Quantum-like model of processing of information in the brain based on classical electromagnetic field

We propose a model of quantum-like (QL) processing of mental information. This model is based on quantum information theory. However, in contrast to models of "quantum physical brain" reducing mental activity (at least at the highest level) to quantum physical phenomena in the brain, our model matches well with the basic neuronal paradigm of the cognitive science. QL information processing is based (surprisingly) on classical electromagnetic signals induced by joint activity of neurons. This novel approach to quantum information is based on representation of quantum mechanics as a version of classical signal theory which was recently elaborated by the author. The brain uses the QL representation (QLR) for working with abstract concepts; concrete images are described by classical information theory. Two processes, classical and QL, are performed parallely. Moreover, information is actively transmitted from one representation to another. A QL concept given in our model by a density operator can generate a variety of concrete images given by temporal realizations of the corresponding (Gaussian) random signal. This signal has the covariance operator coinciding with the density operator encoding the abstract concept under consideration. The presence of various temporal scales in the brain plays the crucial role in creation of QLR in the brain. Moreover, in our model electromagnetic noise produced by neurons is a source of superstrong QL correlations between processes in different spatial domains in the brain; the binding problem is solved on the QL level, but with the aid of the classical background fluctuations.     

Spatial inference without a cognitive map: the role of higher-order path integration

The cognitive map has been taken as the standard model for how agents infer the most efficient route to a goal location. Alternatively, path integration – maintaining a homing vector during navigation – constitutes a primitive and presumably less-flexible strategy than cognitive mapping because path integration relies primarily on vestibular stimuli and pace counting. The historical debate as to whether complex spatial navigation is ruled by associative learning or cognitive map mechanisms has been challenged by experimental difficulties in successfully neutralizing path integration. To our knowledge, there are only three studies that have succeeded in resolving this issue, all showing clear evidence of novel route taking, a behaviour outside the scope of traditional associative learning accounts. Nevertheless, there is no mechanistic explanation as to how animals perform novel route taking. We propose here a new model of spatial learning that combines path integration with higher-order associative learning, and demonstrate how it can account for novel route taking without a cognitive map, thus resolving this long-standing debate. We show how our higher-order path integration (HOPI) model can explain spatial inferences, such as novel detours and shortcuts. Our analysis suggests that a phylogenetically ancient, vector-based navigational strategy utilizing associative processes is powerful enough to support complex spatial inferences.     

Conduction pathways in microtubules, biological quantum computation, and consciousness.

Technological computation is entering the quantum realm, focusing attention on biomolecular information processing systems such as proteins, as presaged by the work of Michael Conrad. Protein conformational dynamics and pharmacological evidence suggest that protein conformational states-fundamental information units ('bits') in biological systems-are governed by quantum events, and are thus perhaps akin to quantum bits ('qubits') as utilized in quantum computation. 'Real time' dynamic activities within cells are regulated by the cell cytoskeleton, particularly microtubules (MTs) which are cylindrical lattice polymers of the protein tubulin. Recent evidence shows signaling, communication and conductivity in MTs, and theoretical models have predicted both classical and quantum information processing in MTs. In this paper we show conduction pathways for electron mobility and possible quantum tunneling and superconductivity among aromatic amino acids in tubulins. The pathways within tubulin match helical patterns in the microtubule lattice structure, which lend themselves to topological quantum effects resistant to decoherence. The Penrose-Hameroff 'Orch OR' model of consciousness is reviewed as an example of the possible utility of quantum computation in MTs.     

Multiscale representations of community structures in attractor neural networks

Our cognition relies on the ability of the brain to segment hierarchically structured events on multiple scales. Recent evidence suggests that the brain performs this event segmentation based on the structure of state-transition graphs behind sequential experiences. However, the underlying circuit mechanisms are poorly understood. In this paper we propose an extended attractor network model for graph-based hierarchical computation which we call the Laplacian associative memory. This model generates multiscale representations for communities (clusters) of associative links between memory items, and the scale is regulated by the heterogenous modulation of inhibitory circuits. We analytically and numerically show that these representations correspond to graph Laplacian eigenvectors, a popular method for graph segmentation and dimensionality reduction. Finally, we demonstrate that our model exhibits chunked sequential activity patterns resembling hippocampal theta sequences. Our model connects graph theory and attractor dynamics to provide a biologically plausible mechanism for abstraction in the brain.     

Human Parvovirus B19 Induced Apoptotic Bodies Contain Altered Self-Antigens that are Phagocytosed by Antigen Presenting Cells

Human parvovirus B19 (B19V) from the erythrovirus genus is known to be a pathogenic virus in humans. Prevalence of B19V infection has been reported worldwide in all seasons, with a high incidence in the spring. B19V is responsible for erythema infectiosum (fifth disease) commonly seen in children. Its other clinical presentations include arthralgia, arthritis, transient aplastic crisis, chronic anemia, congenital anemia, and hydrops fetalis. In addition, B19V infection has been reported to trigger autoimmune diseases such as systemic lupus erythematosus and rheumatoid arthritis. However, the mechanisms of B19V participation in autoimmunity are not fully understood. B19V induced chronic disease and persistent infection suggests B19V can serve as a model for viral host interactions and the role of viruses in the pathogenesis of autoimmune diseases. Here we investigate the involvement of B19V in the breakdown of immune tolerance. Previously, we demonstrated that the non-structural protein 1 (NS 1) of B19V induces apoptosis in non-permissive cells lines and that this protein can cleave host DNA as well as form NS1-DNA adducts. Here we provide evidence that through programmed cell death, apoptotic bodies (ApoBods) are generated by B19V NS1 expression in a non-permissive cell line. Characterization of purified ApoBods identified potential self-antigens within them. In particular, signature self-antigens such as Smith, ApoH, DNA, histone H4 and phosphatidylserine associated with autoimmunity were present in these ApoBods. In addition, when purified ApoBods were introduced to differentiated macrophages, recognition, engulfment and uptake occurred. This suggests that B19V can produce a source of self-antigens for immune cell processing. The results support our hypothesis that B19V NS1-DNA adducts, and nucleosomal and lysosomal antigens present in ApoBods created in non-permissive cell lines, are a source of self-antigens.     

Abstract and associatively based representations in human sequence learning

We give an analysis of performance in an artificial neural network for which the claim had been made that it could learn abstract representations. Our argument is that this network is associative in nature, and cannot develop abstract representations. The network thus converges to a solution that is solely based on the statistical regularities of the training set. Inspired by human experiments that have shown that humans can engage in both associative (statistical) and abstract learning, we present a new, hybrid computational model that combines associative and more abstract, cognitive processes. To cross-validate the model we attempted to predict human behaviour in further experiments. One of these experiments reveals some evidence for the use of abstract representations, whereas the others provide evidence for associatively based performance. The predictions of the hybrid model stand in line with our empirical data.     

条件联想神经网络研究

在人脑的某些功能和神经系统中的突前抑制机制启发下,本文提出一个新型的神经网络模型——条件联想神经网络.模型是一个有突触前抑制的联想记忆神经网络.通过初步分析和计算机模拟,证明本模型具有一般联想记忆模型所未有的一些新的特性,如可以在不同条件下,对同一输入有不同的反应.对同一输入,在不同的条件下,又可以有相同的反应.这些特点将有助于人们对神经系统中信息处理过程的了解.此外,文中也指出可能实现本模型的神经结构.     

Low‐Temperature‐Processed Colloidal Quantum Dots as Building Blocks for Thermoelectrics

Colloidal quantum dots (CQDs) are demonstrated to be promising materials to realize high‐performance thermoelectrics owing to their low thermal conductivity. The most studied CQD films, however, are using long ligands that require high processing and operation temperature (>400 °C) to achieve optimum thermoelectric performance. Here the thermoelectric properties of CQD films cross‐linked using short ligands that allow strong inter‐QD coupling are reported. Using the ligands, p‐type thermoelectric solids are demonstrated with a high Seebeck coefficient and power factor of 400 μV K^?1 and 30 µW m^?1 K^?2, respectively, leading to maximum ZT of 0.02 at a lower measurement temperature (<400 K) and lower processing temperature (<300 °C). These ligands further reduce the annealing temperature to 175 °C, significantly increasing the Seebeck coefficient of the CQD films to 580 μV K^?1. This high Seebeck coefficient with a superior ZT near room temperature compared to previously reported high temperature‐annealed CQD films is ascribed to the smaller grain size, which enables the retainment of quantum confinement and significantly increases the hole effective mass in the films. This study provides a pathway to approach quantum confinement for achieving a high Seebeck coefficient yet strong inter‐QD coupling, which offers a step toward low‐temperature‐processed high‐performance thermoelectric generators.     

Timing-based LTP and LTD at vertical inputs to layer II/III pyramidal cells in rat barrel cortex

Experience-dependent plasticity in somatosensory (S1) and visual (V1) cortex involves rapid depression of responses to a deprived sensory input (a closed eye or a trimmed whisker). Such depression occurs first in layer II/III and may reflect plasticity at vertical inputs from layer IV to layer II/III pyramids. Here, I describe a timing-based, associative form of long-term potentiation and depression (LTP/LTD) at this synapse in S1. LTP occurred when excitatory postsynaptic potentials (EPSPs) led single postsynaptic action potentials (APs) within a narrow temporal window, and LTD occurred when APs led EPSPs within a significantly broader window. This long LTD window is unusual among timing-based learning rules and causes EPSPs that are uncorrelated with postsynaptic APs to become depressed. This behavior suggests a simple model for depression of deprived sensory responses in S1 and V1.     

Modeling complex tone perception: grouping harmonics with combination-sensitive neurons

 Perception of complex communication sounds is a major function of the auditory system. To create a coherent percept of these sounds the auditory system may instantaneously group or bind multiple harmonics within complex sounds. This perception strategy simplifies further processing of complex sounds and facilitates their meaningful integration with other sensory inputs. Based on experimental data and a realistic model, we propose that associative learning of combinations of harmonic frequencies and nonlinear facilitation of responses to those combinations, also referred to as “combination-sensitivity,” are important for spectral grouping. For our model, we simulated combination sensitivity using Hebbian and associative types of synaptic plasticity in auditory neurons. We also provided a parallel tonotopic input that converges and diverges within the network. Neurons in higher-order layers of the network exhibited an emergent property of multifrequency tuning that is consistent with experimental findings. Furthermore, this network had the capacity to “recognize” the pitch or fundamental frequency of a harmonic tone complex even when the fundamental frequency itself was missing. Received: 6 October 2001 / Accepted in revised form: 21 January 2002     

Neural Mechanisms of Cortical Motion Computation Based on a Neuromorphic Sensory System

The visual cortex analyzes motion information along hierarchically arranged visual areas that interact through bidirectional interconnections. This work suggests a bio-inspired visual model focusing on the interactions of the cortical areas in which a new mechanism of feedforward and feedback processing are introduced. The model uses a neuromorphic vision sensor (silicon retina) that simulates the spike-generation functionality of the biological retina. Our model takes into account two main model visual areas, namely V1 and MT, with different feature selectivities. The initial motion is estimated in model area V1 using spatiotemporal filters to locally detect the direction of motion. Here, we adapt the filtering scheme originally suggested by Adelson and Bergen to make it consistent with the spike representation of the DVS. The responses of area V1 are weighted and pooled by area MT cells which are selective to different velocities, i.e. direction and speed. Such feature selectivity is here derived from compositions of activities in the spatio-temporal domain and integrating over larger space-time regions (receptive fields). In order to account for the bidirectional coupling of cortical areas we match properties of the feature selectivity in both areas for feedback processing. For such linkage we integrate the responses over different speeds along a particular preferred direction. Normalization of activities is carried out over the spatial as well as the feature domains to balance the activities of individual neurons in model areas V1 and MT. Our model was tested using different stimuli that moved in different directions. The results reveal that the error margin between the estimated motion and synthetic ground truth is decreased in area MT comparing with the initial estimation of area V1. In addition, the modulated V1 cell activations shows an enhancement of the initial motion estimation that is steered by feedback signals from MT cells.     

Modulation of activity in human visual area V1 during memory masking

Neurons in the primary visual cortex, V1, are specialized for the processing of elemental features of the visual stimulus, such as orientation and spatial frequency. Recent fMRI evidence suggest that V1 neurons are also recruited in visual perceptual memory; a number of studies using multi-voxel pattern analysis have successfully decoded stimulus-specific information from V1 activity patterns during the delay phase in memory tasks. However, consistent fMRI signal modulations reflecting the memory process have not yet been demonstrated. Here, we report evidence, from three subjects, that the low V1 BOLD activity during retention of low-level visual features is caused by competing interactions between neural populations coding for different values along the spectrum of the dimension remembered. We applied a memory masking paradigm in which the memory representation of a masker stimulus interferes with a delayed spatial frequency discrimination task when its frequency differs from the discriminanda with ±1 octave and found that impaired behavioral performance due to masking is reflected in weaker V1 BOLD signals. This cross-channel inhibition in V1 only occurs with retinotopic overlap between the masker and the sample stimulus of the discrimination task. The results suggest that memory for spatial frequency is a local process in the retinotopically organized visual cortex.     

Taking a Call Is Facilitated by the Multisensory Processing of Smartphone Vibrations,Sounds, and Flashes

Many electronic devices that we use in our daily lives provide inputs that need to be processed and integrated by our senses. For instance, ringing, vibrating, and flashing indicate incoming calls and messages in smartphones. Whether the presentation of multiple smartphone stimuli simultaneously provides an advantage over the processing of the same stimuli presented in isolation has not yet been investigated. In this behavioral study we examined multisensory processing between visual (V), tactile (T), and auditory (A) stimuli produced by a smartphone. Unisensory V, T, and A stimuli as well as VA, AT, VT, and trisensory VAT stimuli were presented in random order. Participants responded to any stimulus appearance by touching the smartphone screen using the stimulated hand (Experiment 1), or the non-stimulated hand (Experiment 2). We examined violations of the race model to test whether shorter response times to multisensory stimuli exceed probability summations of unisensory stimuli. Significant violations of the race model, indicative of multisensory processing, were found for VA stimuli in both experiments and for VT stimuli in Experiment 1. Across participants, the strength of this effect was not associated with prior learning experience and daily use of smartphones. This indicates that this integration effect, similar to what has been previously reported for the integration of semantically meaningless stimuli, could involve bottom-up driven multisensory processes. Our study demonstrates for the first time that multisensory processing of smartphone stimuli facilitates taking a call. Thus, research on multisensory integration should be taken into consideration when designing electronic devices such as smartphones.     

Neural mechanisms of human texture processing: texture boundary detection and visual search

Texture of various appearances, geometric distortions, spatial frequency content and densities is utilized by the human visual system to segregate items from background and to enable recognition of complex geometric forms. For automatic, or pre-attentive, segmentation of a visual scene, sophisticated analysis and comparison of surface properties over wide areas of the visual field are required. We investigated the neural substrate underlying human texture processing, particularly the computational mechanisms of texture boundary detection. We present a neural network model which uses as building blocks model cortical areas that are bi-directionally linked to implement cycles of feedforward and feedback interaction for signal detection, hypothesis generation and testing within the infero-temporal pathway of form processing. In the spirit of Jake Beck's early investigations our model particularly builds upon two key hypotheses, namely that (i) texture segregation is based on boundary detection, rather than clustering homogeneous items, and (ii) texture boundaries are detected mainly on the basis of larger scenic contexts mediated by higher cortical areas, such as area V4. The latter constraint provides a basis for element grouping in accordance to the Gestalt laws of similarity and good continuation. It is shown through simulations that the model integrates a variety of psychophysical findings on texture processing and provides a link to the underlying physiology. The functional role of feedback processing is demonstrated by context dependent modulation of V1 cell activation, leading to sharply localized detection of texture boundaries. It furthermore explains why pre-attentive processing in visual search tasks can be directly linked to texture boundary processing as revealed by recent EEG studies on visual search.     

Indicators of Early and Late Processing Reveal the Importance of Within-Trial-Time for Theories of Associative Learning

In four human learning experiments (Pavlovian skin conductance, causal learning, speeded classification task), we evaluated several associative learning theories that assume either an elemental (modified unique cue model and Harris’ model) or a configural (Pearce’s configural theory and an extension of it) form of stimulus processing. The experiments used two modified patterning problems (A/B/C+, AB/BC/AC+ vs. ABC-; A+, BC+ vs. ABC-). Pearce’s configural theory successfully predicted all of our data reflecting early stimulus processing, while the predictions of the elemental theories were in accord with all of our data reflecting later stages of stimulus processing. Our results suggest that the form of stimulus representation depends on the amount of time available for stimulus processing. Our findings highlight the necessity to investigate stimulus processing during conditioning on a finer time scale than usually done in contemporary research.     

Auditory-evoked spike firing in the lateral amygdala and Pavlovian fear conditioning: mnemonic code or fear bias?

Amygdala neuroplasticity has emerged as a candidate substrate for Pavlovian fear memory. By this view, conditional stimulus (CS)-evoked activity represents a mnemonic code that initiates the expression of fear behaviors. However, a fear state may nonassociatively enhance sensory processing, biasing CS-evoked activity in amygdala neurons. Here we describe experiments that dissociate auditory CS-evoked spike firing in the lateral amygdala (LA) and both conditional fear behavior and LA excitability in rats. We found that the expression of conditional freezing and increased LA excitability was neither necessary nor sufficient for the expression of conditional increases in CS-evoked spike firing. Rather, conditioning-related changes in CS-evoked spike firing were solely determined by the associative history of the CS. Thus, our data support a model in which associative activity in the LA encodes fear memory and contributes to the expression of learned fear behaviors.     

A neural network model for the intersensory coordination involved in goal-directed movements

A neural network model for a sensorimotor system, which was developed to simulate oriented movements in man, is presented. It is composed of a formal neural network comprising two layers: a sensory layer receiving and processing sensory inputs, and a motor layer driving a simulated arm. The sensory layer is an extension of the topological network previously proposed by Kohonen (1984). Two kinds of sensory modality, proprioceptive and exteroceptive, are used to define the arm position. Each sensory cell receives proprioceptive inputs provided by each arm-joint together with the exteroceptive inputs. This sensory layer is therefore a kind of associative layer which integrates two separate sensory signals relating to movement coding. It is connected to the motor layer by means of adaptive synapses which provide a physical link between a motor activity and its sensory consequences. After a learning period, the spatial map which emerges in the sensory layer clearly depends on the sensory inputs and an associative map of both the arm and the extra-personal space is built up if proprioceptive and exteroceptive signals are processed together. The sensorimotor transformations occuring in the junctions linking the sensory and motor layers are organized in such a manner that the simulated arm becomes able to reach towards and track a target in extra-personal space. Proprioception serves to determine the final arm posture adopted and to correct the ongoing movement in cases where changes in the target location occur. With a view to developing a sensorimotor control system with more realistic salient features, a robotic model was coupled with the formal neural network. This robotic implementation of our model shows the capacity of formal neural networks to control the displacement of mechanical devices.     

An after-shannon measure of the storage capacity of an associative noise-like coding memory

The maximum amount of information that can be stored, on the average, in each storage element, according to an associative scheme, has been measured for the memory model proposed by the author (Bottini 1980). In this model, the (binary) items being stored are coded by noise-like keys and the memory traces formed in this way are superimposed, by algebraic addition, on the same many-level storage elements. It is shown that the problem of measuring the information retrieved from the memory in a single recall and the problem — concerning the data-communication field —of measuring the information transmitted over a noisy channel are formally similar. In particular, the Shannon noisy-channel coding theorem can find an application also in our case of an associative memory. Finally, it is evidenced that the so-called matrix model of an associative memory has the same storage capacity as the model studied here.