Michael C. Quirk

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.

Experience-Dependent Asymmetric Shape of Hippocampal Receptive Fields

We propose a novel parameter, namely, the skewness, or asymmetry, of the shape of a receptive field to characterize two properties of hippocampal place fields. First, a majority of hippocampal receptive fields on linear tracks are negatively skewed, such that during a single pass the firing rate is low as the rat enters the field but high as it exits. Second, while the place fields are symmetric at the beginning of a session, they become highly asymmetric with experience. Further experiments suggest that these results are likely to arise due to synaptic plasticity during behavior. Using a purely feed forward neural network model, we show that following repeated directional activation, NMDA-dependent long-term potentiation/long-term depotentiation (LTP/LTD) could result in an experience-dependent asymmetrization of receptive fields.

Construction and analysis of non-Poisson stimulus-response models of neural spiking activity

A paradigm for constructing and analyzing non-Poisson stimulus-response models of neural spike train activity is presented. Inhomogeneous gamma (IG) and inverse Gaussian (IIG) probability models are constructed by generalizing the derivation of the inhomogeneous Poisson (IP) model from the exponential probability density. The resultant spike train models have Markov dependence. Quantile-quantile (Q-Q) plots and Kolmogorov-Smirnov (K-S) plots are developed based on the rate-rescaling theorem to assess model goodness-of-fit. The analysis also expresses the spike rate function of the neuron directly in terms of its interspike interval (ISI) distribution. The methods are illustrated with an analysis of 34 spike trains from rat CA1 hippocampal pyramidal neurons recorded while the animal executed a behavioral task. The stimulus in these experiments is the animals position in its environment and the response is the neural spiking activity. For all 34 pyramidal cells, the IG and IIG models gave better fits to the spike trains than the IP. The IG model more accurately described the frequency of longer ISIs, whereas the IIG model gave the best description of the burst frequency, i.e. ISIs < or = 20 ms. The findings suggest that bursts are a significant component of place cell spiking activity even when position and the background variable, theta phase, are taken into account. Unlike the Poisson model, the spatial and temporal rate maps of the IG and IIG models depend directly on the spiking history of the neurons. These rate maps are more physiologically plausible since the interaction between space and time determines local spiking propensity. While this statistical paradigm is being developed to study information encoding by rat hippocampal neurons, the framework should be applicable to stimulus-response experiments performed in other neural systems.

A selective allosteric potentiator of the M1 muscarinic acetylcholine receptor increases activity of medial prefrontal cortical neurons and restores impairments in reversal learning

M1 muscarinic acetylcholine receptors (mAChRs) may represent a viable target for treatment of disorders involving impaired cognitive function. However, a major limitation to testing this hypothesis has been a lack of highly selective ligands for individual mAChR subtypes. We now report the rigorous molecular characterization of a novel compound, benzylquinolone carboxylic acid (BQCA), which acts as a potent, highly selective positive allosteric modulator (PAM) of the rat M1 receptor. This compound does not directly activate the receptor, but acts at an allosteric site to increase functional responses to orthosteric agonists. Radioligand binding studies revealed that BQCA increases M1 receptor affinity for acetylcholine. We found that activation of the M1 receptor by BQCA induces a robust inward current and increases spontaneous EPSCs in medial prefrontal cortex (mPFC) pyramidal cells, effects which are absent in acute slices from M1 receptor knock-out mice. Furthermore, to determine the effect of BQCA on intact and functioning brain circuits, multiple single-unit recordings were obtained from the mPFC of rats that showed BQCA increases firing of mPFC pyramidal cells in vivo. BQCA also restored discrimination reversal learning in a transgenic mouse model of Alzheimers disease and was found to regulate non-amyloidogenic APP processing in vitro, suggesting that M1 receptor PAMs have the potential to provide both symptomatic and disease modifying effects in Alzheimers disease patients. Together, these studies provide compelling evidence that M1 receptor activation induces a dramatic excitation of PFC neurons and suggest that selectively activating the M1 mAChR subtype may ameliorate impairments in cognitive function.

Dynamic Analyses of Information Encoding in Neural Ensembles

Neural spike train decoding algorithms and techniques to compute Shan-non 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 inter-faces. Developing optimal strategies to desig n decoding algorithms and compute mutual information are therefore important problems in com-putational 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 en-tropy, 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 probabil-ity 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 per-formance 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 pre-vious results and suggest an integrated approach to dynamically reading neural codes, measuring their properties, and quantifying the accuracy with which encoded information is extracted.

Stimulus-Specific Adaptation in Auditory Cortex Is an NMDA-Independent Process Distinct from the Sensory Novelty Encoded by the Mismatch Negativity

The significance of the mismatch negativity (MMN), an event-related potential measured in humans which indexes novelty in the auditory environment, has motivated a search for a cellular correlate of this process. A leading candidate is stimulus-specific adaptation (SSA) in auditory cortex units, which shares several characteristics with the MMN. Whether auditory cortex responses encode sensory novelty, a defining property of the MMN, however, has not been resolved. To evaluate this key issue, we used several variations of the auditory oddball paradigm from the human literature and examined psychophysical and pharmacological properties of multiunit activity in the auditory cortex of awake rodents. We found converging evidence dissociating SSA from sensory novelty and the MMN. First, during an oddball paradigm with frequency deviants, neuronal responses showed clear SSA but failed to encode novelty in a manner analogous to the human MMN. Second, oddball paradigms using intensity or duration deviants revealed a pattern of unit responses that showed sensory adaptation, but again without any measurable novelty correlates aligning to the human MMN. Finally NMDA antagonists, which are known to disrupt the MMN, suppressed the magnitude of multiunit responses in a nonspecific manner, leaving the process of SSA intact. Together, our results suggest that auditory novelty detection as indexed by the MMN is dissociable from SSA at the level of activity encoded by auditory cortex neurons. Further, the NMDA sensitivity reported for the MMN, which models the disruption of MMN observed in schizophrenia, may occur at a mechanistic locus outside of SSA.

Interaction between spike waveform classification and temporal sequence detection.

In vivo extracellular recordings have allowed researchers to study the response properties of neurons to behaviorally relevant stimuli. In this paper we use multiple tetrode recordings from the hippocampus of the freely behaving rat to show that the action potential amplitude of a given cell can vary in a systematic and activity dependent manner over behaviorally relevant time scales. Since the discrimination algorithms used by experimenters to isolate cells from extracellular recordings are based on differences in waveforms, we show how these systematic changes in waveform shape can lead to non-random errors in single cell isolation. We further demonstrate that these non-random errors can lead to apparent temporal ordering effects between neurons in the absence of any specific temporal relationship. A firm understanding of these naturally occurring physiological changes is therefore critical for the evaluation of higher order phenomena such as the temporally correlated firing of ensembles of neurons.

A Bayesian decoding algorithm for analysis of information encoding in neural ensembles

Developing optimal strategies for constructing and testing decoding algorithms is an important question in computational neuroscience, In this field, decoding algorithms are mathematical methods that model ensemble neural spiking activity as they dynamically represent a biological signal. We present a recursive decoding algorithm based on a Bayesian point process model of individual neuron spiking activity and a linear stochastic state-space model of the biological signal. 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 analyzing the position and ensemble neural spiking activity of CA1 hippocampal neurons from a rat foraging in an open circular environment The median decoding error during 10 minutes of open foraging was 5.5 cm, and the true coverage probability for 0.95 confidence regions was 0.75 using 32 neurons. These findings improve significantly on our previous results and suggest an approach to reading dynamically information represented in ensemble neural spiking activity.

Construction and analysis of non-Gaussian spatial models of neural spiking activity

Abstract The spiking activity of rat CA1 hippocampal place cells during open field foraging can be described with stimulus-response models based on an inhomogeneous Poisson (IP) interspike interval probability model. The spatial structure of this model has been previously represented as a simple Gaussian surface. We analyze four new spatial models, two extensions of the Gaussian surface, a surface based on a logistic transformation, and a surface constructed by using Zernike polynomials. Goodness-of-fit analysis based on the Bayesian information criterion (BIC) shows that the Zernike polynomial surfaces give the most accurate description of the spatial place cell spiking activity under this experimental paradigm.

A time-dependent analysis of spatial information encoding in the rat hippocampus

Abstract The place fields of rat hippocampal pyramidal cells evolve over time as the animal moves through its environment. To study the nature of this evolution we use an inhomogeneous gamma probability model and analyze the properties of place cell spike trains in non-overlapping windows. Although the locations and heights of the fields evolve over time for 27 of the 35 place cells studied, the temporal parameter was essentially unchanged. This result suggests that adaptive estimation methods may be useful to characterize the dynamics of hippocampal place cells for behavioral tasks executed in novel and non-novel environments.