Fundamental Information Processing Research Articles

A QR Code for the Brain: A dynamical systems framework for computing neurophysiological biomarkers.

Neural circuits are often considered the bridge connecting genetic causes and behavior. Whereas prenatal neural circuits are believed to be derived from a combination of genetic and intrinsic activity, postnatal circuits are largely influenced by exogenous activity and experience. A dynamical neuroelectric field maintained by neural activity is proposed as the fundamental information processing substrate of cognitive function. Time series measurements of the neuroelectric field can be collected by scalp sensors and used to mathematically quantify the essential dynamical features of the neuroelectric field by constructing a digital twin of the dynamical system phase space. The multiscale nonlinear values that result can be organized into tensor data structures, from which latent features can be extracted using tensor factorization. These latent features can be mapped to behavioral constructs to derive digital biomarkers. This computational framework provides a robust method for incorporating neurodynamical measures into neuropsychiatric biomarker discovery.

Low-dimensional criticality embedded in high-dimensional awake brain dynamics.

Whether cortical neurons operate in a strongly or weakly correlated dynamical regime determines fundamental information processing capabilities and has fueled decades of debate. We offer a resolution of this debate; we show that two important dynamical regimes, typically considered incompatible, can coexist in the same local cortical circuit by separating them into two different subspaces. In awake mouse motor cortex, we find a low-dimensional subspace with large fluctuations consistent with criticality-a dynamical regime with moderate correlations and multi-scale information capacity and transmission. Orthogonal to this critical subspace, we find a high-dimensional subspace containing a desynchronized dynamical regime, which may optimize input discrimination. The critical subspace is apparent only at long timescales, which explains discrepancies among some previous studies. Using a computational model, we show that the emergence of a low-dimensional critical subspace at large timescales agrees with established theory of critical dynamics. Our results suggest that the cortex leverages its high dimensionality to multiplex dynamical regimes across different subspaces.

Enhancement and contextual modulation of visuospatial processing by thalamocollicular projections from ventral lateral geniculate nucleus

In the mammalian visual system, the ventral lateral geniculate nucleus (vLGN) of the thalamus receives salient visual input from the retina and sends prominent GABAergic axons to the superior colliculus (SC). However, whether and how vLGN contributes to fundamental visual information processing remains largely unclear. Here, we report in mice that vLGN facilitates visually-guided approaching behavior mediated by the lateral SC and enhances the sensitivity of visual object detection. This can be attributed to the extremely broad spatial integration of vLGN neurons, as reflected in their much lower preferred spatial frequencies and broader spatial receptive fields than SC neurons. Through GABAergic thalamocollicular projections, vLGN specifically exerts prominent surround suppression of visuospatial processing in SC, leading to a fine tuning of SC preferences to higher spatial frequencies and smaller objects in a context-dependent manner. Thus, as an essential component of the central visual processing pathway, vLGN serves to refine and contextually modulate visuospatial processing in SC-mediated visuomotor behaviors via visually-driven long-range feedforward inhibition.

55 nm CMOS Mixed-Signal Neuromorphic Circuits for Constructing Energy-Efficient Reconfigurable SNNs

The development of brain-inspired spiking neural networks (SNNs) has great potential for neuromorphic edge computing applications, while challenges remain in optimizing power-efficiency and silicon utilization. Neurons, synapses and spike-based learning algorithms form the fundamental information processing mechanism of SNNs. In an effort to achieve compact and biologically plausible SNNs while restricting power consumption, we propose a set of new neuromorphic building circuits, including an analog Leaky Integrate-and-Fire (LIF) neuron circuit, configurable synapse circuits and Spike Driven Synaptic Plasticity (SDSP) learning algorithm circuits. Specifically, we explore methods to minimize large leakage current and device mismatch effects, and optimize the design of these neuromorphic circuits to enable low-power operation. A reconfigurable mixed-signal SNN is proposed based on the building circuits, allowing flexible configuration of synapse weights and attributes, resulting in enhanced SNN functionality and reduced unnecessary power consumption. This SNN chip is fabricated using 55 nm CMOS technology, and test results indicate that the proposed circuits have the ability to closely mimic the behaviors of LIF neurons, synapses and SDSP mechanisms. By configuring synaptic arrays, we established varied connections between neurons in the SNN and demonstrated that this SNN chip can implement Pavlov’s dog associative learning and binary classification tasks, while dissipating less energy per spike of the order of Pico Joules per spike at a firing rate ranging from 30 Hz to 1 kHz. The proposed circuits can be used as building blocks for constructing large-scale SNNs in neuromorphic processors.

Effect of topographic comparison of electroencephalographic microstates on the diagnosis and prognosis prediction of patients with prolonged disorders of consciousness.

The electroencephalography (EEG) microstates are indicative of fundamental information processing mechanisms, which are severely damaged in patients with prolonged disorders of consciousness (pDoC). We aimed to improve the topographic analysis of EEG microstates and explore indicators available for diagnosis and prognosis prediction of patients with pDoC, which were still lacking. We conducted EEG recordings on 59 patients with pDoC and 32 healthy controls. We refined the microstate method to accurately estimate topographical differences, and then classify and forecast the prognosis of patients with pDoC. An independent dataset was used to validate the conclusion. Through optimized topographic analysis, the global explained variance (GEV) of microstate E increased significantly in groups with reduced levels of consciousness. However, its ability to classify the VS/UWS group was poor. In addition, the optimized GEV of microstate E exhibited a statistically significant decrease in the good prognosis group as opposed to the group with a poor prognosis. Furthermore, the optimized GEV of microstate E strongly predicted a patient's prognosis. This technique harmonizes with the existing microstate analysis and exhibits precise and comprehensive differences in microstate topography between groups. Furthermore, this method has significant potential for evaluating the clinical prognosis of pDoC patients.

Enhancement of quadripartite quantum correlation via phase-sensitive cascaded four-wave mixing process

Multipartite quantum correlation is important for both fundamental science and quantum information processing. Its enhancement is critical to improve the performance of physical systems. Therefore, it is of great practical significance to study the enhancement of multipartite quantum correlation. Here, we theoretically investigate a scheme for enhancing quadripartite quantum correlation by utilizing a phase-sensitive cascaded four-wave mixing (CFWM) process. We find that the intensity-difference squeezing (IDS) among the four output beams generated by the phase-insensitive CFWM process can be largely enhanced by introducing the phase-sensitive CFWM process. We also find that our phase-sensitive CFWM process can be used to generate intensity-sum squeezing (ISS), which cannot be generated by the phase-insensitive CFWM process. Moreover, we find that the maximum squeezing value of ISS is equal to that of IDS when the intensity gains of the three four-wave mixing processes are equal in the phase-sensitive cascaded scheme. In the end, we discuss the effects of the losses and the phase fluctuations on the squeezing values of our phase-sensitive CFWM process and the corresponding phase-insensitive CFWM process with the same intensity gains. It can be seen that the squeezing enhancement of our phase-sensitive CFWM process holds also in presence of the losses and the phase fluctuations. Our results pave the way for experimental implementation and may find applications in quantum metrology and quantum communication.

Azimuthal controlling of electromagnetically induced phase grating in five-level quantum systems

In this paper, we suggest a theoretical model for creating a two-dimensional (2D) electromagnetically induced phase grating in a five-level quantum system interacting with a weak probe light, two coupling standing light in the x and y directions and a Laguerre–Gaussian (LG) field. By derivation of the Maxwell’s wave equation, we obtain the dynamic response of the probe light in the quantum system. Then, we perform the analytical solution of the probe susceptibility for obtaining the linear and nonlinear properties of the medium. By numerical calculations, we discuss the amplitude and phase modulations and Fraunhofer diffraction patterns of the probe light in different parametric conditions. We show that by adjusting the orbital angular momentum (OAM) of the LG light a 2D grating is observed. Moreover, we find that the probe energy can be transferred from zero order to high order of direction when we change the OAM number of LG light. Our proposed model may have potential applications in fundamental research and quantum information processing based on OAM light in quantum systems.

A perspective on quantum entanglement in optomechanical systems

Quantum entanglement in optomechanical systems plays an important role in the progress of quantum science and technology, such as the exploration of fundamental physics and quantum information processing. Although the physical law of quantum mechanics does not specifically limit the size of objects that carry the entangled states, the experimental preparation and detection of quantum entanglement in the macro world still face great challenges. Fortunately, with the experimental advances in recent years, several pioneering works have demonstrated non-local correlation and entanglement among mechanical oscillators or between oscillators and electromagnetic fields. In this perspective, we summarize the theoretical and experimental progress related to the macro entanglement states in optomechanical systems and outlook its future direction and potential applications.

Nonreciprocity and Quantum Correlations of Light Transport in Hot Atoms via Reservoir Engineering.

The breaking of reciprocity is a topic of great interest in fundamental physics and optical information processing applications. We demonstrate nonreciprocal light transport in a quantum system of hot atoms by engineering the dissipative atomic reservoir. Our scheme is based on the phase-sensitive light transport in a multichannel photon-atom interaction configuration, where the phase of collective atomic excitations is tunable through external driving fields. Remarkably, we observe interchannel quantum correlations that originate from interactions with the judiciously engineered reservoir. The nonreciprocal transport in a quantum optical atomic system constitutes a new paradigm for atom-based nonreciprocal optics and offers opportunities for quantum simulations with coupled optical channels.

Simultaneous cooling of double oscillators in an optomechanical system with an optical parametric amplifier

Quantum manipulation of mechanical oscillators has important applications in fundamental physics and quantum information processing. Ground-state cooling of the mechanical oscillators is the prerequisite for these applications. In this paper, we propose a scheme for cooling double mechanical oscillators simultaneously, in which the parametric processes induced by a degenerate optical parameter amplifier (OPA) change the statistical properties of the cavity field, resulting in the lower average phonon numbers. However, it is worth noting that two mechanical modes with the same frequency cannot be cooled due to destructive interference between the two cooling processes. While two mechanical oscillators with different frequencies can be simultaneously cooled to near their ground-state, and the cooling efficiency can be improved by increasing the parametric gain of OPA.

A dual role for Cav1.4 Ca2+ channels in the molecular and structural organization of the rod photoreceptor synapse.

Synapses are fundamental information processing units that rely on voltage-gated Ca2+ (Cav) channels to trigger Ca2+-dependent neurotransmitter release. Cav channels also play Ca2+-independent roles in other biological contexts, but whether they do so in axon terminals is unknown. Here, we addressed this unknown with respect to the requirement for Cav1.4 L-type channels for the formation of rod photoreceptor synapses in the retina. Using a mouse strain expressing a non-conducting mutant form of Cav1.4, we report that the Cav1.4 protein, but not its Ca2+ conductance, is required for the molecular assembly of rod synapses; however, Cav1.4 Ca2+ signals are needed for the appropriate recruitment of postsynaptic partners. Our results support a model in which presynaptic Cav channels serve both as organizers of synaptic building blocks and as sources of Ca2+ ions in building the first synapse of the visual pathway and perhaps more broadly in the nervous system.

Quantum entanglement between an atom and a molecule.

Conventional information processors convert information between different physical carriers for processing, storage and transmission. It seems plausible that quantum information will also be held by different physical carriers in applications such as tests of fundamental physics, quantum enhanced sensors and quantum information processing. Quantum controlled molecules, in particular, could transduce quantum information across a wide range of quantum bit (qubit) frequencies-from a few kilohertz for transitions within the same rotational manifold1, a few gigahertz for hyperfine transitions, a few terahertz for rotational transitions, to hundreds of terahertz for fundamental and overtone vibrational and electronic transitions-possibly all within the same molecule. Here we demonstrate entanglement between the rotational states of a 40CaH+ molecular ion and the internal states of a 40Ca+ atomic ion2. We extend methods used in quantum logic spectroscopy1,3 for pure-state initialization, laser manipulation and state readout of the molecular ion. The quantum coherence of the Coulomb coupled motion between the atomic and molecular ions enables subsequent entangling manipulations. The qubit addressed in the molecule has a frequency of either 13.4 kilohertz1 or 855 gigahertz3, highlighting the versatility of molecular qubits. Our work demonstrates how molecules can transduce quantum information between qubits with different frequencies to enable hybrid quantum systems. We anticipate that our method of quantum control and measurement of molecules will find applications in quantum information science, quantum sensors, fundamental and applied physics, and controlled quantum chemistry.

Ground-state cooling of a magnomechanical resonator induced by magnetic damping

Quantum manipulation of mechanical resonators has been widely applied in fundamental physics and quantum information processing. Among them, cooling a mechanical system to its quantum ground state is regarded as a key step. In this work, we propose a scheme that can realize ground-state cooling of the resonator in a cavity magnomechanical system. The system consists of a microwave cavity and a small ferromagnetic sphere, in which phonon–magnon coupling and cavity photon–magnon coupling can be achieved via magnetostrictive interaction and magnetic dipole interaction, respectively. Within experimentally feasible parameters, we demonstrate that the extra magnetic damping can be utilized to achieve ground-state cooling of the magnomechanical resonator via an effective dark-mode interaction. The magnomechanical cooling mainly comes from the magnon–phonon interaction terms. We further illustrate that optimal cooling can be obtained by adjusting the external magnetic field.

Stochastic Resonance with Unital Quantum Noise

The fundamental quantum information processing task of estimating the phase of a qubit is considered. Following quantum measurement, the estimation efficiency is evaluated by the classical Fisher information which determines the best performance limiting any estimator and achievable by the maximum likelihood estimator. The estimation process is analyzed in the presence of decoherence represented by essential quantum noises that can affect the qubit and belonging to the broad class of unital quantum noises. Such a class especially contains the bit-flip, the phase-flip, the depolarizing noises, or the whole family of Pauli noises. As the level of noise is increased, we report the possibility of non-standard behaviors where the estimation efficiency does not necessarily deteriorate uniformly, but can experience non-monotonic variations. Regimes are found where higher noise levels prove more favorable to estimation. Such behaviors are related to stochastic resonance effects in signal estimation, shown here feasible for the first time with unital quantum noises. The results provide enhanced appreciation of quantum noise or decoherence, manifesting that it is not always detrimental for quantum information processing.

Information spectra and optimal background states for dynamical networks

We consider the notion of stimulus representation over dynamic networks, wherein the network states encode information about the identify of an afferent input (i.e. stimulus). Our goal is to understand how the structure and temporal dynamics of networks support information processing. In particular, we conduct a theoretical study to reveal how the background or ‘default’ state of a network with linear dynamics allows it to best promote discrimination over a continuum of stimuli. Our principal contribution is the derivation of a matrix whose spectrum (eigenvalues) quantify the extent to which the state of a network encodes its inputs. This measure, based on the notion of a Fisher linear discriminant, is relativistic in the sense that it provides an information value quantifying the ‘knowablility’ of an input based on its projection onto the background state. We subsequently optimize the background state and highlight its relationship to underlying state noise covariance. This result demonstrates how the best idle state of a network may be informed by its structure and dynamics. Further, we relate the proposed information spectrum to the controllabilty gramian matrix, establishing a link between fundamental control-theoretic network analysis and information processing.

Category Structure and Categorical Perception Jointly Explained by Similarity-Based Information Theory.

Categorization is a fundamental information processing phenomenon in the brain. It is critical for animals to compress an abundance of stimulations into groups to react quickly and efficiently. In addition to labels, categories possess an internal structure: the goodness measures how well any element belongs to a category. Interestingly, this categorization leads to an altered perception referred to as categorical perception: for a given physical distance, items within a category are perceived closer than items in two different categories. A subtler effect is the perceptual magnet: discriminability is reduced close to the prototypes of a category and increased near its boundaries. Here, starting from predefined abstract categories, we naturally derive the internal structure of categories and the phenomenon of categorical perception, using an information theoretical framework that involves both probabilities and pairwise similarities between items. Essentially, we suggest that pairwise similarities between items are to be tuned to render some predefined categories as well as possible. However, constraints on these pairwise similarities only produce an approximate matching, which explains concurrently the notion of goodness and the warping of perception. Overall, we demonstrate that similarity-based information theory may offer a global and unified principled understanding of categorization and categorical perception simultaneously.

Local word vectors guiding keyphrase extraction

Automated keyphrase extraction is a fundamental textual information processing task concerned with the selection of representative phrases from a document that summarize its content. This work presents a novel unsupervised method for keyphrase extraction, whose main innovation is the use of local word embeddings (in particular GloVe vectors), i.e., embeddings trained from the single document under consideration. We argue that such local representation of words and keyphrases are able to accurately capture their semantics in the context of the document they are part of, and therefore can help in improving keyphrase extraction quality. Empirical results offer evidence that indeed local representations lead to better keyphrase extraction results compared to both embeddings trained on very large third corpora or larger corpora consisting of several documents of the same scientific field and to other state-of-the-art unsupervised keyphrase extraction methods.

It from Qutrit: Braided Loop Metaheuristic

Cosmologies are developed by physicists and philosophers to explain our experiences of the evolving cosmos. Intelligent deep-learning metaheuristics provide original frameworks for cosmologies which are founded on quantum information. Mathematical standard models of physical cosmology and particle physics formalize an abundance of observations, yet there is no scientific consensus about how these models include our conscious experiences and fundamental philosophies of information. Furthermore, Naturalness in physics is coupled to the related problem of fine-tuning. To address these foundational problems, within the quantum information paradigm, whilst aligning with standard scientific models, I introduce a topological deep-learning cosmology metaheuristic. Braided, 3-coloured, world-strands are proposed to be the fundamental quantum information tracts (ethereal fibre bundles) of our evolving Triuniverse. This Braided Loop Metaheuristic comprises eternally evolving deep-learning feedback loops of superposed, braided, 3-coloured, quantum information world-strands, which process (in 3-level qutrit states) foundational properties coined Algebrus (labelled red), Algorithmus (labelled green) and Geometrus (labelled blue). Braids split from 1→2→3 (in knot representation respectively: closed loop→trefoil knot→Borromean loops) thence combine from 3→2→1 to form eternally evolving deep-learning loops. This cosmology metaheuristic simultaneously incorporates initial Laws of Form; Emergentism (from substrate Mathematics, through Quantum Physics to Life); Consciousness (as a superposed triunity of Implicate Order, Process Philosophy and Aesthetic Relationalism); Reductionism (from Life, through Quantum Physics to Pure Mathematics expressed as Logical Axioms, Laws of Parsimony and Ideal Form); and the Braided Loop Metaheuristic reboots its eternal cycle with the initial Laws of Form. An agent’s personal anthropic Braided Loop Metaheuristic represents one of many-worlds, a meridional loop in a multiverse with horn-torus topology, where Nature’s physical parameters vary equatorially. Fundamental information processing is driven by ψ-Epistemic Drive, the Natural appetite for information selected for advantageous knowledge. The meridional loops are ψ-Epistemic Field lines emanating from an epistemic dipole at the horn-torus core. Equatorial parameter fine-tuning in many-worlds quantum physics and the many-species of Darwinian Life are similar deep-learning optimizations in the Braided Loop Metaheuristic.

Stochastic antiresonance in qubit phase estimation with quantum thermal noise

We consider the fundamental quantum information processing task consisting in estimating the phase of a qubit. Following quantum measurement, the estimation performance is evaluated by the classical Fisher information which determines the best performance limiting any estimator and achievable by the maximum likelihood estimator. Estimation is analyzed in the presence of decoherence represented by a quantum thermal noise at arbitrary temperature. As the noise temperature is increased, we show the possibility of nontrivial behaviors of decoherence, with an estimation performance which does not necessarily degrade uniformly, but can experience nonmonotonic evolutions. Regimes are found where higher noise temperatures turn more favorable to estimation. Such behaviors are related to stochastic resonance or antiresonance effects, where noise reveals beneficial to information processing.

SU32. Impact of Catechol-O-Methyl Transferase Polymorrphism on Cognition and Information Processing in Schizophrenia Using Novel MBIAL Computerized Battery—A Case-Control Study

Abstract Background: Frontal executive dysfunction have been robustly demonstrated in schizophrenia. Several fundamental information processing deficits were reported in schizophrenia underlying the various symptom dimensions. Results of previous studies of neuropsychological functioning in schizophrenia were inconsistent. Association between COMT allele type and cognition was not confirmed in all studies. This study attempted to assess the neuropsychological performance on a hypothesis-driven MBIAL-computerized neuropsychological battery in a sample of schizophrenia subjects and matched healthy comparison subjects and its relationship with the gene polymorphism. Methods: Total 34 patients and 30 matched healthy controls were recruited after obtaining informed consent. An HMSE was applied to exclude those with scores <23. The matrix reasoning module of WAIS-III was applied to obtain an estimate of their perceptual organization index as a reflection of their intellectual abilities, independent of verbal abilities. A novel MBIAL Computerized neuropsychological battery consisting of 6 tests was applied on all the subjects. Later, both the groups were assessed for symptom severity using PANSS. All Neuropsychological tests and PANSS Interviews using SCI-PANSS were Video and Audio recorded. Genotyping for COMTval158met polymorphism was accomplished by PCR-RFLP method. Genotype frequencies across groups were noted using chi-square test and later were compared using ANCOVA as we had WAIS III and HMSE as covariates. Results: Schizophrenia patients performed significantly poorly on most of the neuropsychological tests as compared with healthy matched controls. Negative symptom severity was associated with significant impairment in neuropsychological deficits as compared to positive symptoms. Separate analysis of association of gene polymorphism on these cognitive tests done on patients and controls did not reveal much results. But on pooled data, we found Val-Carriers performed poorly on most of the Neuropsychological and Information processing tasks and have high threat perception compared to Non-Val Carriers. Conclusion: Hypothesis-driven neuropsychological battery was used in schizophrenia that incorporated tests for frontal executive functioning; rostral prefrontal functioning; threat perception (aberration of which is postulated to underlie delusion formation); and cross-modal sensory integration (aberration of which is postulated to underlie development of hallucinations). The MBIAL battery is sensitive in detecting differences between patients and controls in the adult population. We suggest here that Val158Met polymorphism rather exerts a modifying influence on brain activation in general than impacts directly on performance of the particular neuropsychological task.