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Sensory perception and memory recall generate different conscious experiences. Although externally and internally driven neural activities signifying the same perceptual content overlap in the sensory cortex, their distribution in the prefrontal cortex (PFC), an area implicated in both perception and memory, remains elusive. Here, we test whether the local spatial configurations and frequencies of neural oscillations driven by perception and memory recall overlap in the macaque PFC using high-density electrocorticography and multivariate pattern analysis. We find that dynamically changing oscillatory signals distributed across the PFC in the delta-, theta-, alpha-, and beta-band ranges carry significant, but mutually different, information predicting the same feature of memory-recalled internal targets and passively perceived external objects. These findings suggest that the frequency-specific distribution of oscillatory neural signals in the PFC serves cortical signatures responsible for distinguishing between different types of cognition driven by external perception and internal memory.

The application of near-term quantum devices to machine learning (ML) has attracted much attention. Recently, Mitarai et al. [Phys. Rev. A 98, 032309 (2018)] proposed a framework to use a quantum circuit for ML tasks, called quantum circuit learning (QCL). Due to the use of a quantum circuit, QCL employs an exponentially high-dimensional Hilbert space as its feature space. However, its efficiency compared to classical algorithms remains unexplored. Here, we present a classical ML algorithm that uses the same Hilbert space. In numerical simulations, our algorithm demonstrates similar performance to QCL for several ML tasks, providing a perspective for the computational and memory efficiency of quantum ML algorithms.

Machine learning algorithms specialized for neural data have allowed the extraction of information encoded in the brain. As an example, in previous studies, the images human subjects see were reconstructed from their brain activity measured via functional magnetic resonance imaging (fMRI) [1]. However, the application of those machine learning algorithms to high-resolution fMRI data, which may become mainstream in the near future, is limited due to their high computational cost. To solve this problem, scalable machine learning algorithms are being designed by utilizing computational techniques developed in the field of quantum computation [2,3]. In this report, taking one of the popular statistical methods, principal component analysis (PCA), as an example, we show that machine algorithms can be approximated with the use of such quantum-inspired techniques. The computational time and approximation performance of quantum-inspired PCA are demonstrated. The main results of this report have been presented in a previous paper by the author [3]. References 1G. Shen, T. Horikawa, K. Majima, Y. Kamitani, PLOS Computational Biology, Volume 15, e1006633 (2019). 2E. Tang, Physical Review Letters, Volume 127, 060503 (2021). 3N. Koide-Majima, K. Majima, Neural Networks, Volume 135, 55–67 (2021)., QUANTUM INNOVATION 2021

Canonical correlation analysis (CCA) serves to identify statistical dependencies between pairs of multivariate data. However, its application to high-dimensional data is limited due to considerable computational complexity. As an alternative to the conventional CCA approach that requires polynomial computational time, we propose an algorithm that approximates CCA using quantum-inspired computations with computational time proportional to the logarithm of the input dimensionality. The computational efficiency and performance of the proposed quantum-inspired CCA (qiCCA) algorithm are experimentally evaluated on synthetic and real datasets. Furthermore, the fast computation provided by qiCCA allows directly applying CCA even after nonlinearly mapping raw input data into high-dimensional spaces. The conducted experiments demonstrate that, as a result of mapping raw input data into the high-dimensional spaces with the use of second-order monomials, qiCCA extracts more correlations compared with the linear CCA and achieves comparable performance with state-of-the-art nonlinear variants of CCA on several datasets. These results confirm the appropriateness of the proposed qiCCA and the high potential of quantum-inspired computations in analyzing high-dimensional data.