Real Biological Neurons Research Articles

Generalized Bio-Plausible Neuron Model with Refractoriness, Frequency Adaptation and Dynamic Threshold

Real biological neurons are dynamical systems that can process a nonlinear, arbitrary input signal with a dynamic threshold voltage, exhibit refractory time, and have a smooth [Formula: see text]–[Formula: see text] curve. This paper presents a generalized bio-plausible neuron model with refractoriness, frequency adaptation, and dynamic threshold. The spiking characteristics of the generalized neuron are enhanced with less circuitry and fewer parameters compared to older circuits. The proposed silicon neuron can generate all known spiking behaviors, just like a biological neuron. The circuit is designed using a 180 nm standard CMOS process. The entire design uses 22 transistors with a total power consumption of only 2 nW for a 1 nA step current at a spike frequency of 122 Hz. The power consumption and spike frequency rate depend on the input current. The proposed neuron is also capable of displaying both Type I and Type II frequency response characteristics. In addition, our neuron model underwent Monte Carlo simulation with 1000 sample runs to assess its robustness and stability against process variations and uncertainties. The results indicate that the average energy consumption of the proposed neuron model falls within the range of 16 pJ to 68 pJ.

Neural encoding with unsupervised spiking convolutional neural network

Accurately predicting the brain responses to various stimuli poses a significant challenge in neuroscience. Despite recent breakthroughs in neural encoding using convolutional neural networks (CNNs) in fMRI studies, there remain critical gaps between the computational rules of traditional artificial neurons and real biological neurons. To address this issue, a spiking CNN (SCNN)-based framework is presented in this study to achieve neural encoding in a more biologically plausible manner. The framework utilizes unsupervised SCNN to extract visual features of image stimuli and employs a receptive field-based regression algorithm to predict fMRI responses from the SCNN features. Experimental results on handwritten characters, handwritten digits and natural images demonstrate that the proposed approach can achieve remarkably good encoding performance and can be utilized for “brain reading” tasks such as image reconstruction and identification. This work suggests that SNN can serve as a promising tool for neural encoding.

Comparative study of single biological neuron with an artificial neuron

A number of artificial neural models have been presented in the literature in an effort to suggest a more accurate representation of a single biological neuron. There are numerous publications on synthetic neurons that attempted to replicate a single biological neuron, however, such models were unable to generate the spiking patterns of a real biological neuron. Therefore, there is still scope to design and research improved spiking neural models that more accurately reflect the functions of a biological neuron. This motivation drives extensive modification of an artificial neuron model to produce the spike patterns of a real biological neuron. The modified single artificial neuron model that has been proposed exhibits the functions of a biological neuron. It’s still crucial to model spiking bio-neuron behavior. Modeling a spiking bio-neuron is still an important exercise in view of possible applications of the underlying features in the areas of neuromorphic engineering, cognitive radio, and spiking neural networks

Distribution of spiking and bursting in Rulkov’s neuron model

Large-scale brain simulations require the investigation of large networks of realistic neuron models, usually represented by sets of differential equations. Here we report a detailed fine-scale study of the dynamical response over extended parameter ranges of a computationally inexpensive model, the two-dimensional Rulkov map, which reproduces well the spiking and spiking-bursting activity of real biological neurons. In addition, we provide evidence of the existence of nested arithmetic progressions among periodic pulsing and bursting phases of Rulkov’s neuron. We find that specific remarkably complex nested sequences of periodic neural oscillations can be expressed as simple linear combinations of pairs of certain basal periodicities. Moreover, such nested progressions are robust and can be observed abundantly in diverse control parameter planes which are described in detail. We believe such findings to add significantly to the knowledge of Rulkov neuron dynamics and to be potentially helpful in large-scale simulations of the brain and other complex neuron networks.

The Engineered CRISPR-CAS System is a Beneficial Biological Tool for Detecting and Combating Antibiotic Resistance Microbes

Nowadays, the quick detection of antibiotic resistance bacteria causes a major problem in the field of the development of new antibiotics against the resistant bacteria. To overcome this problem, genome editing tools like clustered regularly interspaced short palindromic repeats (CRISPR) can be used. The CRISPR-CAS system is useful for targeting and killing antibiotic-resistant bacteria by cleaving resistance genes. It is also used to detect antibioticresistant bacteria. CRISPR is made up of a single guide RNA and the CAS 9 protein. The single guide RNA is used to guide toward the target sequence, and the CAS 9 protein is an enzyme that cuts DNA and is used in conjunction with the guide RNA. This modified sgRNA contains a complementary sequence to that of the target resistance gene and recognizes the target resistance sequence; therefore, it is cleaved by CAS-9 protein, and the removal of the resistance gene turns bacteria into antibiotic-sensitive ones. One of the delivery systems of CRISPR into bacteria is via bacteriophage.

FPGA Realization of Fractional Order Neuron

In this paper fractional Hindmarsh Rose (HR) neuron, which mimics several behaviors of a real biological neuron is implemented on field programmable gate array (FPGA). The results show several differences in the dynamic characteristics of integer and fractional order Hindmarsh Rose neuron models. The integer order model shows only one type of firing characteristics when the parameters of model remains same. The fractional order model depicts several dynamical behaviors even for the same parameters as the order of the fractional operator is varied. The firing frequency increases when the order of the fractional operator decreases. The fractional order is therefore key in determining the firing characteristics of biological neurons. To implement this neuron model first the digital realization of different fractional operator approximations are obtained, then the fractional integrator is used to obtain the low power and low cost hardware realization of fractional HR neuron. The fractional neuron model has been implemented on a low voltage and low power circuit and then compared with its integer counter part. The hardware is used to demonstrate the different dynamical behaviors of fractional HR neuron for different type of approximations obtained for fractional operator in this paper. A coupled network of fractional order HR neurons is also implemented. The results also show that synchronization between neurons increases as long as coupling factor keeps on increasing.

Synchronization of Hindmarsh Rose Neurons

Modeling and implementation of biological neurons are key to the fundamental understanding of neural network architectures in the brain and its cognitive behavior. Synchronization of neuronal models play a significant role in neural signal processing as it is very difficult to identify the actual interaction between neurons in living brain. Therefore, the synchronization study of these neuronal architectures has received extensive attention from researchers. Higher biological accuracy of these neuronal units demands more computational overhead and requires more hardware resources for implementation. This paper presents a two coupled hardware implementation of Hindmarsh Rose neuron model which is mathematically simpler model and yet mimics several behaviors of a real biological neuron. These neurons are synchronized using an exponential function. The coupled system shows several behaviors depending upon the parameters of HR model and coupling function. An approximation of coupling function is also provided to reduce the hardware cost. Both simulations and a low cost hardware implementations of exponential synaptic coupling function and its approximation are carried out for comparison. Hardware implementation on field programmable gate array (FPGA) of approximated coupling function shows that the coupled network produces different dynamical behaviors with acceptable error. Hardware implementation shows that the approximated coupling function has significantly lower implementation cost. A spiking neural network based on HR neuron is also shown as a practical application of this coupled HR neural networks. The spiking network successfully encodes and decodes a time varying input.

Book Reviews

Sometimes, people confuse artificial neural networks (ANNs) with real biological brains, at least metaphorically. The section starts with a featured review by Braden Brinkman on the book An Introduction to Modeling Neuronal Dynamics, by Christoph Börgers. This book deals with mathematical models for real biological neurons, and from the review one can conclude that they are much more interesting and also much more complicated than usual ANNs. Braden praises this book as “the latest sign of maturity for the field of mathematical neuroscience” and as “essential for training the next generation of neuroscientists.” The Book Reviews section also offers the other foundation of the usage of ANNs---the mathematical optimization concepts which are fundamental to the practical usage of ANNs. Tim Hoheisel reviews First-Order Methods in Optimization, by Amir Beck, which serves as a reference and includes many MATLAB examples. Like in the previous issue, we have again a review on another disease modeling book. It's the book Modelling Disease Ecology with Mathematics, by Robert Smith? (the question mark is part of the name), reviewed by Chris Bauch. Besides rigorous mathematics, this book contains valuable advice for humans who, facing a zombie apocalypse, must design strategies to counter zombie populations growing through a contagious process. We have five other insightful reviews on different topics. Here, I would like to put particular emphasis on the review of David Goluskin on Exploring ODEs, by Nick Trefethen, Ásgeir Birkisson, and Tobin Driscoll. David recommends this book as a “main text for a first course in ODEs.”

A simple parameter can switch between different weak-noise–induced phenomena in a simple neuron model

In recent years, several, apparently quite different, weak-noise–induced resonance phenomena have been discovered. Here, we show that at least two of them, self-induced stochastic resonance (SISR) and inverse stochastic resonance (ISR), can be related by a simple parameter switch in one of the simplest models, the FitzHugh-Nagumo (FHN) neuron model. We consider a FHN model with a unique fixed point perturbed by synaptic noise. Depending on the stability of this fixed point and whether it is located to either the left or right of the fold point of the critical manifold, two distinct weak-noise–induced phenomena, either SISR or ISR, may emerge. SISR is more robust to parametric perturbations than ISR, and the coherent spike train generated by SISR is more robust than that generated deterministically. ISR also depends on the location of initial conditions and on the time-scale separation parameter of the model equation. Our results could also explain why real biological neurons having similar physiological features and synaptic inputs may encode very different information.

An online supervised learning method based on gradient descent for spiking neurons

The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by precise firing times of spikes. The gradient-descent-based (GDB) learning methods are widely used and verified in the current research. Although the existing GDB multi-spike learning (or spike sequence learning) methods have good performance, they work in an offline manner and still have some limitations. This paper proposes an online GDB spike sequence learning method for spiking neurons that is based on the online adjustment mechanism of real biological neuron synapses. The method constructs error function and calculates the adjustment of synaptic weights as soon as the neurons emit a spike during their running process. We analyze and synthesize desired and actual output spikes to select appropriate input spikes in the calculation of weight adjustment in this paper. The experimental results show that our method obviously improves learning performance compared with the offline learning manner and has certain advantage on learning accuracy compared with other learning methods. Stronger learning ability determines that the method has large pattern storage capacity.

Electrical activity of ON and OFF retinal ganglion cells: a modelling study

Objective. Retinal ganglion cells (RGCs) demonstrate a large range of variation in their ionic channel properties and morphologies. Cell-specific properties are responsible for the unique way RGCs process synaptic inputs, as well as artificial electrical signals such as that from a visual prosthesis. A cell-specific computational modelling approach allows us to examine the functional significance of regional membrane channel expression and cell morphology. Approach. In this study, an existing RGC ionic model was extended by including a hyperpolarization activated non-selective cationic current as well as a T-type calcium current identified in recent experimental findings. Biophysically-defined model parameters were simultaneously optimized against multiple experimental recordings from ON and OFF RGCs. Main results. With well-defined cell-specific model parameters and the incorporation of detailed cell morphologies, these models were able to closely reconstruct and predict ON and OFF RGC response properties recorded experimentally. Significance. The resulting models were used to study the contribution of different ion channel properties and spatial structure of neurons to RGC activation. The techniques of this study are generally applicable to other excitable cell models, increasing the utility of theoretical models in accurately predicting the response of real biological neurons.

Variable Time Delays and Representational Capacity in Sparsely Connected Populations of Spiking Neurons

Successive generations of artificial neural networks have leveraged their multiplicity of connections and weights for significant improvements in information processing capability and memory capacity. The most recent generation of artificial neural networks, third generation networks, consist of spiking neuron models that attempt to mimic the complex dynamic features exhibited by real biological neurons in the hopes of improvements in computational and representational capacities. While the theoretical capabilities of these networks are impressive, understanding the nature and extent of their computational advantages, and the appropriate network architectures and algorithms necessary for their successful exploitation, have lagged far behind the theory. With this in mind, we herein explore the representational capacity of two related forms of neural networks: synfire chains, and polychronic networks. We find that the computational capacity of such cellular assembly based networks increases with the size of between-neural-pool time delays and that for relatively small changes in time delay, linear increases in network representational capacities are obtained.

An Improvement in Power Management in green Computing using Neural Networks

The green computing is the technology which is based on the environmental use of computer related resources. The computer related resources includes processing units, storage units etc. In such type of technology the energy consumption is the main concern. Green computing wills leads to reduce in resource consumption and electronic waste. The new technology of green computing will also include cloud computing services, grid computing services. In the past decades, many techniques have been proposed for the energy conservation in green computing. In this paper, we are proposing new technique for energy conservation in green computing. This novel technique is based on neural networks. The neural network is having capability of learning from the past experiences. The dynamic clustering approach is used with the neural networks for the energy conservation. The proposed technique is implemented in NS2 and simulation results are shown in the graphical form. The green computing is basically a technology which is based on the environmental responsible way to reduce power and environmental waste. In the present time, we are making efforts to make everything greener. This technology is related to reduce or managing impact of IT systems which basically includes materials and resources required for equipment, energy and also materials used in OS, potential health effects on humans from using equipment, and responsibility for the waste products that are created from IT systems. The green technology mainly focuses on super computers and cluster system which harm the green environment badly. The green computing is similar to green chemistry. In the approach of green computing we mainly focus on to design such systems which are having minimum effect to environment. The products supports reduce the use of hazardous materials, maximum energy efficient during the products lifetime and promote the recyclability of defunct products and factory waste. Artificial neural network is composed of interconnecting of artificial neurons. Artificial neural networks may either be used to gain understanding of biological neuron or to solve artificially intelligence problems without creating any model (8). Biological neural networks are made up of real biological neurons that are connected or functionally related in nervous system (wiki).neural network has been motivated from human brain. The brain is highly complex, parallel computer and nonlinear. It has capability to organize its constituent's structure known as neurons to perform complex computations. It is faster than digital computer exit in today's world (8). It is an adaptive in nature that changes its structure based on internal and external information that flows through the networks. Dynamic clustering is an energy efficient algorithm. Energy dissipation of the network can be reduced by using clustering algorithms. The energy consumption of nodes is depends upon the transmission distance, optimal routing protocols and amount of data to be transmitted. In cluster based networks, cluster heads (CH) meets these requirements 1) same adjacent sensors are grouped into a cluster. 2) High energy resources 3) Network should be distributed.

HARDWARE IMPLEMENTATION OF STOCHASTIC SPIKING NEURAL NETWORKS

Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized by its bio-inspired nature and by a higher computational capacity with respect to other neural models. In real biological neurons, stochastic processes represent an important mechanism of neural behavior and are responsible of its special arithmetic capabilities. In this work we present a simple hardware implementation of spiking neurons that considers this probabilistic nature. The advantage of the proposed implementation is that it is fully digital and therefore can be massively implemented in Field Programmable Gate Arrays. The high computational capabilities of the proposed model are demonstrated by the study of both feed-forward and recurrent networks that are able to implement high-speed signal filtering and to solve complex systems of linear equations.

Design of Cortical Neuron Circuits With VLSI Design Approach

A simple CMOS circuitry using very less number of MOSFETs reproduce most of the electrophysiological cortical neuron types and is capable of producing a variety of different behaviors with diversity similar to that of real biological neuron cell. The firing pattern of basic cell classes like regular spiking (RS), chattering (CH), intrinsic bursting (IB) and fast spiking(FS) are obtained with a simple adjustment of only one biasing voltage makes circuit suitable for applications in reconfigurable neuromorphic devices that implement biologically resemble circuit of cortex. This paper discusses spice simulation of the various spiking pattern ability with required and firing frequency of a given cell type. The circuit operation is verified for both conditions-constant input and pulsating input .

Light-induced stochastic resonance in a nanoscale resonant-tunneling diode

Resonant tunneling diodes (RTDs) have been often invoked as a primary nanoelectronic device candidate for cellular neural network physical implementation and for mimicking biological neuronlike behaviors. In this letter we report on the light-induced behavior of trench-etched RTDs capable of undergoing complex stochastic dynamics where electronic noise and light can cooperate for reproducing the stochastic resonance phenomenon previously observed in real biological neurons. The experimental measurements presented here add a missing piece to the quest for the optimal mimicking of bio-neural simulation by improving the functionality and thus the potential role of nanoscale RTDs.

Energy and information in Hodgkin-Huxley neurons

The generation of spikes by neurons is energetically a costly process and the evaluation of the metabolic energy required to maintain the signaling activity of neurons a challenge of practical interest. Neuron models are frequently used to represent the dynamics of real neurons but hardly ever to evaluate the electrochemical energy required to maintain that dynamics. This paper discusses the interpretation of a Hodgkin-Huxley circuit as an energy model for real biological neurons and uses it to evaluate the consumption of metabolic energy in the transmission of information between neurons coupled by electrical synapses, i.e., gap junctions. We show that for a single postsynaptic neuron maximum energy efficiency, measured in bits of mutual information per molecule of adenosine triphosphate (ATP) consumed, requires maximum energy consumption. For groups of parallel postsynaptic neurons we determine values of the synaptic conductance at which the energy efficiency of the transmission presents clear maxima at relatively very low values of metabolic energy consumption. Contrary to what could be expected, the best performance occurs at a low energy cost.

A simple neural autism model based on sensory invariants

Autistic individuals show a variety of curious behavioral and perceptual patterns which generally seem to lack a common underlying cause. We propose a theory of learning and perception which suggests that the general impairment of the autistic brain may be a restriction on the class of connectivity patterns (or `features') that can be utilized for learning perceptual and cognitive tasks. In particular, we argue that the autistic brain does not make proper use of features which pool information over larger areas of the input space, which would allow them to make use of symmetries and develop invariants to permutations. We present preliminary findings with a simple neuronal model which provides useful analogies to psycho-physical experimental findings in support of this hypothesis. Our model consists of a two layer perceptron model. 'Neurons' in the model are connectionist neurons, thus are not meant to represent real biological neurons but rather functional entities that serve our task. In the first layer we have a set of model neurons with fixed random receptive fields. The second neuron layer learns by quadratic optimization to discriminate a noisy “smiley face” from a non-smiling face placed at random locations within the visual field. The only difference between the autistic and non-autistic version of the model is the shape of the receptive fields – autistic neurons have circular receptive fields while non-autistic neurons have horizontal receptive fields. As shown in Figure 2, the non-autistic version is able to solve the discrimination task with a much smaller number of neurons in the first layer. However if the “smiley face” is presented only partly to the model, the autistic version is actually better at the discrimination task then the non-autistic version of the model. This coincides –among other parallels to psycho-physical experiments– with findings in which autistic children show better performance in recognizing faces from parts [1]. Figure 1 Example receptive fields. Left: autistic, Right: non-autistic. Figure 2 Left: Discrimination error (y-axis) versus number of neurons (x-axis) for both non-autistic (full line) and autistic model (dashed). In both types the error reduces with the number of neurons, the non-autistic type shows a better performance. Right: In ...

MAP BASED MODELS IN NEURODYNAMICS

This tutorial reviews a new important class of mathematical phenomenological models of neural activity generated by iterative dynamical systems: the so-called map-based systems. We focus on 1-D and 2-D maps for the replication of many features of the neural activity of a single neuron. It was shown that such systems can reproduce the basic activity modes such as spiking, bursting, chaotic spiking-bursting, subthreshold oscillations, tonic and phasic spiking, normal excitability, etc. of the real biological neurons. We emphasize on the representation of chaotic spiking-bursting oscillations by chaotic attractors of 2-D models. We also explain the dynamical mechanism of formation of such attractors and transition from one mode to another. We briefly present some synchronization mehanisms of chaotic spiking-bursting activity for two coupled neurons described by 1-D maps.

호지킨-헉슬리 모델을 위한 시냅스 기능을 지닌 신경세포 체인의 하드웨어 구현

Integrated circuit of a new neuron chain with a synapse function for Hodgkin-Huxley model which is a good electrical model about a real biological neuron is implemented in a 1 poly 2 metal CMOS technology. Pulse type neuron chain consist of series connected current controlled single neurons through synapses. For the realization of the single neuron, a pair of voltage mode oscillators using operational transconductance amplifiers and capacitors is used. The synapse block which is a connection element between neurons consist of a voltage-current conversion circuit using current mirror. SPICE simulation results of the proposed circuit show 160 mV amplitude pulse output and propagation of the signal through synapses. Measurements of the fabricated pulse type neuron chip in condition of power supply are shown and compared with the simulated results.