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ABSTRACT: Neural decoding is an important approach for extracting information from population codes. We previously proposed a novel transductive neural decoding paradigm and applied it to reconstruct the rat's position during navigation based on unsorted rat hippocampal ensemble spiking activity. Here, we investigate several important technical issues of this new paradigm using one data set of one animal. Several extensions of our decoding method are discussed.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2012; 2012:1310-3.
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ABSTRACT: Hippocampal population codes play an important role in representation of spatial environment and spatial navigation. Uncovering the internal representation of hippocampal population codes will help understand neural mechanisms of the hippocampus. For instance, uncovering the patterns represented by rat hippocampus (CA1) pyramidal cells during periods of either navigation or sleep has been an active research topic over the past decades. However, previous approaches to analyze or decode firing patterns of population neurons all assume the knowledge of the place fields, which are estimated from training data a priori. The question still remains unclear how can we extract information from population neuronal responses either without a priori knowledge or in the presence of finite sampling constraint. Finding the answer to this question would leverage our ability to examine the population neuronal codes under different experimental conditions. Using rat hippocampus as a model system, we attempt to uncover the hidden "spatial topology" represented by the hippocampal population codes. We develop a hidden Markov model (HMM) and a variational Bayesian (VB) inference algorithm to achieve this computational goal, and we apply the analysis to extensive simulation and experimental data. Our empirical results show promising direction for discovering structural patterns of ensemble spike activity during periods of active navigation. This study would also provide useful insights for future exploratory data analysis of population neuronal codes during periods of sleep.
Journal of Computational Neuroscience 02/2012; 33(2):227-55. · 2.51 Impact Factor
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ABSTRACT: Characterizing neural spiking activity as a function of intrinsic and extrinsic factors is important in neuroscience. Point process models are valuable for capturing such information; however, the process of fully applying these models is not always obvious. A complete model application has four broad steps: specification of the model, estimation of model parameters given observed data, verification of the model using goodness of fit, and characterization of the model using confidence bounds. Of these steps, only the first three have been applied widely in the literature, suggesting the need to dedicate a discussion to how the time-rescaling theorem, in combination with parametric bootstrap sampling, can be generally used to compute confidence bounds of point process models. In our first example, we use a generalized linear model of spiking propensity to demonstrate that confidence bounds derived from bootstrap simulations are consistent with those computed from closed-form analytic solutions. In our second example, we consider an adaptive point process model of hippocampal place field plasticity for which no analytical confidence bounds can be derived. We demonstrate how to simulate bootstrap samples from adaptive point process models, how to use these samples to generate confidence bounds, and how to statistically test the hypothesis that neural representations at two time points are significantly different. These examples have been designed as useful guides for performing scientific inference based on point process models.
Neural Computation 08/2011; 23(11):2731-45. · 1.88 Impact Factor
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ABSTRACT: Understanding the way in which groups of cortical neurons change their individual and mutual firing activity during the induction of general anesthesia may improve the safe usage of many anesthetic agents. Assessing neuronal interactions within cell assemblies during anesthesia may be useful for understanding the neural mechanisms of general anesthesia. Here, a point process generalized linear model (PPGLM) was applied to infer the functional connectivity of neuronal ensembles during both baseline and anesthesia, in which neuronal firing rates and network connectivity might change dramatically. A hierarchical Bayesian modeling approach combined with a variational Bayes (VB) algorithm is used for statistical inference. The effectiveness of our approach is evaluated with synthetic spike train data drawn from small and medium-size networks (consisting of up to 200 neurons), which are simulated using biophysical voltage-gated conductance models. We further apply the analysis to experimental spike train data recorded from rats' barrel cortex during both active behavior and isoflurane anesthesia conditions. Our results suggest that that neuronal interactions of both putative excitatory and inhibitory connections are reduced after the induction of isoflurane anesthesia.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2011; 2011:4175-8.
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ABSTRACT: The rate at which neurons fire has wide-reaching implications for the coding schemes used by neural systems. Despite the extensive use of the barrel cortex as a model system, relatively few studies have examined the rate of sensory activity in single neurons in freely moving animals. We examined the activity of barrel cortex neurons in behaving animals during sensory cue interaction, during non-stimulus-related activity, during various states of sleep, and during the administration of isoflurane. The activity of regular-spiking units (RSUs: predominantly excitatory neurons) and fast spiking units (FSUs: a subtype of inhibitory interneurons) was examined separately. We characterized activity by calculating neural firing rates, because several reports have emphasized the low firing rates in this system, reporting that both baseline activity and stimulus evoked activity is <1 Hz. We report that, during sensory cue interaction or non-stimulus-related activity, the majority of RSUs in rat barrel cortex fired at rates significantly >1 Hz, with 27.4% showing rates above 10 Hz during cue interaction. Even during slow wave sleep, which had the lowest mean and median firing rates of any nonanesthetized state observed, 80.0% of RSUs fired above 1 Hz. During all of the nonanesthetized states observed 100% of the FSUs fired well above 1 Hz. When rats were administered isoflurane and at a depth of anesthesia used in standard in vivo electrophysiological preparations, all of the RSUs fired below 1 Hz. We also found that >80% of RSUs either upmodulated or downmodulated their firing during cue interaction. These data suggest that low firing rates do not typify the output of the barrel cortex during awake activity and during sleep and indicate that sensory coding at both the individual and population levels may be nonsparse.
Journal of Neurophysiology 02/2010; 103(4):2074-84. · 3.32 Impact Factor
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Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010, 14-19 March 2010, Sheraton Dallas Hotel, Dallas, Texas, USA; 01/2010
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ABSTRACT: The hippocampus plays a key role in the acquisition of new memories for places and events. Evidence suggests that the consolidation of these memories is enhanced during sleep. At the neuronal level, reactivation of awake experience in the hippocampus during sharp-wave ripple events, characteristic of slow-wave sleep, has been proposed as a neural mechanism for sleep-dependent memory consolidation. However, a causal relation between sleep reactivation and memory consolidation has not been established. Here we show that disrupting neuronal activity during ripple events impairs spatial learning. We trained rats daily in two identical spatial navigation tasks followed each by a 1-hour rest period. After one of the tasks, stimulation of hippocampal afferents selectively disrupted neuronal activity associated with ripple events without changing the sleep-wake structure. Rats learned the control task significantly faster than the task followed by rest stimulation, indicating that interfering with hippocampal processing during sleep led to decreased learning.
Hippocampus 10/2009; 20(1):1-10. · 5.18 Impact Factor
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ABSTRACT: Gamma oscillations are a prominent feature of hippocampal network activity, but their functional role remains debated, ranging from mere epiphenomena to being crucial for information processing. Similarly, persistent gamma oscillations sometimes appear prior to epileptic discharges in patients with mesial temporal sclerosis. However, the significance of this activity in hippocampal excitotoxicity is unclear. We assessed the relationship between kainic acid (KA)-induced gamma oscillations and excitotoxicity in genetically engineered mice in which N-methyl-D-aspartic acid receptor deletion was confined to CA3 pyramidal cells. Mutants showed reduced CA3 pyramidal cell firing and augmented sharp wave-ripple activity, resulting in higher susceptibility to KA-induced seizures, and leading to strikingly selective neurodegeneration in the CA1 subfield. Interestingly, the increase in KA-induced gamma-aminobutyric acid (GABA) levels, and the persistent 30-50-Hz gamma oscillations, both of which were observed in control mice prior to the first seizure discharge, were abolished in the mutants. Consequently, on subsequent days, mutants manifested prolonged epileptiform activity and massive neurodegeneration of CA1 cells, including local GABAergic neurons. Remarkably, pretreatment with the potassium channel blocker alpha-dendrotoxin increased GABA levels, restored gamma oscillations, and prevented CA1 degeneration in the mutants. These results demonstrate that the emergence of low-frequency gamma oscillations predicts increased resistance to KA-induced excitotoxicity, raising the possibility that gamma oscillations may have potential prognostic value in the treatment of epilepsy.
European Journal of Neuroscience 10/2009; 30(6):1036-55. · 3.63 Impact Factor
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ABSTRACT: During pauses in exploration, ensembles of place cells in the rat hippocampus re-express firing sequences corresponding to recent spatial experience. Such "replay" co-occurs with ripple events: short-lasting (approximately 50-120 ms), high-frequency (approximately 200 Hz) oscillations that are associated with increased hippocampal-cortical communication. In previous studies, rats exploring small environments showed replay anchored to the rat's current location and compressed in time into a single ripple event. Here, we show, using a neural decoding approach, that firing sequences corresponding to long runs through a large environment are replayed with high fidelity and that such replay can begin at remote locations on the track. Extended replay proceeds at a characteristic virtual speed of approximately 8 m/s and remains coherent across trains of ripple events. These results suggest that extended replay is composed of chains of shorter subsequences, which may reflect a strategy for the storage and flexible expression of memories of prolonged experience.
Neuron 09/2009; 63(4):497-507. · 14.74 Impact Factor
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ABSTRACT: UP and DOWN states, the periodic fluctuations between increased and decreased spiking activity of a neuronal population, are a fundamental feature of cortical circuits. Understanding UP-DOWN state dynamics is important for understanding how these circuits represent and transmit information in the brain. To date, limited work has been done on characterizing the stochastic properties of UP-DOWN state dynamics. We present a set of Markov and semi-Markov discrete- and continuous-time probability models for estimating UP and DOWN states from multiunit neural spiking activity. We model multiunit neural spiking activity as a stochastic point process, modulated by the hidden (UP and DOWN) states and the ensemble spiking history. We estimate jointly the hidden states and the model parameters by maximum likelihood using an expectation-maximization (EM) algorithm and a Monte Carlo EM algorithm that uses reversible-jump Markov chain Monte Carlo sampling in the E-step. We apply our models and algorithms in the analysis of both simulated multiunit spiking activity and actual multi- unit spiking activity recorded from primary somatosensory cortex in a behaving rat during slow-wave sleep. Our approach provides a statistical characterization of UP-DOWN state dynamics that can serve as a basis for verifying and refining mechanistic descriptions of this process.
Neural Computation 04/2009; 21(7):1797-862. · 1.88 Impact Factor
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ABSTRACT: Chronic recording of large populations of neurons is a valuable technique for studying the function of neuronal circuits in awake behaving rats. Lightweight recording devices carrying a high density array of tetrodes allow for the simultaneous monitoring of the activity of tens to hundreds of individual neurons. Here we describe a protocol for the fabrication of a micro-drive array with twenty one independently movable micro-drives. This device has been used successfully to record from hippocampal and cortical neurons in our lab. We show how to prepare a custom designed, 3-D printed plastic base that will hold the micro-drives. We demonstrate how to construct the individual micro-drives and how to assemble the complete micro-drive array. Further preparation of the drive array for surgical implantation, such as the fabrication of tetrodes, loading of tetrodes into the drive array and gold-plating, is covered in a subsequent video article.
Journal of Visualized Experiments 02/2009;
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ABSTRACT: The tetrode, a bundle of four electrodes, has proven to be a valuable tool for the simultaneous recording of multiple neurons in-vivo. The differential amplitude of action potential signatures over the channels of a tetrode allows for the isolation of single-unit activity from multi-unit signals. The ability to precisely control the stereotaxic location and depth of the tetrode is critical for studying coordinated neural activity across brain regions. In combination with a micro-drive array, it is possible to achieve precise placement and stable control of many tetrodes over the course of days to weeks. In this protocol, we demonstrate how to fabricate and condition tetrodes using basic tools and materials, install the tetrodes into a multi-drive tetrode array for chronic in-vivo recording in the rat, make ground wire connections to the micro-drive array, and attach a protective cone onto the micro-drive array in order to protect the tetrodes from physical contact with the environment.
Journal of Visualized Experiments 02/2009;
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ABSTRACT: Fast oscillations or "ripples" are found in the local field potential (LFP) of the rodent hippocampus during awake and sleep states. Ripples have been found to correlate with memory related neural processing, however, the functional role of the ripple has yet to be fully established. We applied a Kalman smoother based estimator of instantaneous frequency (iFreq) and frequency modulation (FM) to ripple oscillations recorded in-vivo from region CA1 of the rat and mouse hippocampus during slow wave sleep. We found that (1) ripples exhibit stereotypical frequency dynamics that are consistent in the rat and mouse, (2) instantaneous frequency information may be used as an additional dimension in the classification of ripple events, and (3) the instantaneous frequency structure of ripples may be used to improve the detection of ripple events by reducing Type I and Type II errors. Based on our results, we propose that high temporal and spectral resolution estimates of frequency dynamics may be used to help elucidate the mechanisms of ripple generation and memory related processing.
Frontiers in Integrative Neuroscience 02/2009; 3:11.
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ABSTRACT: Much of the work in systems neuroscience thus far has focused on the brain's parts studied individually. The past 20 years has seen the advent, rise, and application of multiple-electrode technology. This allows the study of the activity of many neurons simultaneously, which in turn has provided insight into how different neuron populations interact and collaborate to produce thought and action.
Neuron 12/2008; 60(3):483-8. · 14.74 Impact Factor
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ABSTRACT: EEG and LFP activity reflect the dynamic and organized interactions of neural ensembles; therefore, it may be possible to use the features of brain rhythms to determine the computational state of a neuronal network. When neuronal networks are activated, physical principles predict that the frequency content of the field potential should reflect the network state, per se, and ergo the state transition. A novel way for characterizing brain states is by quantifying the temporal structure of AM and FM activity (change in amplitude and frequency over time) for brain rhythms of interest. The concept of AM and FM, in the quantitative sense, is virtually unexplored in systems neuroscience. This is not surprising considering estimation of FM activity requires fine temporal and precise estimation of instantaneous frequency. For AM activity, the absolute value of the Hilbert transform is sufficient. Here, we outline a practical pole tracking algorithm which uses a Kalman filter for univariate AR processes to estimate instantaneous frequency. We demonstrate the filter performance using simulated chirp and real EEG/LFP data recorded from the rat hippocampus; and show that AM/FM activity in EEG/LFP is temporally structured and dependent on behavioral and cognitive state. This algorithm has the potential to be a practical tool for characterizing fundamental structure in electrophysiology data and classifying computational states in the brain.
Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE; 09/2008
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ABSTRACT: Multiple single-unit recording has become one of the most powerful in vivo electro-physiological techniques for studying neural circuits. The demand has been increasing for small and lightweight chronic recording devices that allow fine adjustments to be made over large numbers of electrodes across multiple brain regions. To achieve this, we developed precision motorized microdrive arrays that use a novel motor multiplexing headstage to dramatically reduce wiring while preserving precision of the microdrive control. Versions of the microdrive array were chronically implanted on both rats (21 microdrives) and mice (7 microdrives), and relatively long-term recordings were taken.
Journal of Neurophysiology 08/2008; 100(4):2430-40. · 3.32 Impact Factor
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ABSTRACT: The hippocampus is essential for spatial navigation, which may involve sequential learning. However, how the hippocampus encodes new sequences in familiar environments is unknown. To study the impact of novel spatial sequences on the activity of hippocampal neurons, we monitored hippocampal ensembles while rats learned to switch from two familiar trajectories to a new one in a familiar environment. Here, we show that this novel spatial experience induces two types of changes in firing rates, but not locations of hippocampal place cells. First, place-cell firing rates on the two familiar trajectories start to change before the actual behavioral switch to the new trajectory. Second, repeated exposure on the new trajectory is associated with an increased dependence of place-cell firing rates on immediate past locations. The result suggests that sequence encoding in the hippocampus may involve integration of information about the recent past into current state.
Journal of Neuroscience 05/2008; 28(18):4679-89. · 7.11 Impact Factor
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ABSTRACT: Forming distinct representations of multiple contexts, places, and episodes is a crucial function of the hippocampus. The dentate gyrus subregion has been suggested to fulfill this role. We have tested this hypothesis by generating and analyzing a mouse strain that lacks the gene encoding the essential subunit of the N-methyl-d-aspartate (NMDA) receptor NR1, specifically in dentate gyrus granule cells. The mutant mice performed normally in contextual fear conditioning, but were impaired in the ability to distinguish two similar contexts. A significant reduction in the context-specific modulation of firing rate was observed in the CA3 pyramidal cells when the mutant mice were transferred from one context to another. These results provide evidence that NMDA receptors in the granule cells of the dentate gyrus play a crucial role in the process of pattern separation.
Science 08/2007; 317(5834):94-9. · 31.20 Impact Factor
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ABSTRACT: The stochastic state point process filter (SSPPF) and steepest descent point process filter (SDPPF) are adaptive filter algorithms for state estimation from point process observations that have been used to track neural receptive field plasticity and to decode the representations of biological signals in ensemble neural spiking activity. The SSPPF and SDPPF are constructed using, respectively, Gaussian and steepest descent approximations to the standard Bayes and Chapman-Kolmogorov (BCK) system of filter equations. To extend these approaches for constructing point process adaptive filters, we develop sequential Monte Carlo (SMC) approximations to the BCK equations in which the SSPPF and SDPPF serve as the proposal densities. We term the two new SMC point process filters SMC-PPFs and SMC-PPFD, respectively. We illustrate the new filter algorithms by decoding the wind stimulus magnitude from simulated neural spiking activity in the cricket cercal system. The SMC-PPFs and SMC-PPFD provide more accurate state estimates at low number of particles than a conventional bootstrap SMC filter algorithm in which the state transition probability density is the proposal density. We also use the SMC-PPFs algorithm to track the temporal evolution of a spatial receptive field of a rat hippocampal neuron recorded while the animal foraged in an open environment. Our results suggest an approach for constructing point process adaptive filters using SMC methods.
IEEE Transactions on Biomedical Engineering 04/2007; 54(3):419-28. · 2.28 Impact Factor
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ABSTRACT: 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.
Nature Neuroscience 02/2007; 10(1):100-7. · 15.53 Impact Factor
