Ryo Yokota

Learning-stage-dependent, field-specific, map plasticity in the rat auditory cortex during appetitive operant conditioning

Cortical reorganizations during acquisition of motor skills and experience-dependent recovery after deafferentation consist of several distinct phases, in which expansion of receptive fields is followed by the shrinkage and use-dependent refinement. In perceptual learning, however, such non-monotonic, stage-dependent plasticity remains elusive in the sensory cortex. In the present study, microelectrode mapping characterized plasticity in the rat auditory cortex, including primary, anterior, and ventral/suprarhinal auditory fields (A1, AAF, and VAF/SRAF), at the early and late stages of appetitive operant conditioning. We first demonstrate that most plasticity at the early stage was tentative, and that long-lasting plasticity after extended training was able to be categorized into either early- or late-stage-dominant plasticity. Second, training-induced plasticity occurred both locally and globally with a specific temporal order. Conditioned-stimulus (CS) frequency used in the task tended to be locally over-represented in AAF at the early stage and in VAF/SRAF at the late stage. The behavioral relevance of neural responses suggests that the local plasticity also occurred in A1 at the early stage. In parallel, the tone-responsive area globally shrank at the late stage independently of CS frequency, and this shrinkage was also correlated with the behavioral improvements. Thus, the stage-dependent plasticity may commonly underlie cortical reorganization in the perceptual learning, yet the interactions of local and global plasticity have led to more complicated reorganization than previously thought. Field-specific plasticity has important implications for how each field subserves in the learning; for example, consistent with recent notions, A1 should construct filters to better identify auditory objects at the early stage, while VAF/SRAF contribute to hierarchical computation and storage at the late stage.

Detecting Causality by Combined Use of Multiple Methods: Climate and Brain Examples

Identifying causal relations from time series is the first step to understanding the behavior of complex systems. Although many methods have been proposed, few papers have applied multiple methods together to detect causal relations based on time series generated from coupled nonlinear systems with some unobserved parts. Here we propose the combined use of three methods and a majority vote to infer causality under such circumstances. Two of these methods are proposed here for the first time, and all of the three methods can be applied even if the underlying dynamics is nonlinear and there are hidden common causes. We test our methods with coupled logistic maps, coupled Rössler models, and coupled Lorenz models. In addition, we show from ice core data how the causal relations among the temperature, the CH4 level, and the CO2 level in the atmosphere changed in the last 800,000 years, a conclusion also supported by irregularly sampled data analysis. Moreover, these methods show how three regions of the brain interact with each other during the visually cued, two-choice arm reaching task. Especially, we demonstrate that this is due to bottom up influences at the beginning of the task, while there exist mutual influences between the posterior medial prefrontal cortex and the presupplementary motor area. Based on our results, we conclude that identifying causality with an appropriate ensemble of multiple methods ensures the validity of the obtained results more firmly.

Response variance in functional maps: neural darwinism revisited.

The mechanisms by which functional maps and map plasticity contribute to cortical computation remain controversial. Recent studies have revisited the theory of neural Darwinism to interpret the learning-induced map plasticity and neuronal heterogeneity observed in the cortex. Here, we hypothesize that the Darwinian principle provides a substrate to explain the relationship between neuron heterogeneity and cortical functional maps. We demonstrate in the rat auditory cortex that the degree of response variance is closely correlated with the size of its representational area. Further, we show that the response variance within a given population is altered through training. These results suggest that larger representational areas may help to accommodate heterogeneous populations of neurons. Thus, functional maps and map plasticity are likely to play essential roles in Darwinian computation, serving as effective, but not absolutely necessary, structures to generate diverse response properties within a neural population.

Tonotopic-column-dependent variability of neural encoding in the auditory cortex of rats

Neural computation could benefit from the heterogeneity of neurons to achieve energy efficiency. Beyond a single neuron level, adaptation to biologically important signals should also make functional columns heterogeneous. In the present study, we test a hypothesis that variability of neural response depends on tonotopic columns in the primary auditory cortex (A1) of rats. Mutual information (MI) was estimated from multi-unit responses in A1 of anesthetized rats, to quantify how spike count (SC) and the first spike latency (FSL) carried information about frequency and intensity of test tones. Consequently, for both SC and FSL, we found best frequency (BF)-dependent MI distributions with wide variances in high BF regions. These MI distributions were caused by BF-dependence of the amount of information that neurons conveyed, i.e., total entropy, rather than the transmission efficiency. In addition, the relationship between the transmission efficiency and the total entropy differentiated SC encoding and FSL encoding, suggesting that SC encoding and FSL encoding are not redundant but each plays a different role in intensity encoding. These results provide compelling evidence that BF columns are heterogeneous. Such heterogeneity of columns may make the global computation in A1 more efficient. Thus, the efficient coding in the neural system could be achieved by multiple-scale heterogeneity.

Learning-Stage-Dependent Plasticity of Temporal Coherence in the Auditory Cortex of Rats

Temporal coherence among neural populations may contribute importantly to signal encoding, specifically by providing an optimal tradeoff between encoding reliability and efficiency. Here, we considered the possibility that learning modulates the temporal coherence among neural populations in association with well-characterized map plasticity. We previously demonstrated that, in appetitive operant conditioning tasks, the tone-responsive area globally expanded during the early stage of learning, but shrank during the late stage. The present study further showed that phase locking of the first spike to band-specific oscillations of local field potentials (LFPs) significantly increased during the early stage of learning but decreased during the late stage, suggesting that neurons in A1 were more synchronously activated during early learning, whereas they were more asynchronously activated once learning was completed. Furthermore, LFP amplitudes increased during early learning but decreased during later learning. These results suggest that, compared to naïve encoding, early-stage encoding is more reliable but energy-consumptive, whereas late-stage encoding is more energetically efficient. Such a learning-stage-dependent encoding strategy may underlie learning-induced, non-monotonic map plasticity. Accumulating evidence indicates that the cholinergic system is likely to be a shared neural substrate of the processes for perceptual learning and attention, both of which modulate neural encoding in an adaptive manner. Thus, a better understanding of the links between map plasticity and modulation of temporal coherence will likely lead to a more integrated view of learning and attention.

Quantification of Inter-Sample Differences in T-Cell Receptor Repertoires Using Sequence-Based Information

Inter-sample comparisons of T-cell receptor (TCR) repertoires are crucial for gaining a better understanding of the immunological states determined by different collections of T cells from different donor sites, cell types, and genetic and pathological backgrounds. For quantitative comparison, most previous studies utilized conventional methods in ecology, which focus on TCR sequences that overlap between pairwise samples. Some recent studies attempted another approach that is categorized into Poisson abundance models using the abundance distribution of observed TCR sequences. However, these methods ignore the details of the measured sequences and are consequently unable to identify sub-repertoires that might have important contributions to the observed inter-sample differences. Moreover, the sparsity of sequence data due to the huge diversity of repertoires hampers the performance of these methods, especially when few overlapping sequences exist. In this paper, we propose a new approach for REpertoire COmparison in Low Dimensions (RECOLD) based on TCR sequence information, which can estimate the low-dimensional structure by embedding the pairwise sequence dissimilarities in high-dimensional sequence space. The inter-sample differences between repertoires are then quantified by information-theoretic measures among the distributions of data estimated in the embedded space. Using datasets of mouse and human TCR repertoires, we demonstrate that RECOLD can accurately identify the inter-sample hierarchical structures, which have a good correspondence with our intuitive understanding about sample conditions. Moreover, for the dataset of transgenic mice that have strong restrictions on the diversity of their repertoires, our estimated inter-sample structure was consistent with the structure estimated by previous methods based on abundance or overlapping sequence information. For the dataset of human healthy donors and Sézary syndrome patients, our method also showed robust estimation performance even under the condition of high sparsity in TCR sequences, while previous studies failed to estimate the structure. In addition, we identified the sequences that contribute to the pairwise-sample differences between the repertoires with the different genetic backgrounds of mice. Such identification of the sequences contributing to variation in immune cell repertoires may provide substantial insight for the development of new immunotherapies and vaccines.

Quantification of inter-sample differences in T cell receptor sequences

Inter-sample comparisons of the T cell receptor (TCR) repertoire are crucial for gaining a better understanding into the immunological states determined by different collections of T cells from different donor sites, cell types, and genetic and pathological backgrounds. As a theoretical approach for the quantitative comparison, previous studies utilized the Poisson abundance models and the conventional methods in ecology, which focus on the abundance distribution of observed TCR sequences. However, these methods ignore the details of the measured sequences and are consequently unable to identify sub-repertoires that might have the contributions to the observed inter-sample differences. In this paper, we propose a new comparative approach based on TCR sequence information, which can estimate the low-dimensional structure by projecting the pairwise sequence dissimilarities in high-dimensional sequence space. The inter-sample differences are then quantified according to information-theoretic measures among the distributions of data estimated in the embedded space. Using an actual dataset of TCR sequences in transgenic mice that have strong restrictions on somatic recombination, we demonstrate that our proposed method can accurately identify the inter-sample hierarchical structure, which is consistent with that estimated by previous methods based on abundance or count information. Moreover, we identified the key sequences that contribute to the pairwise sample differences. Such identification of the sequences contributing to variation in immune cell repertoires may provide substantial insight for the development of new immunotherapies and vaccines.

Feedback Regulation and Its Efficiency in Biochemical Networks

Intracellular biochemical networks fluctuate dynamically due to various internal and external sources of fluctuation. Dissecting the fluctuation into biologically relevant components is important for understanding how a cell controls and harnesses noise and how information is transferred over apparently noisy intracellular networks. While substantial theoretical and experimental advancement on the decomposition of fluctuation was achieved for feedforward networks without any loop, we still lack a theoretical basis that can consistently extend such advancement to feedback networks. The main obstacle that hampers is the circulative propagation of fluctuation by feedback loops. In order to define the relevant quantity for the impact of feedback loops for fluctuation, disentanglement of the causally interlocked influences between the components is required. In addition, we also lack an approach that enables us to infer non-perturbatively the influence of the feedback to fluctuation in the same way as the dual reporter system does in the feedforward networks. In this work, we address these problems by extending the work on the fluctuation decomposition and the dual reporter system. For a single-loop feedback network with two components, we define feedback loop gain as the feedback efficiency that is consistent with the fluctuation decomposition for feedforward networks. Then, we clarify the relation of the feedback efficiency with the fluctuation propagation in an open-looped FF network. Finally, by extending the dual reporter system, we propose a conjugate feedback and feedforward system for estimating the feedback efficiency non-perturbatively only from the statistics of the system.

Experimental and theoretical bases for mechanisms of antigen discrimination by T cells

Interaction only within specific molecules is a requisite for accurate operations of a biochemical reaction in a cell where bulk of background molecules exist. While structural specificity is a well-established mechanism for specific interaction, biophysical and biochemical experiments indicate that the mechanism is not sufficient for accounting for the antigen discrimination by T cells. In addition, the antigen discrimination by T cells also accompanies three intriguing properties other than the specificity: sensitivity, speed, and concentration compensation. In this work, we review experimental and theoretical works on the antigen discrimination by focusing on these four properties and show future directions towards understanding of the fundamental principle for molecular discrimination.

Analysis of Spatio-temporal Cortical Activity with Artificial Neural Network

The artificial neural network (ANN) can translate spatio-temporal neural activities into the corresponding test stimuli. ANN with a simple structure and generalization ability has a potential to reflect a prominent feature of the computation mechanism in the brain. In the present work, we propose a novel analysis using ANN. In the constructed ANN, neural activities in the primary auditory cortex (A1) served as the inputs, and time-series changes of test frequencies of tones served as the targets. We then investigated input-output relationships of hidden layer neurons. Consequently, we found that some hidden layer neurons tuned the frequency preference by excitatory inputs from all frequency regions, while others tuned with inhibitory inputs from a low frequency region. These results suggest that neural activities in A1 form the frequency preference with excitatory inputs from all frequency pathways and inhibitory inputs from a low frequency pathway. This suggestion is consistent with physiological facts, thus proving the feasibility of the proposed analysis.