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Although the hippocampus is critical to episodic memory, neuronal representations supporting this role, especially relating to nonspatial information, remain elusive. Here, we investigated rate and temporal coding of hippocampal CA1 neurons in rats performing a cue-combination task that requires the integration of sequentially provided sound and odor cues. The majority of CA1 neurons displayed sensory cue-, combination-, or choice-specific (simply, "event"-specific) elevated discharge activities, which were sustained throughout the event period. These event cells underwent transient theta phase precession at event onset, followed by sustained phase locking to the early theta phases. As a result of this unique single neuron behavior, the theta sequences of CA1 cell assemblies of the event sequences had discrete representations. These results help to update the conceptual framework for space encoding toward a more general model of episodic event representations in the hippocampus., (Copyright © 2017 Elsevier Inc. All rights reserved.)

The basal ganglia play a pivotal role in reward-oriented behavior. The striatum, an input channel of the basal ganglia, is composed of subdivisions that are topographically connected with different cortical and subcortical areas. To test whether reward information is differentially processed in the different parts of the striatum, we compared reward-related neuronal activity along the dorsolateral-ventromedial axis in the caudate nucleus of monkeys performing an asymmetrically rewarded oculomotor task. In a given block, a target in one position was associated with a large reward, whereas the other target was associated with a small reward. The target position-reward value contingency was switched between blocks. We found the following: (1) activity that reflected the block-wise reward contingency emerged before the appearance of a visual target, and it was more prevalent in the dorsal, rather than central and ventral, caudate; (2) activity that was positively related to the reward size of the current trial was evident, especially after reward delivery, and it was more prevalent in the ventral and central, rather than dorsal, caudate; and (3) activity that was modulated by the memory of the outcomes of the previous trials was evident in the dorsal and central caudate. This multiple reward information, together with the target-direction information, was represented primarily by individual caudate neurons, and the different reward information was represented in caudate subpopulations with distinct electrophysiological properties, e.g., baseline firing and spike width. These results suggest parallel processing of different reward information by the basal ganglia subdivisions defined by extrinsic connections and intrinsic properties.

In the cortex, the interactions among neurons give rise to transient coherent activity patterns that underlie perception, cognition, and action. Recently, it was actively debated whether the most basic interactions, i.e., the pairwise correlations between neurons or groups of neurons, suffice to explain those observed activity patterns. So far, the evidence reported is controversial. Importantly, the overall organization of neuronal interactions and the mechanisms underlying their generation, especially those of high-order interactions, have remained elusive. Here we show that higher-order interactions are required to properly account for cortical dynamics such as ongoing neuronal avalanches in the alert monkey and evoked visual responses in the anesthetized cat. A Gaussian interaction model that utilizes the observed pairwise correlations and event rates and that applies intrinsic thresholding identifies those higher-order interactions correctly, both in cortical local field potentials and spiking activities. This allows for accurate prediction of large neuronal population activities as required, e.g., in brain-machine interface paradigms. Our results demonstrate that higher-order interactions are inherent properties of cortical dynamics and suggest a simple solution to overcome the apparent formidable complexity previously thought to be intrinsic to those interactions.

Midbrain dopamine neurons are thought to signal predictions about future rewards based on the memory of past rewarding experience. Little is known about the source of their reward memory and the factors that control its timescale. Here we recorded from dopamine neurons, as well as one of their sources of input, the lateral habenula, while animals predicted upcoming rewards based on the past reward history. We found that lateral habenula and dopamine neurons accessed two distinct reward memories: a short-timescale memory expressed at the start of the task and a near-optimal long-timescale memory expressed when a future reward outcome was revealed. The short- and long-timescale memories were expressed in different forms of reward-oriented eye movements. Our data show that the habenula-dopamine pathway contains multiple timescales of memory and provide evidence for their role in motivated behavior., ((c) 2010 Elsevier Inc. All rights reserved.)

Recent advances in the analysis of neuronal activities suggest that the instantaneous activity patterns can be mostly explained by considering only first-order and pairwise interactions between recorded elements, i.e., action potentials or local field potentials (LFP), and do not require higher-than-pairwise-order interactions. If generally applicable, this pairwise approach greatly simplifies the description of network interactions. However, an important question remains: are the recorded elements the units of interaction that best describe neuronal activity patterns? To explore this, we recorded spontaneous LFP peak activities in cortical organotypic cultures using planar, integrated 60-microelectrode arrays. We compared predictions obtained using a pairwise approach with those using a hierarchical approach that uses two different spatial units for describing the activity interactions: single electrodes and electrode clusters. In this hierarchical model, short-range interactions within each cluster were modeled by pairwise interactions of electrode activities and long-range interactions were modeled by pairwise interactions of cluster activities. Despite the relatively low number of parameters used, the hierarchical model provided a more accurate description of the activity patterns than the pairwise model when applied to ensembles of 10 electrodes. Furthermore, the hierarchical model was successfully applied to a larger-scale data of approximately 60 electrodes. Electrode activities within clusters were highly correlated and spatially contiguous. In contrast, long-range interactions were diffuse, suggesting the presence of higher-than-pairwise-order interactions involved in the LFP peak activities. Thus, the identification of appropriate units of interaction may allow for the successful characterization of neuronal activities in large-scale networks.

The decoding scheme of a stimulus can be different from the stochastic encoding scheme in the neural population coding. The stochastic fluctuations are not independent in general, but an independent version could be used for the ease of decoding. How much information is lost by using this unfaithful model for decoding? There are discussions concerning loss of information (Nirenberg & Latham, 2003; Schneidman, Bialek, & Berry, 2003). We elucidate the Nirenberg-Latham loss from the point of view of information geometry.

In examining spike trains, different models are used to describe their structure. The different models often seem quite similar, but because they are cast in different formalisms, it is often difficult to compare their predictions. Here we use the information-geometric measure, an orthogonal coordinate representation of point processes, to express different models of stochastic point processes in a common coordinate system. Within such a framework, it becomes straightforward to visualize higher-order correlations of different models and thereby assess the differences between models. We apply the information-geometric measure to compare two similar but not identical models of neuronal spike trains: the inhomogeneous Markov and the mixture of Poisson models. It is shown that they differ in the second- and higher-order interaction terms. In the mixture of Poisson model, the second- and higher-order interactions are of comparable magnitude within each order, whereas in the inhomogeneous Markov model, they have alternating signs over different orders. This provides guidance about what measurements would effectively separate the two models. As newer models are proposed, they also can be compared to these models using information geometry.

The present study investigates the performance of population codes when the fluctuations in neural activity have mutual correlation with strength being proportional to the neuronal firing rate (multiplicative noise). The neural field is used to calculate the Fisher information, which is decomposed in two parts, one due to the tuning function and spatial correlation, and the other due to the multiplicative structure. Their different characteristics are studied. The paper also investigates three types of maximum likelihood method, namely, decoding by using faithful and unfaithful models and the Center of Mass strategy, and compares their performances in terms of decoding accuracy and computational complexity.

Midbrain dopamine (DA) neurons are thought to encode reward prediction error. Reward prediction can be improved if any relevant context is taken into account. We found that monkey DA neurons can encode a context-dependent prediction error. In the first noncontextual task, a light stimulus was randomly followed by reward, with a fixed equal probability. The response of DA neurons was positively correlated with the number of preceding unrewarded trials and could be simulated by a conventional temporal difference (TD) model. In the second contextual task, a reward-indicating light stimulus was presented with the probability that, while fixed overall, was incremented as a function of the number of preceding unrewarded trials. The DA neuronal response then was negatively correlated with this number. This history effect corresponded to the prediction error based on the conditional probability of reward and could be simulated only by implementing the relevant context into the TD model.

Changes in the reward context are associated with changes in neuronal activity in the basal ganglia as well as changes in motor outputs. A typical example is found in the caudate (CD) projection neurons and saccade parameters. It raised the possibility that the changes in CD neuronal activity contribute to the changes in saccade parameters. To examine this possibility, we calculated the correlation coefficients (CORs) of the firing rates of each neuron with saccade parameters (peak saccade velocity and latency) on a trial-by-trial basis. We then calculated the mean CORs separately for two CD populations: reward-enhanced type neurons (RENs) that showed enhanced activity and reward-depressed type neurons (RDNs) that showed depressed activity when reward was expected. The activity of RENs was positively correlated with the saccadic peak velocity and negatively correlated with the saccade latency. The activity of RDNs was not significantly correlated with the saccade parameters. We further analyzed the CORs for RENs, a major type of CD neurons. First, we examined the time courses of the CORs using a moving time window (duration: 200 ms). The positive correlation with the saccade velocity and the negative correlation with the saccade latency were present not only in the peri-saccadic period but also during the pre- and postcue periods. Second, we asked whether the CORs with the saccade parameters were direction-selective. A majority of RENs were more active before contralateral saccades (contralateral-preferring neurons) and their activity was correlated more strongly with contralateral saccades than with ipsilateral saccades. A minority of RENs, ipsilateral-preferring neurons, showed no such preference. These results are consistent with the hypothesis that CD neuronal activity exerts facilitatory effects on contralateral saccades and that the effects start well before saccade execution. Furthermore, a multiple regression analysis indicated that changes in activity of some, but not all, CD neurons could be explained by changes in saccade parameters; a major determinant was reward context (presence or absence of reward). These results suggest that, while a majority of CD neurons receive reward-related signals, only some of them can make a significant contribution to change saccadic outputs based on expected reward.

The stochastic mechanism of synchronous firing in a population of neurons is studied from the point of view of information geometry. Higher-order interactions of neurons, which cannot be reduced to pairwise correlations, are proved to exist in synchronous firing. In a neuron pool where each neuron fires stochastically, the probability distribution q(r) of the activity r, which is the fraction of firing neurons in the pool, is studied. When q(r) has a widespread distribution, in particular, when q(r) has two peaks, the neurons fire synchronously at one time and are quiescent at other times. The mechanism of generating such a probability distribution is interesting because the activity r is concentrated on its mean value when each neuron fires independently, because of the law of large numbers. Even when pairwise interactions, or third-order interactions, exist, the concentration is not resolved. This shows that higher-order interactions are necessary to generate widespread activity distributions. We analyze a simple model in which neurons receive common overlapping inputs and prove that such a model can have a widespread distribution of activity, generating higher-order stochastic interactions.

This study introduces information-geometric measures to analyze neural firing patterns by taking not only the second-order but also higher-order interactions among neurons into account. Information geometry provides useful tools and concepts for this purpose, including the orthogonality of coordinate parameters and the Pythagoras relation in the Kullback-Leibler divergence. Based on this orthogonality, we show a novel method for analyzing spike firing patterns by decomposing the interactions of neurons of various orders. As a result, purely pairwise, triple-wise, and higher-order interactions are singled out. We also demonstrate the benefits of our proposal by using several examples.

This study investigates the influence of attention modulation on neural tuning functions. It has been shown in experiments that attention modulation alters neural tuning curves. Attention has been considered at least to serve to resolve limiting capacities and to increase the sensitivity to attended stimulus, while the exact functions of attention are still under debate. Inspired by recent experimental results on attention modulation, we investigate the influence of changes in the height and base rate of the tuning curve on the encoding accuracy, using the Fisher information. Under an assumption of stimulus-conditional independence of neural responses, we derive explicit conditions that determine when the height and base rate should be increased or decreased to improve encoding accuracy. Notably, a decrease in the tuning height and base rate can improve the encoding accuracy in some cases. Our theoretical results can predict the effective size of attention modulation on the neural population with respect to encoding accuracy. We discuss how our method can be used quantitatively to evaluate different aspects of attention function.

This study investigates a population decoding paradigm in which the maximum likelihood inference is based on an unfaithful decoding model (UMLI). This is usually the case for neural population decoding because the encoding process of the brain is not exactly known or because a simplified decoding model is preferred for saving computational cost. We consider an unfaithful decoding model that neglects the pair-wise correlation between neuronal activities and prove that UMLI is asymptotically efficient when the neuronal correlation is uniform or of limited range. The performance of UMLI is compared with that of the maximum likelihood inference based on the faithful model and that of the center-of-mass decoding method. It turns out that UMLI has advantages of decreasing the computational complexity remarkably and maintaining high-level decoding accuracy. Moreover, it can be implemented by a biologically feasible recurrent network (Pouget, Zhang, Deneve, & Latham, 1998). The effect of correlation on the decoding accuracy is also discussed.