Spiking Neurons

The process of segmenting images is one of the most critical ones in automatic image analysis whose goal can be regarded as to find what objects are present in images. Artificial neural networks have been well developed so far. First two generations of neural networks have a lot of successful applications. Spiking neuron networks (SNNs) are often referred to as the third generation of neural networks which have potential to solve problems related to biological stimuli. They derive their strength and interest from an accurate modeling of synaptic interactions between neurons, taking into account the time of spike emission. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Based on dynamic event-driven processing, they open up new horizons for developing models with an exponential capacity of memorizing and a strong ability to fast adaptation. Moreover, SNNs add a new dimension, the temporal axis, to the representation capacity and the processing abilities of neural networks. In this paper, we present how SNN can be applied with efficacy in image segmentation and edge detection. Results obtained confirm the validity of the approach.

Transcranial alternating current stimulation (tACS) is used in clinical applications and basic neuroscience research. Although its behavioral effects are evident from prior reports, current understanding of the mechanisms that underlie these effects is limited. We used motion perception, a percept with relatively well known properties and underlying neural mechanisms to investigate tACS mechanisms. Healthy human volunteers showed a surprising improvement in motion sensitivity when visual stimuli were paired with 10 Hz tACS. In addition, tACS reduced the motion-after effect, and this reduction was correlated with the improvement in motion sensitivity. Electrical stimulation had no consistent effect when applied before presenting a visual stimulus or during recovery from motion adaptation. Together, these findings suggest that perceptual effects of tACS result from an attenuation of adaptation. Important consequences for the practical use of tACS follow from our work. First, because t...

Neuromorphic neural networks are of interest both from a biological point of view and in terms of robust signaling in noisy environments. The basic question however, is what type of architecture can be used to efficiently build such neural networks in hardware devices, in order to use them in real time process control problems. In this paper a novel, hardware implementation friendly, "pulse reactive" model of spiking neurons is described. This is used then to implement a fully connected network, yielding a high degree of parallelism. The modular neuron structure, acquired signals and a process control application are given.

A brain-inspired computational system is presented that allows sequential selection and processing of objects from a visual scene. The system is comprised of three modules. The selective attention module is designed as a network of spiking neurons of the Hodgkin-Huxley type with star-like connections between the central unit and peripheral elements. The attention focus is represented by those peripheral neurons that generate spikes synchronously with the central neuron while the activity of other peripheral neurons is suppressed. Such dynamics corresponds to the partial synchronization mode. It is shown that peripheral neurons with higher firing rates are preferentially drawn into partial synchronization. We show that local excitatory connections facilitate synchronization, while local inhibitory connections help distinguishing between two groups of peripheral neurons with similar intrinsic frequencies. The module automatically scans a visual scene and sequentially selects regions o...

In this paper we present 2 patient-specific models (PSM) developed by 3D Visual Membrane Petri Nets (VMPN) application. A PSM of Blood Glucose Level for Type 1 Diabetes (T1D) patients that reflects different glucose concentrations in arteries and veins for 3 distinct cases: health condition and both the hepatic insulin-resistance and hepatic insulin-sensitivity in T1D patient is described. This model can explain the predisposal of T1D patients to diabetic retinopathy and nephropathy. A computational PSM of Epileptic Activity that exhibits epileptic manifestations and treatment is proposed. Both of these PSMs are parts of the integrated PSM (IPSM) that represents healthy patients or with certain diseases (T1D, epilepsy, thyroid disorders, cardiac diseases). Over time, our IPSM is completed and developed by new PSMs.

Transcranial alternating current stimulation (tACS) seems likely to open a new era of the field of noninvasive electrical stimulation of the human brain by directly interfering with cortical rhythms. It is expected to synchronize (by one single resonance frequency) or desynchronize (e.g., by the application of several frequencies) cortical oscillations. If applied long enough it may cause neuroplastic effects. In the theta range it may improve cognition when applied in phase. Alpha rhythms could improve motor performance, whereas beta intrusion may deteriorate them. TACS with both alpha and beta frequencies has a high likelihood to induce retinal phosphenes. Gamma intrusion can possibly interfere with attention. Stimulation in the "ripple" range induces intensity dependent inhibition or excitation in the motor cortex (M1) most likely by entrainment of neuronal networks, whereas stimulation in the low kHz range induces excitation by neuronal membrane interference. TACS in t...

Spiking neural networks (SNN) are promising artificial neural network (ANN) models as they utilise information representation as trains of spikes, that adds new dimensions of time, frequency and phase to the structure and the functionality of ANN. The current SNN models though are deterministic, that restricts their applications for large scale engineering and cognitive modelling of stochastic processes. This paper proposes a novel probabilistic spiking neuron model (pSNM) and suggests ways of building pSNN for a wide range of applications including classification, string pattern recognition and associative memory. It also extends previously published computational neurogenetic models.

Inspired by the Theory of Neuronal Group Selection (TNGS), we have carried out synthesis of frequency generator via spiking neurons network through genetic algorithm. The TNGS sets that a neuronal group is the most basic unit in the cortical area and are generated by synapses of localized neural cells in the cortical area of the brain firing and oscillating in synchrony at a predefined frequency. Each one of these clusters (Neuronal Groups) is a set of localized, tightly coupled neurons developed in the embryo. According to this proposal, this paper describes a method of tuning the parameters of the Izhikevich spiking neuron model. Computational experiments consisting of a network with all neurons of the same type and a network with different neurons were conducted. A genetic algorithm was used to tune the parameters in these two different cases. The results were compared in order to find the best way to create a frequency generator of spiking neurons network.

Neuron form the basic computation unit of brain. These cells form connection with one another in the brain and form network in which information is processed. Immense information processing ability of neural network is attributed to its exceptional parallel processing. Neuron in network fires spikes which travels and effect other neurons in the network as well. Brain cultures of rat embryos are made on multi electrode array dish. MEA dish facilitates study of network topology as it allows recording from population of neuron. Time of spike occurrences in the network was recorded along with the electrode from which activity was recorded. For analysis of network related activity of dynamic network of neurons, the proposed framework ranks nodes of network based on two paradigms and defines a parameter activity index. Activity index takes into account the activity of neuron and reports the changing of activity hub of the neuron network with time. A comparative study is done on the result of two paradigms.

Cheetahs and beetles run, dolphins and salmon swim, and bees and birds fly with grace and economy surpassing our technology. Evolution has shaped the breathtaking abilities of animals, leaving us the challenge of reconstructing their targets of control and mechanisms of dexterity. In this review we explore a corner of this fascinating world. We describe mathematical models for legged animal

In this article, our wish is to demystify some aspects of coding with spike-timing, through a simple review of well-understood technical facts regarding spike coding. The goal is to help better understanding to which extend computing and modelling with spiking neuron networks can be biologically plausible and computationally efficient. We intentionally restrict ourselves to a deterministic dynamics, in this review, and we consider that the dynamics of the network is defined by a non-stochastic mapping. This allows us to stay in a rather simple framework and to propose a review with concrete numerical values, results and formula on (i) general time constraints, (ii) links between continuous signals and spike trains, (iii) spiking networks parameter adjustments. When implementing spiking neuron networks, for computational or biological simulation purposes, it is important to take into account the indisputable facts here reviewed. This precaution could prevent from implementing mechanisms meaningless with regards to obvious time constraints, or from introducing spikes artificially, when continuous calculations would be sufficient and simpler. It is also pointed out that implementing a spiking neuron network is finally a simple task, unless complex neural codes are considered.

This paper is an attempt to incorporate the idea of spiking neurons into the area of membrane computing, and to this aim we introduce a class of neural-like P systems which we call spiking neural P systems (in short, SN P systems). In these devices, the time (when the neurons flre and/or spike) plays an essential role. For instance, the

Mining temporal network models from discrete event streams is an important problem with applications in computational neuroscience, physical plant diagnostics, and human-computer interaction modeling. We focus in this paper on temporal models representable as excitatory networks where all connections are stimulative, rather than inhibitive. Through this emphasis on excitatory networks, we show how they can be learned by creating bridges to frequent episode mining. Specifically, we show that frequent episodes help identify nodes with high mutual information relationships and which can be summarized into a dynamic Bayesian network (DBN). To demonstrate the practical feasibility of our approach, we show how excitatory networks can be inferred from both mathematical models of spiking neurons as well as real neuroscience datasets.

While both dopamine (DA) fluctuations and spike-timing-dependent plasticity (STDP) are known to influence long-term corticostriatal plasticity, little attention has been devoted to the interaction between these two fundamental mechanisms. Here, a theoretical framework is proposed to account for experimental results specifying the role of presynaptic activation, postsynaptic activation, and concentrations of extracellular DA in synaptic plasticity. Our starting point was an explicitly-implemented multiplicative rule linking STDP to Michaelis-Menton equations that models the dynamics of extracellular DA fluctuations. This rule captures a wide range of results on conditions leading to long-term potentiation and depression in simulations that manipulate the frequency of induced corticostriatal stimulation and DA release. A well-documented biphasic function relating DA concentrations to synaptic plasticity emerges naturally from simulations involving a multiplicative rule linking DA and neural activity. This biphasic function is found consistently across different neural coding schemes employed (voltage-based vs. spike-based models). By comparison, an additive rule fails to capture these results. The proposed framework is the first to generate testable predictions on the dual influence of DA concentrations and STDP on long-term plasticity, suggesting a way in which the biphasic influence of DA concentrations can modulate the direction and magnitude of change induced by STDP, and raising the possibility that DA concentrations may inverse the LTP/LTD components of the STDP rule.

The influx of calcium ions into the dendritic spines through the N-metyl-D-aspartate (NMDA) channels is believed to be the primary trigger for various forms of synaptic plasticity. In this paper, the authors calculate analytically the mean values of the calcium transients elicited by a spiking neuron undergoing a simple model of ionic currents and back-propagating action potentials. The relative variability of these transients, due to the stochastic nature of synaptic transmission, is further considered using a simple Markov model of NMDA receptos. One finds that both the mean value and the variability depend on the timing between pre- and postsynaptic action-potentials. These results could have implications on the expected form of synaptic-plasticity curve and can form a basis for a unified theory of spike time-dependent, and rate based plasticity.