Information Processing In Neural Networks Research Articles

Dynamical Mechanism of Sampling-Based Probabilistic Inference Under Probabilistic Population Codes.

Animals make efficient probabilistic inferences based on uncertain and noisy information from the outside environment. It is known that probabilistic population codes, which have been proposed as a neural basis for encoding probability distributions, allow general neural networks (NNs) to perform near-optimal point estimation. However, the mechanism of sampling-based probabilistic inference has not been clarified. In this study, we trained two types of artificial NNs, feedforward NN (FFNN) and recurrent NN (RNN), to perform sampling-based probabilistic inference. Then we analyzed and compared their mechanisms of sampling. We found that sampling in RNN was performed by a mechanism that efficiently uses the properties of dynamical systems, unlike FFNN. In addition, we found that sampling in RNNs acted as an inductive bias, enabling a more accurate estimation than in maximum a posteriori estimation. These results provide important arguments for discussing the relationship between dynamical systems and information processing in NNs.

Enhancing threshold neural network via suprathreshold stochastic resonance for pattern classification

Hard-threshold nonlinearities are of significant interest for neural-network information processing due to their simplicity and low-cost implementation. They however lack an important differentiability property. Here, hard-threshold nonlinearities receiving assistance from added noise are pooled into a large-scale summing array to approximate a neuron with a noise-smoothed activation function. Differentiability that facilitates gradient-based learning is restored for such neurons, which are assembled into a feed-forward neural network. The added noise components used to smooth the hard-threshold responses have adjustable parameters that are adaptively optimized during the learning process. The converged non-zero optimal noise levels establish a beneficial role for added noise in operation of the threshold neural network. In the retrieval phase the threshold neural network operating with non-zero optimal added noise, is tested for data classification and for handwritten digit recognition, which achieves state-of-the-art performance of existing backpropagation-trained analog neural networks, while requiring only simpler two-state binary neurons.

Temporal Signatures of Criticality in Human Cortical Excitability as Probed by Early Somatosensory Responses.

Brain responses vary considerably from moment to moment, even to identical sensory stimuli. This has been attributed to changes in instantaneous neuronal states determining the system's excitability. Yet the spatiotemporal organization of these dynamics remains poorly understood. Here we test whether variability in stimulus-evoked activity can be interpreted within the framework of criticality, which postulates dynamics of neural systems to be tuned toward the phase transition between stability and instability as is reflected in scale-free fluctuations in spontaneous neural activity. Using a novel noninvasive approach in 33 male human participants, we tracked instantaneous cortical excitability by inferring the magnitude of excitatory postsynaptic currents from the N20 component of the somatosensory evoked potential. Fluctuations of cortical excitability demonstrated long-range temporal dependencies decaying according to a power law across trials, a hallmark of systems at critical states. As these dynamics covaried with changes in prestimulus oscillatory activity in the alpha band (8-13 Hz), we establish a mechanistic link between ongoing and evoked activity through cortical excitability and argue that the co-emergence of common temporal power laws may indeed originate from neural networks poised close to a critical state. In contrast, no signatures of criticality were found in subcortical or peripheral nerve activity. Thus, criticality may represent a parsimonious organizing principle of variability in stimulus-related brain processes on a cortical level, possibly reflecting a delicate equilibrium between robustness and flexibility of neural responses to external stimuli.SIGNIFICANCE STATEMENT Variability of neural responses in primary sensory areas is puzzling, as it is detrimental to the exact mapping between stimulus features and neural activity. However, such variability can be beneficial for information processing in neural networks if it is of a specific nature, namely, if dynamics are poised at a so-called critical state characterized by a scale-free spatiotemporal structure. Here, we demonstrate the existence of a link between signatures of criticality in ongoing and evoked activity through cortical excitability, which fills the long-standing gap between two major directions of research on neural variability: the impact of instantaneous brain states on stimulus processing on the one hand and the scale-free organization of spatiotemporal network dynamics of spontaneous activity on the other.

Progress in fluorescent visualization techniques for neurotransmitter detection

Neurotransmitters play an important role in nervous system. Temporal and spatial changes of neurotransmitter distribution are crucial to information processing in neural networks. Biosensors that can visually monitor neurotransmitters are one of the vital tools to explore a variety of physiological and pathological activities. This article reviews recent advances in monitoring neurotransmitters with high temporal and spatial resolution, and introduces the latest fluorescent imaging methods for typical neurotransmitters, including glutamate, dopamine, γ-aminobutyric acid and acetylcholine. The article also summarizes the basic principles, advantages and disadvantages of various visually detection methods, and provides systematic suggestions for designing neurotransmitter sensors with high temporal and spatial resolution.

Closed-Loop Transcranial Ultrasound Stimulation for Real-Time Non-invasive Neuromodulation in vivo.

The closed-loop brain stimulation technique plays a key role in neural network information processing and therapies of neurological diseases. Transcranial ultrasound stimulation (TUS) is an established neuromodulation method for the neural oscillation in animals or human. All available TUS systems provide brain stimulation in an open-loop pattern. In this study, we developed a closed-loop transcranial ultrasound stimulation (CLTUS) system for real-time non-invasive neuromodulation in vivo. We used the CLTUS system to modulate the neural activities of the hippocampus of a wild-type mouse based on the phase of the theta rhythm recorded at the ultrasound-targeted location. In addition, we modulated the hippocampus of a temporal lobe epilepsy (TLE) mouse. The ultrasound stimulation increased the absolute power and reduced the relative power of the theta rhythm, which were independent of the specific phase of the theta rhythm. Compared with those of a sham stimulation, the latency of epileptic seizures was significantly increased, while the epileptic seizure duration was significantly decreased under the CLTUS. The above results indicate that the CLTUS can be used to not only modulate the neural oscillation through the theta-phase-specific manipulation of the hippocampus but also effectively inhibit the seizure of a TLE mouse in time. CLTUS has large application potentials for the understanding of the causal relationship of neural circuits as well as for timely, effective, and non-invasive therapies of neurological diseases such as epilepsy and Parkinson’s disease.

Inductively coupled burst oscillators in neural network information processing systems

In the current study, we present a self-generating circuit based on an LC oscillatory circuit with a switching element having an S-type current-voltage characteristic. Using the example of a VO2 switch, we demonstrate that such circuits generate burst oscillations and have a specific mode of bursts synchronization using inductive coupling between generators. Such generators of burst oscillations (of a neural type) can find application in the tasks of cognitive technologies, including the implementation of pulsed neural networks.

Real-time implementation of ReSuMe learning in Spiking Neural Network

Neuromorphic systems are designed by mimicking or being inspired by the nervous system, which realizes robust, autonomous, and power-efficient information processing by highly parallel architecture. Supervised learning was proposed as a successful concept of information processing in neural network. Recently, there has been an increasing body of evidence that instruction-based learning is also exploited by the brain. ReSuMe is a proposed algorithm by Ponulak and Kasinski in 2010. It proposes a supervised learning for biologically plausible neurons that reproduce template signals (instructions) or patterns encoded in precisely timed sequences of spikes. Here, we present a real-time ReSuMe learning implementation on FPGA using Leaky Integrate-and-fire (LIF) Spiking Neural Network (SNN). FPGA allows real-time implementation and embedded system. We show that this implementation can make successful the learning on a specific pattern.

Enhancement of electro-chemical properties of TiO2 nanotubes for biological interfacing

Micro/nano electrodes employing nanotubes has attracted paramount attention in recent years due to their inherent superior mechanical and structural properties. Electrical interfaces with different geometries and sizes have been developed as electrodes for measuring action potentials and investigating neural information processing in neural networks. In this work, we investigated the possibility of using TiO2 nanotube arrays that were grown using electrochemical anodization technique, as a micro/nano electrode for neural interfacing. The morphology of fabricated nanotube arrays were found to be significantly affected by the applied voltage. Annealing and doping of TiO2 nanotube arrays has been performed to improve the structural and electrical properties of the nanotube arrays. It was found that the annealing and doping with nitrogen improve the electrical conductivity of the nanotube arrays. Moreover, the tube diameter and length can be controlled by changing the applied voltage and that can significantly affect the biocompatibility of the nanotube arrays. It was observed that nitrogen doped nanotubes with morphology consisting of 61nm diameter, 25nm wall thickness and tube length of 2.25μm could be good candidate to be used as electrodes for biological interfacing. This is due to the fact that the nitrogen doped nanotubes with aforementioned morphology possess great properties necessary for effective biological interfacing such as low impedance, high capacitance and good biocompatibility.

Spintronic characteristics of self-assembled neurotransmitter acetylcholine molecular complexes enable quantum information processing in neural networks and brain

Implementation of liquid state quantum information processing based on spatially localized electronic spin in the neurotransmitter stable acetylcholine (ACh) neutral molecular radical is discussed. Using DFT quantum calculations we proved that this molecule possesses stable localized electron spin, which may represent a qubit in quantum information processing.The necessary operating conditions for ACh molecule are formulated in self-assembled dimer and more complex systems.The main quantum mechanical research result of this paper is that the neurotransmitter ACh systems, which were proposed, include the use of quantum molecular spintronics arrays to control the neurotransmission in neural networks.

Label-Free SERS Selective Detection of Dopamine and Serotonin Using Graphene-Au Nanopyramid Heterostructure.

Ultrasensitive detection and spatially resolved mapping of neurotransmitters, dopamine and serotonin, are critical to facilitate understanding brain functions and investigate the information processing in neural networks. In this work, we demonstrated single molecule detection of dopamine and serotonin using a graphene-Au nanopyramid heterostructure platform. The quasi-periodic Au structure boosts high-density and high-homogeneity hotspots resulting in ultrahigh sensitivity with a surface enhanced Raman spectroscopic (SERS) enhancement factor ∼10(10). A single layer graphene superimposed on a Au structure not only can locate SERS hot spots but also modify the surface chemistry to realize selective enhancement Raman yield. Dopamine and serotonin could be detected and distinguished from each other at 10(-10) M level in 1 s data acquisition time without any pretreatment and labeling process. Moreover, the heterostructure realized nanomolar detection of neurotransmitters in the presence of simulated body fluids. These findings represent a step forward in enabling in-depth studies of neurological processes including those closely related to brain activity mapping (BAM).

Neurotransmitter Detection Using Corona Phase Molecular Recognition on Fluorescent Single-Walled Carbon Nanotube Sensors

Temporal and spatial changes in neurotransmitter concentrations are central to information processing in neural networks. Therefore, biosensors for neurotransmitters are essential tools for neuroscience. In this work, we applied a new technique, corona phase molecular recognition (CoPhMoRe), to identify adsorbed polymer phases on fluorescent single-walled carbon nanotubes (SWCNTs) that allow for the selective detection of specific neurotransmitters, including dopamine. We functionalized and suspended SWCNTs with a library of different polymers (n = 30) containing phospholipids, nucleic acids, and amphiphilic polymers to study how neurotransmitters modulate the resulting band gap, near-infrared (nIR) fluorescence of the SWCNT. We identified several corona phases that enable the selective detection of neurotransmitters. Catecholamines such as dopamine increased the fluorescence of specific single-stranded DNA- and RNA-wrapped SWCNTs by 58-80% upon addition of 100 μM dopamine depending on the SWCNT chirality (n,m). In solution, the limit of detection was 11 nM [K(d) = 433 nM for (GT)15 DNA-wrapped SWCNTs]. Mechanistic studies revealed that this turn-on response is due to an increase in fluorescence quantum yield and not covalent modification of the SWCNT or scavenging of reactive oxygen species. When immobilized on a surface, the fluorescence intensity of a single DNA- or RNA-wrapped SWCNT is enhanced by a factor of up to 5.39 ± 1.44, whereby fluorescence signals are reversible. Our findings indicate that certain DNA/RNA coronae act as conformational switches on SWCNTs, which reversibly modulate the SWCNT fluorescence. These findings suggest that our polymer-SWCNT constructs can act as fluorescent neurotransmitter sensors in the tissue-compatible nIR optical window, which may find applications in neuroscience.

Cell Clocks and Neuronal Networks: Neuron Ticking and Synchronization in Aging and Aging-Related Neurodegenerative Disease

Body function rhythmicity has a key function for the regulation of internal timing and adaptation to the environment. A wealth of recent data has implicated endogenous biological rhythm generation and regulation in susceptibility to disease, longevity, cognitive performance. Concerning brain diseases, it has been established that many molecular pathways implicated in neurodegeneration are under circadian regulation. At the molecular level, this regulation relies on clock genes forming interconnected, self-sustained transcriptional/translational feedback loops. Cells of the master circadian pacemaker, the hypothalamic suprachiasmatic nucleus, are endowed with this molecular clockwork. Brain cells in many other regions, including those which play a key role in learning and memory, as well as peripheral cells show a circadian oscillatory behavior regulated by the same molecular clockwork. We here address the question as to whether intracellular clockwork signaling and/or the intercellular dialogue between "brain clocks" are disrupted in aging-dependent neurodegenerative diseases, such as Parkinson's disease and Alzheimer's disease. The potential implications of clock genes in cognitive functions in normal conditions, clinical disturbances of circadian rhythms, and especially the sleep-wake cycle, in aging-dependent neurodegenerative diseases and data in animal models are reviewed. The currently limited knowledge in this field is discussed in the context of the more extensive body of data available on cell clocks and molecular clockwork during normal aging. Hypotheses on implications of the synchronization between brain oscillators in information processing in neural networks lay ground for future studies on brain health and disease.

Weight to Vision Neural Network Information Processing Influence Research

Many factors influence vision neural network information processing process, for example: Signal initial value, weight, time and number of learning. This paper discussed the importance of weight in vision neural network information processing process. Different weight values can cause different results in neural networks learning. We structure a vision neural network model with three layers based on synapse dynamics at first. Then we change the weights of the vision neural network model’s to make the three layers a neural network of learning Chinese characters. At last we change the initial weight distribution to simulate the neural network of process of the learning Chinese words. Two results are produced. One is that weight plays a very important role in vision neural networks learning, the other is that different initial weight distributions have different results in vision neural networks learning.

What are the mechanisms for analogue and digital signalling in the brain?

Synaptic transmission in the brain generally depends on action potentials. However, recent studies indicate that subthreshold variation in the presynaptic membrane potential also determines spike-evoked transmission. The informational content of each presynaptic action potential is therefore greater than initially expected. The contribution of this synaptic property, which is a fast (from 0.01 to 10 s) and state-dependent modulation of functional coupling, has been largely underestimated and could have important consequences for our understanding of information processing in neural networks. We discuss here how the membrane voltage of the presynaptic terminal might modulate neurotransmitter release by mechanisms that do not involve a change in presynaptic Ca(2+) influx.

Human embryonic stem cell-derived neurons adopt and regulate the activity of an established neural network

Whether hESC-derived neurons can fully integrate with and functionally regulate an existing neural network remains unknown. Here, we demonstrate that hESC-derived neurons receive unitary postsynaptic currents both in vitro and in vivo and adopt the rhythmic firing behavior of mouse cortical networks via synaptic integration. Optical stimulation of hESC-derived neurons expressing Channelrhodopsin-2 elicited both inhibitory and excitatory postsynaptic currents and triggered network bursting in mouse neurons. Furthermore, light stimulation of hESC-derived neurons transplanted to the hippocampus of adult mice triggered postsynaptic currents in host pyramidal neurons in acute slice preparations. Thus, hESC-derived neurons can participate in and modulate neural network activity through functional synaptic integration, suggesting they are capable of contributing to neural network information processing both in vitro and in vivo.

Multiple resonances with time delays and enhancement by non-Gaussian noise in Newman–Watts networks of Hodgkin–Huxley neurons

In this paper, we study the effect of time delay on the spiking activity in Newman–Watts small-world networks of Hodgkin–Huxley neurons with non-Gaussian noise, and investigate how the non-Gaussian noise affects the delay-induced behaviors. It was found that, as the delay increases, the neuron spiking intermittently performs the most ordered and synchronized behavior when the delay lengths are integer multiples of the spiking periods, which shows multiple temporal resonances and spatial synchronizations, and reveals that the locking between the delay lengths and the spiking periods might be the mechanism behind the behaviors. It was also found that the delay-optimized spiking behaviors could be enhanced when non-Gaussian noise's deviation from the Gaussian noise is appropriate. These results show that time delay and non-Gaussian noise would cooperate to play more constructive and efficient roles in the information processing of neural networks.

Enhancement of information transmission of sub-threshold signals applied to distal positions of dendritic trees in hippocampal CA1 neuron models with stochastic resonance

Stochastic resonance (SR) has been shown to enhance the signal-to-noise ratio and detection of low level signals in neurons. It is not yet clear how this effect of SR plays an important role in the information processing of neural networks. The objective of this article is to test the hypothesis that information transmission can be enhanced with SR when sub-threshold signals are applied to distal positions of the dendrites of hippocampal CA1 neuron models. In the computer simulation, random sub-threshold signals were presented repeatedly to a distal position of the main apical branch, while the homogeneous Poisson shot noise was applied as a background noise to the mid-point of a basal dendrite in the CA1 neuron model consisting of the soma with one sodium, one calcium, and five potassium channels. From spike firing times recorded at the soma, the mutual information and information rate of the spike trains were estimated. The simulation results obtained showed a typical resonance curve of SR, and that as the activity (intensity) of sub-threshold signals increased, the maximum value of the information rate tended to increased and eventually SR disappeared. It is concluded that SR can play a key role in enhancing the information transmission of sub-threshold stimuli applied to distal positions on the dendritic trees.

Somatostatin interneurons delineate the inner part of the external plexiform layer in the mouse main olfactory bulb

Neuropeptides play a major role in the modulation of information processing in neural networks. Somatostatin, one of the most concentrated neuropeptides in the brain, is found in many sensory systems including the olfactory pathway. However, its cellular distribution in the mouse main olfactory bulb (MOB) is yet to be characterized. Here we show that approximately 95% of mouse bulbar somatostatin-immunoreactive (SRIF-ir) cells describe a homogeneous population of interneurons. These are restricted to the inner lamina of the external plexiform layer (iEPL) with dendritic field strictly confined to the region. iEPL SRIF-ir neurons share some morphological features of Van Gehuchten short-axon cells, and always express glutamic acid decarboxylase, calretinin, and vasoactive intestinal peptide. One-half of SRIF-ir neurons are parvalbumin-ir, revealing an atypical neurochemical profile when compared to SRIF-ir interneurons of other forebrain regions such as cortex or hippocampus. Somatostatin is also present in fibers and in a few sparse presumptive deep short-axon cells in the granule cell layer (GCL), which were previously reported in other mammalian species. The spatial distribution of somatostatin interneurons in the MOB iEPL clearly outlines the region where lateral dendrites of mitral cells interact with GCL inhibitory interneurons through dendrodendritic reciprocal synapses. Symmetrical and asymmetrical synaptic contacts occur between SRIF-ir dendrites and mitral cell dendrites. Such restricted localization of somatostatin interneurons and connectivity in the bulbar synaptic network strongly suggest that the peptide plays a functional role in the modulation of olfactory processing.

Magnetoenzephalographie in der Psychiatrie

Neuropsychiatric disorders usually come with only sublime structural changes. Functional imaging can point at specific disturbances in information processing in neural networks. Besides imaging of receptor and metabolic functions with PET and fMRI, electromagnetic methods such as electroencephalography (EEG) and magnetoencephalography (MEG) offer the possibility for imaging of dynamic dysfunctions. As compared to EEG, MEG has a shorter history and is less common despite offering considerable advantages in temporospatial resolution and sensitivity to detect impaired signal processing and network functioning which renders it particularly interesting for psychiatric applications. Disturbed processing in the auditory and visual domain emerging in schizophrenic, affective and anxiety disorders can be detected with high sensitivity. Moreover, the neuromagnetic baseline activity allows conclusions to be drawn regarding neural network functions. Due to its high sensitivity to single deficits in information processing and to pharmacological effects, MEG will achieve clinical significance in specific areas.

Response Properties of Motion-Sensitive Visual Interneurons in the Lobula Plate of Drosophila melanogaster

The crystalline-like structure of the optic lobes of the fruit fly Drosophila melanogaster has made them a model system for the study of neuronal cell-fate determination, axonal path finding, and target selection. For functional studies, however, the small size of the constituting visual interneurons has so far presented a formidable barrier. We have overcome this problem by establishing in vivo whole-cell recordings from genetically targeted visual interneurons of Drosophila. Here, we describe the response properties of six motion-sensitive large-field neurons in the lobula plate that form a network consisting of individually identifiable, directionally selective cells most sensitive to vertical image motion (VS cells). Individual VS cell responses to visual motion stimuli exhibit all the characteristics that are indicative of presynaptic input from elementary motion detectors of the correlation type. Different VS cells possess distinct receptive fields that are arranged sequentially along the eye's azimuth, corresponding to their characteristic cellular morphology and position within the retinotopically organized lobula plate. In addition, lateral connections between individual VS cells cause strongly overlapping receptive fields that are wider than expected from their dendritic input. Our results suggest that motion vision in different dipteran fly species is accomplished in similar circuitries and according to common algorithmic rules. The underlying neural mechanisms of population coding within the VS cell network and of elementary motion detection, respectively, can now be analyzed by the combination of electrophysiology and genetic intervention in Drosophila.