging was 5.9 (5.5) cm, the median entropy was 6.9 (7.0) bits, the median information was 9.4 (9.4) bits, and the true coverage probability for 0.95 confidence regions was 0.67 (0.75) using 34 (32) neurons. These findings improve significantly on our previous results and suggest an integrated approach to dynamically reading neural codes, measuring their properties, and quantifying the accuracy with which encoded information is extracted.

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PubMed2003

Genetic neuroscience of mammalian learning and memory.

Our primary research interest is to understand the molecular and cellular mechanisms on neuronal circuitry underlying the acquisition, consolidation and retrieval of hippocampus-dependent memory in rodents. We study these problems by producing genetically engineered (i.e. spatially targeted and/or temporally restricted) mice and analysing these mice by multifaceted methods including molecular and cellular biology, in vitro and in vivo physiology and behavioural studies. We attempt to identify deficits at each of the multiple levels of complexity in specific brain areas or cell types and deduce those deficits that underlie specific learning or memory. We will review our recent studies on the acquisition, consolidation and recall of memories that have been conducted with mouse strains in which genetic manipulations were targeted to specific types of cells in the hippocampus or forebrain of young adult mice.

Animals↗
PubMed2003

Hippocampal CA3 NMDA receptors are crucial for memory acquisition of one-time experience.

Lesion and pharmacological intervention studies have suggested that in both human patients and animals the hippocampus plays a crucial role in the rapid acquisition and storage of information from a novel one-time experience. However, how the hippocampus plays this role is poorly known. Here, we show that mice with NMDA receptor (NR) deletion restricted to CA3 pyramidal cells in adulthood are impaired in rapidly acquiring the memory of novel hidden platform locations in a delayed matching-to-place version of the Morris water maze task but are normal when tested with previously experienced platform locations. CA1 place cells in the mutant animals had place field sizes that were significantly larger in novel environments, but normal in familiar environments relative to those of control mice. These results suggest that CA3 NRs play a crucial role in rapid hippocampal encoding of novel information for fast learning of one-time experience.

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PubMed2002

Memory of sequential experience in the hippocampus during slow wave sleep.

Rats repeatedly ran through a sequence of spatial receptive fields of hippocampal CA1 place cells in a fixed temporal order. A novel combinatorial decoding method reveals that these neurons repeatedly fired in precisely this order in long sequences involving four or more cells during slow wave sleep (SWS) immediately following, but not preceding, the experience. The SWS sequences occurred intermittently in brief ( approximately 100 ms) bursts, each compressing the behavioral sequence in time by approximately 20-fold. This rapid encoding of sequential experience is consistent with evidence that the hippocampus is crucial for spatial learning in rodents and the formation of long-term memories of events in time in humans.

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PubMed2002

Requirement for hippocampal CA3 NMDA receptors in associative memory recall.

Pattern completion, the ability to retrieve complete memories on the basis of incomplete sets of cues, is a crucial function of biological memory systems. The extensive recurrent connectivity of the CA3 area of hippocampus has led to suggestions that it might provide this function. We have tested this hypothesis by generating and analyzing a genetically engineered mouse strain in which the N-methyl-D-asparate (NMDA) receptor gene is ablated specifically in the CA3 pyramidal cells of adult mice. The mutant mice normally acquired and retrieved spatial reference memory in the Morris water maze, but they were impaired in retrieving this memory when presented with a fraction of the original cues. Similarly, hippocampal CA1 pyramidal cells in mutant mice displayed normal place-related activity in a full-cue environment but showed a reduction in activity upon partial cue removal. These results provide direct evidence for CA3 NMDA receptor involvement in associative memory recall.

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PubMed2002

Contrasting patterns of receptive field plasticity in the hippocampus and the entorhinal cortex: an adaptive filtering approach.

Neural receptive fields are frequently plastic: a neural response to a stimulus can change over time as a result of experience. We developed an adaptive point process filtering algorithm that allowed us to estimate the dynamics of both the spatial receptive field (spatial intensity function) and the interspike interval structure (temporal intensity function) of neural spike trains on a millisecond time scale without binning over time or space. We applied this algorithm to both simulated data and recordings of putative excitatory neurons from the CA1 region of the hippocampus and the deep layers of the entorhinal cortex (EC) of awake, behaving rats. Our simulation results demonstrate that the algorithm accurately tracks simultaneous changes in the spatial and temporal structure of the spike train. When we applied the algorithm to experimental data, we found consistent patterns of plasticity in the spatial and temporal intensity functions of both CA1 and deep EC neurons. These patterns tended to be opposite in sign, in that the spatial intensity functions of CA1 neurons showed a consistent increase over time, whereas those of deep EC neurons tended to decrease, and the temporal intensity functions of CA1 neurons showed a consistent increase only in the "theta" (75-150 msec) region, whereas those of deep EC neurons decreased in the region between 20 and 75 msec. In addition, the minority of deep EC neurons whose spatial intensity functions increased in area over time fired in a significantly more spatially specific manner than non-increasing deep EC neurons. We hypothesize that this subset of deep EC neurons may receive more direct input from CA1 and may be part of a neural circuit that transmits information about the animal's location to the neocortex.

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