Masami Tatsuno

Fast-Forward Playback of Recent Memory Sequences in Prefrontal Cortex During Sleep

As previously shown in the hippocampus and other brain areas, patterns of firing-rate correlations between neurons in the rat medial prefrontal cortex during a repetitive sequence task were preserved during subsequent sleep, suggesting that waking patterns are reactivated. We found that, during sleep, reactivation of spatiotemporal patterns was coherent across the network and compressed in time by a factor of 6 to 7. Thus, when behavioral constraints are removed, the brains intrinsic processing speed may be much faster than it is in real time. Given recent evidence implicating the medial prefrontal cortex in retrieval of long-term memories, the observed replay may play a role in the process of memory consolidation.

Stored-trace reactivation in rat prefrontal cortex is correlated with down-to-up state fluctuation density

Spontaneous reactivation of previously stored patterns of neural activity occurs in hippocampus and neocortex during non-rapid eye movement (NREM) sleep. Notable features of the neocortical local field potential during NREM sleep are high-amplitude, low-frequency thalamocortical oscillations including K-complexes, low-voltage spindles, and high-voltage spindles. Using combined neuronal ensemble and local field potential recordings, we show that prefrontal stored-trace reactivation is correlated with the density of down-to-up state transitions of the population of simultaneously recorded cells, as well as K-complexes and low-voltage spindles in the local field potential. This result strengthens the connection between reactivation and learning, as these same NREM sleep features have been correlated with memory. Although memory trace reactivation is correlated with low-voltage spindles, it is not correlated with high-voltage spindles, indicating that despite their similar frequency characteristics, these two oscillations serve different functions.

Methodological Considerations on the Use of Template Matching to Study Long-Lasting Memory Trace Replay

Replay of behaviorally induced neural activity patterns during subsequent sleep has been suggested to play an important role in memory consolidation. Many previous studies, mostly involving familiar experiences, suggest that such reactivation occurs, but decays quickly (∼1 h). Recently, however, long-lasting (up to ∼48 h) “reverberation” of neural activity patterns induced by a novel experience was reported on the basis of a template-matching analysis. Because detection and quantification of memory-trace replay depends critically on analysis methods, we investigated the statistical properties of the template-matching method and analyzed rodent neural ensemble activity patterns after a novel experience. For comparison, we also analyzed the same data with an independent analysis technique, the explained variance method. Contrary to the recent report, we did not observe significant long-lasting reverberation using either the template matching or the explained variance approaches. The latter, however, did reveal short-lasting reactivation in the hippocampus and prefrontal cortex. In addition, detailed analysis of the template-matching method shows that, in the present study, coarse mean firing rate differences among neurons, but not fine temporal spike structures, dominate the results of template matching. Most importantly, it is also demonstrated that partial comparisons of template-matching correlations, such as used in the recent paper, may lead to erroneous conclusions. These investigations indicate that the outcome of template-matching analysis is very sensitive to the conditions of how it is applied, and should be interpreted cautiously, and that the existence of long-lasting reverberation after a novel experience requires additional verification.

Interaction of egocentric and world-centered reference frames in the rat posterior parietal cortex.

Navigation requires coordination of egocentric and allocentric spatial reference frames and may involve vectorial computations relative to landmarks. Creation of a representation of target heading relative to landmarks could be accomplished from neurons that encode the conjunction of egocentric landmark bearings with allocentric head direction. Landmark vector representations could then be created by combining these cells with distance encoding cells. Landmark vector cells have been identified in rodent hippocampus. Given remembered vectors at goal locations, it would be possible to use such cells to compute trajectories to hidden goals. To look for the first stage in this process, we assessed parietal cortical neural activity as a function of egocentric cue light location and allocentric head direction in rats running a random sequence to light locations around a circular platform. We identified cells that exhibit the predicted egocentric-by-allocentric conjunctive characteristics and anticipate orienting toward the goal.

Long-Term Recordings Improve the Detection of Weak Excitatory–Excitatory Connections in Rat Prefrontal Cortex

Characterization of synaptic connectivity is essential to understanding neural circuit dynamics. For extracellularly recorded spike trains, indirect evidence for connectivity can be inferred from short-latency peaks in the correlogram between two neurons. Despite their predominance in cortex, however, significant interactions between excitatory neurons (E) have been hard to detect because of their intrinsic weakness. By taking advantage of long duration recordings, up to 25 h, from rat prefrontal cortex, we found that 7.6% of the recorded pyramidal neurons are connected. This corresponds to ∼70% of the local E–E connection probability that has been reported by paired intracellular recordings (11.6%). This value is significantly higher than previous reports from extracellular recordings, but still a substantial underestimate. Our analysis showed that long recording times and strict significance thresholds are necessary to detect weak connections while avoiding false-positive results, but will likely still leave many excitatory connections undetected. In addition, we found that hyper-reciprocity of connections in prefrontal cortex that was shown previously by paired intracellular recordings was only present in short-distance, but not in long distance (∼300 micrometers or more) interactions. As hyper-reciprocity is restricted to local clusters, it might be a minicolumnar effect. Given the current surge of interest in very high-density neural spike recording (e.g., NIH BRAIN Project) it is of paramount importance that we have statistically reliable methods for estimating connectivity from cross-correlation analysis available. We provide an important step in this direction.

Information-geometric measures as robust estimators of connection strengths and external inputs

Information geometry has been suggested to provide a powerful tool for analyzing multineuronal spike trains. Among several advantages of this approach, a significant property is the close link between information-geometric measures and neural network architectures. Previous modeling studies established that the first- and second-order information-geometric measures corresponded to the number of external inputs and the connection strengths of the network, respectively. This relationship was, however, limited to a symmetrically connected network, and the number of neurons used in the parameter estimation of the log-linear model needed to be known. Recently, simulation studies of biophysical model neurons have suggested that information geometry can estimate the relative change of connection strengths and external inputs even with asymmetric connections. Inspired by these studies, we analytically investigated the link between the information-geometric measures and the neural network structure with asymmetrically connected networks of N neurons. We focused on the information-geometric measures of orders one and two, which can be derived from the two-neuron log-linear model, because unlike higher-order measures, they can be easily estimated experimentally. Considering the equilibrium state of a network of binary model neurons that obey stochastic dynamics, we analytically showed that the corrected first- and second-order information-geometric measures provided robust and consistent approximation of the external inputs and connection strengths, respectively. These results suggest that information-geometric measures provide useful insights into the neural network architecture and that they will contribute to the study of system-level neuroscience.

Investigation of Possible Neural Architectures Underlying Information-Geometric Measures

A novel analytical method based on information geometry was recently proposed, and this method may provide useful insights into the statistical interactions within neural groups. The link between information-geometric measures and the structure of neural interactions has not yet been elucidated, however, because of the ill-posed nature of the problem. Here, possible neural architectures underlying information-geometric measures are investigated using an isolated pair and an isolated triplet of model neurons. By assuming the existence of equilibrium states, we derive analytically the relationship between the information-geometric parameters and these simple neural architectures. For symmetric networks, the first- and second-order information-geometric parameters represent, respectively, the external input and the underlying connections between the neurons provided that the number of neurons used in the parameter estimation in the log-linear model and the number of neurons in the network are the same. For asymmetric networks, however, these parameters are dependent on both the intrinsic connections and the external inputs to each neuron. In addition, we derive the relation between the information-geometric parameter corresponding to the two-neuron interaction and a conventional cross-correlation measure. We also show that the information-geometric parameters vary depending on the number of neurons assumed for parameter estimation in the log-linear model. This finding suggests a need to examine the information-geometric method carefully. A possible criterion for choosing an appropriate orthogonal coordinate is also discussed. This article points out the importance of a model-based approach and sheds light on the possible neural structure underlying the application of information geometry to neural network analysis.

Rule-dynamical approach to hippocampal network

Abstract To approach a complex system such as brain, we proposed a new constructive strategy based on rule dynamics. Firstly, the similarity between brain and rule-dynamical cellular automata (CA) was pointed out, and we showed that the rule-dynamical CA could be represented by 2-layered neural networks. Based on these findings, hippocampal network from a rule-dynamical point of view was constructed, and it was shown that the temporal pattern in each region was dependent on the input pathway, that is, the multi-synaptic input produced a spatio-temporal pattern, while the direct input produced a periodic pattern.

Information-geometric measures for estimation of connection weight under correlated inputs

The brain processes information in a highly parallel manner. Determination of the relationship between neural spikes and synaptic connections plays a key role in the analysis of electrophysiological data. Information geometry (IG) has been proposed as a powerful analysis tool for multiple spike data, providing useful insights into the statistical interactions within a population of neurons. Previous work has demonstrated that IG measures can be used to infer the connection weight between two neurons in a neural network. This property is useful in neuroscience because it provides a way to estimate learning-induced changes in synaptic strengths from extracellular neuronal recordings. A previous study has shown, however, that this property would hold only when inputs to neurons are not correlated. Since neurons in the brain often receive common inputs, this would hinder the application of the IG method to real data. We investigated the two-neuron-IG measures in higher-order log-linear models to overcome this limitation. First, we mathematically showed that the estimation of uniformly connected synaptic weight can be improved by taking into account higher-order log-linear models. Second, we numerically showed that the estimation can be improved for more general asymmetrically connected networks. Considering the estimated number of the synaptic connections in the brain, we showed that the two-neuron IG measure calculated by the fourth- or fifth-order log-linear model would provide an accurate estimation of connection strength within approximately a 10% error. These studies suggest that the two-neuron IG measure with higher-order log-linear expansion is a robust estimator of connection weight even under correlated inputs, providing a useful analytical tool for real multineuronal spike data.

Information-geometric measures estimate neural interactions during oscillatory brain states

The characterization of functional network structures among multiple neurons is essential to understanding neural information processing. Information geometry (IG), a theory developed for investigating a space of probability distributions has recently been applied to spike-train analysis and has provided robust estimations of neural interactions. Although neural firing in the equilibrium state is often assumed in these studies, in reality, neural activity is non-stationary. The brain exhibits various oscillations depending on cognitive demands or when an animal is asleep. Therefore, the investigation of the IG measures during oscillatory network states is important for testing how the IG method can be applied to real neural data. Using model networks of binary neurons or more realistic spiking neurons, we studied how the single- and pairwise-IG measures were influenced by oscillatory neural activity. Two general oscillatory mechanisms, externally driven oscillations and internally induced oscillations, were considered. In both mechanisms, we found that the single-IG measure was linearly related to the magnitude of the external input, and that the pairwise-IG measure was linearly related to the sum of connection strengths between two neurons. We also observed that the pairwise-IG measure was not dependent on the oscillation frequency. These results are consistent with the previous findings that were obtained under the equilibrium conditions. Therefore, we demonstrate that the IG method provides useful insights into neural interactions under the oscillatory condition that can often be observed in the real brain.