Neural population coding: combining insights from microscopic and mass signals

Behavior relies on the distributed and coordinated activity of neural populations. Population activity can be measured using multi-neuron recordings and neuroimaging. Neural recordings reveal how the heterogeneity, sparseness, timing, and correlation of population activity shape information processing in local networks, whereas neuroimaging shows how long-range coupling and brain states impact on local activity and perception. To obtain an integrated perspective on neural information processing we need to combine knowledge from both levels of investigation. We review recent progress of how neural recordings, neuroimaging, and computational approaches begin to elucidate how interactions between local neural population activity and large-scale dynamics shape the structure and coding capacity of local information representations, make them state-dependent, and control distributed populations that collectively shape behavior

超高次元データ解析のための量子インスパイア主成分分析・正準相関分析の開発

主成分分析・正準相関分析はともに多変量データから重要な低次元成分を抽出する統計手法である．しかし，これらのアルゴリズムは特異値分解に基づくため，次元数（変量の数）が数百万を超えるデータには，計算時間の問題からしばしば適用が困難となる．我々は近年発案された「量子インスパイアアルゴリズム」を用い，計算時間を次元数の対数オーダーに抑えつつ，主成分分析・正準相関分析を近似するアルゴリズムを計算機実装した．本報告において，複数の人工データ・実データを用いてその計算時間と性能を評価した結果を紹介する．また，量子インスパイアアルゴリズムを用いた高速計算は単なる計算時間の削減にとどまらず，新しいデータ解析の方法を提供する．例として，与えられた多変量データ内の変量同士で積をとり，それを新しい変量とみなすことで次元数を増加させ，得られた高次元データに我々の開発した量子インスパイア正準相関分析を適用した．この操作はデータの次元数を増加させるため，通常の正準相関分析では計算時間の肥大化により取り扱いが困難となる．MNISTデータセットを用いこれを行ったところ，提案法は線形のみの正準相関分析より多くの相関を抽出した．また抽出できた相関の量はカーネル正準相関分析，深層正準相関分析などの代表的な非線形手法と同程度であった．以上の結果は，量子インスパイアアルゴリズムが実データの解析において有用であり，従来では計算時間の問題から不可能であった超高次元データを扱う分野を開拓できる可能性を示している．第2回量子ソフトウェア研究発表

Studying Category-Based Visual Attention and Mental Imagery in the Human Brain Using Local Field Potentials

Cerebral processing of visual stimuli is characterized by a complex circuitry involved in processing visual inputs while simultaneously contextualizing, categorizing or modulating these inputs by higher-order cortical centers. The sensory input and the cognitive control over this input can potentially be differentiated spatially, i.e. different brain regions, or spectrally, i.e. different frequencies of neuronal activity. In this work, we used object-category based visual tasks to investigate the spectral and spatial patterns of encoding of visual inputs and cognitive control at the level of visual attention and mental imagery using local field potentials in human subjects across widely distributed recording sites and a broad frequency spectrum(1-100Hz). Using PCA, we demonstrate that during a task involving both visual attention to an object category with varied observed category a broadband, and two narrowband low-frequency explained the main variance in the data. When comparing response to attended versus seen categories, we did not observe a spatial difference in location of encoding sites, but using decoding models, the broadband signal decodes vision better than attention in visual cortex and vision and attention equally in the temporal lobe. However, narrowband delta-theta decodes the best for both vision and attention, and alpha-beta differentially decodes for attention better than vision. Using an alternate task that involves image memorization, imagery, and passive viewing, we demonstrate that the main power spectral modulation among those mental states are represented by a broadband, gamma band, and low-frequency band. Encoding sites were similarly spatially widely distributed. Decoding models showed that gamma band predicts attentive viewing and mental imagery with best accuracy. Both broadband and low frequency band accurately decode for passive and attentive viewing. Our findings demonstrate that encoding of vision, attention and mental imagery is not dependent on a single spectral domain and optimal decoding of visual processes should consider the co-contribution of narrowband and broadband spectral patterns to account for the different co-occurring top-down and bottom-up processes. Therefore, although gamma-significantly studied in vision-is involved in visual working-memory tasks, both broadband and low-frequency narrowband patterns of neuronal activity co-participate in visual processing in the context of object-based visual attention and mental imagery