Digital implementation of a biological astrocyte model and its application

Situation-Based Neuromorphic Memory in Spiking Neuron-Astrocyte Network

Mammalian brains operate in very special surroundings: to survive they have to react quickly and effectively to the pool of stimuli patterns previously recognized as danger. Many learning tasks often encountered by living organisms involve a specific set-up centered around a relatively small set of patterns presented in a particular environment. For example, at a party, people recognize friends immediately, without deep analysis, just by seeing a fragment of their clothes. This set-up with reduced "ontology" is referred to as a "situation." Situations are usually local in space and time. In this work, we propose that neuron-astrocyte networks provide a network topology that is effectively adapted to accommodate situation-based memory. In order to illustrate this, we numerically simulate and analyze a well-established model of a neuron-astrocyte network, which is subjected to stimuli conforming to the situation-driven environment. Three pools of stimuli patterns are considered: external patterns, patterns from the situation associative pool regularly presented to the network and learned by the network, and patterns already learned and remembered by astrocytes. Patterns from the external world are added to and removed from the associative pool. Then, we show that astrocytes are structurally necessary for an effective function in such a learning and testing set-up. To demonstrate this we present a novel neuromorphic computational model for short-term memory implemented by a two-net spiking neural-astrocytic network. Our results show that such a system tested on synthesized data with selective astrocyte-induced modulation of neuronal activity provides an enhancement of retrieval quality in comparison to standard spiking neural networks trained via Hebbian plasticity only. We argue that the proposed set-up may offer a new way to analyze, model, and understand neuromorphic artificial intelligence systems.

Computational Modeling of Ganglion Cell Bicolor Opponent Receptive Fields and FPGA Adaptation for Parallel Arrays

The biological system is not a perfect system, but it is a relatively complete system. It is difficult to realize the lower power consumption and high parallelism that characterize biological systems if lower-level information pathways are ignored. In this paper, we focus on the K, M and P pathways of visual signal processing from the retina to the lateral geniculate nucleus (LGN). We model the visual system at a fine-grained level to ensure efficient information transmission while minimizing energy use. We also implement a circuit-level distributed parallel computing model on FPGAs. The results show that we are able to transfer information with low energy consumption and high parallelism. The Artix-7 family of xc7a200tsbv484-1 FPGAs can reach a maximum frequency of 200 MHz and a maximum parallelism of 600, and a single receptive field model consumes only 0.142 W of power. This can be useful for building assistive vision systems for small and light devices.

High-Matching and Low-Cost Realization of the FHN Neuron Model on Reconfigurable FPGA Board

The main objectives of neuromorphic engineering are the research, modeling, and implementation of neural functioning in the human brain. We provide a hardware solution that can replicate such a nature-inspired system by merging multiple scientific domains and is based on neural cell processes. This work provides a modified version of the original Fitz-Hugh Nagumo (FHN) neuron using a simple 2V term called Hybrid Piece-Wised Base-2 Model (HPWBM), which accurately reproduces numerous patterns of the original neuron model. With reduced terms, we suggest modifying the original nonlinear term to achieve high matching accuracy and little computing error. Time domain and phase portraits are used to validate the proposed model, which shows that it can reproduce all of the FHN model's properties with high accuracy and little mistake. We provide an effective digital hardware approach for large-scale neuron implementations based on resource-sharing and pipelining strategies. The Hardware Description Language (HDL) is used to construct the hardware on an FPGA as a proof of concept. The recommended model hardly uses 0.48 percent of the resources on a Virtex 4 FPGA board, according to the results of the hardware implementation. The circuit can run at a maximum frequency of 448.236 MHz, according to the static timing study.

Analysis of Network Models with Neuron-Astrocyte Interactions

Neural networks, composed of many neurons and governed by complex interactions between them, are a widely accepted formalism for modeling and exploring global dynamics and emergent properties in brain systems. In the past decades, experimental evidence of computationally relevant neuron-astrocyte interactions, as well as the astrocytic modulation of global neural dynamics, have accumulated. These findings motivated advances in computational glioscience and inspired several models integrating mechanisms of neuron-astrocyte interactions into the standard neural network formalism. These models were developed to study, for example, synchronization, information transfer, synaptic plasticity, and hyperexcitability, as well as classification tasks and hardware implementations. We here focus on network models of at least two neurons interacting bidirectionally with at least two astrocytes that include explicitly modeled astrocytic calcium dynamics. In this study, we analyze the evolution of these models and the biophysical, biochemical, cellular, and network mechanisms used to construct them. Based on our analysis, we propose how to systematically describe and categorize interaction schemes between cells in neuron-astrocyte networks. We additionally study the models in view of the existing experimental data and present future perspectives. Our analysis is an important first step towards understanding astrocytic contribution to brain functions. However, more advances are needed to collect comprehensive data about astrocyte morphology and physiology in vivo and to better integrate them in data-driven computational models. Broadening the discussion about theoretical approaches and expanding the computational tools is necessary to better understand astrocytes' roles in brain functions.

Investigation on the Wilson Neuronal Model: Optimized Approximation and Digital Multiplierless Implementation

Neuromorphic engineering is an essential science field which incorporates the basic aspects of issues together such as: physics, mathematics, electronics, etc. The primary block in the Central Nervous System (CNS) is neurons that have functional roles such as: receiving, processing, and transmitting data in the brain. This paper presents Wilson Multiplierless Neuron (WMN) model which is a modified version of the original model. This model uses power-2 based functions, Look-Up Table (LUT) approach and shifters to apply a multiplierless digital realization leads to overhead costs reduction and increases in the final system frequency. The proposed model specifically follows the original neuron model in case of spiking patterns and also dynamical pathways. To validate the proposed model in digital hardware implementation, the FPGA board (Xilinx Virtex II XC2VP30) can be used. Hardware results show the increasing in the system frequency compared with the original model and other similar papers. Numerical results demonstrate that the proposed system speed-up is 210 MHz that is higher than the original one, 85 MHz. Additionally, the overall saving in FPGA resources for the proposed model is 96.86 % that is more than the original model, 95.13 %. From case study viewpoint for CNS consideration, a network consisting of Wilson neurons, synapses, and astrocytes have been considered to test the controlling effects on LTP and LTD processes for investigating the neuronal diseases (medical approaches) such as Epilepsy.

An Optimization on the Neuronal Networks Based on the ADEX Biological Model in Terms of LUT-State Behaviors: Digital Design and Realization on FPGA Platforms

Design and implementation of biological neural networks is a vital research field in the neuromorphic engineering. This paper presents LUT-based modeling of the Adaptive Exponential integrate-and-fire (ADEX) model using Nyquist frequency method. In this approach, a continuous term is converted to a discrete term by sampling factor. This new modeling is called N-LUT-ADEX (Nyquist-Look Up Table-ADEX) and is based on accurate sampling of the original ADEX model. Since in this modeling, the high-accuracy matching is achieved, it can exactly reproduce the spiking patterns, which have the same behaviors of the original neuron model. To confirm the N-LUT-ADEX neuron, the proposed model is realized on Virtex-II Field-Programmable Gate Array (FPGA) board for validating the final hardware. Hardware implementation results show the high degree of similarity between the proposed and original models. Furthermore, low-cost and high-speed attributes of our proposed neuron model will be validated. Indeed, the proposed model is capable of reproducing the spiking patterns in terms of low overhead costs and higher frequencies in comparison with the original one. The properties of the proposed model cause can make it a suitable choice for neuromorphic network implementations with reduced-cost attributes.

Memristive Circuit Implementation of a Self-Repairing Network Based on Biological Astrocytes in Robot Application

A large number of studies have shown that astrocytes can be combined with the presynaptic terminals and postsynaptic spines of neurons to constitute a triple synapse via an endocannabinoid retrograde messenger to achieve a self-repair ability in the human brain. Inspired by the biological self-repair mechanism of astrocytes, this work proposes a self-repairing neuron network circuit that utilizes a memristor to simulate changes in neurotransmitters when a set threshold is reached. The proposed circuit simulates an astrocyte-neuron network and comprises the following: 1) a single-astrocyte-neuron circuit module; 2) an astrocyte-neuron network circuit; 3) a module to detect malfunctions; and 4) a neuron PR (release probability of synaptic transmission) enhancement module. When faults occur in a synapse, the neuron module becomes silent or near silent because of the low PR of the synapses. The circuit can detect faults automatically. The damaged neuron can be repaired by enhancing the PR of other healthy neurons, analogous to the biological repair mechanism of astrocytes. This mechanism helps to repair the damaged circuit. A simulation of the circuit revealed the following: 1) as the number of neurons in the circuit increases, the self-repair ability strengthens and 2) as the number of damaged neurons in the astrocyte-neuron network increases, the self-repair ability weakens, and there is a significant degradation in the performance of the circuit. The self-repairing circuit was used for a robot, and it effectively improved the robots' performance and reliability.

Central Nervous System: Overall Considerations Based on Hardware Realization of Digital Spiking Silicon Neurons (DSSNs) and Synaptic Coupling

The Central Nervous System (CNS) is the part of the nervous system including the brain and spinal cord. The CNS is so named because the brain integrates the received information and influences the activity of different sections of the bodies. The basic elements of this important organ are: neurons, synapses, and glias. Neuronal modeling approach and hardware realization design for the nervous system of the brain is an important issue in the case of reproducing the same biological neuronal behaviors. This work applies a quadratic-based modeling called Digital Spiking Silicon Neuron (DSSN) to propose a modified version of the neuronal model which is capable of imitating the basic behaviors of the original model. The proposed neuron is modeled based on the primary hyperbolic functions, which can be realized in high correlation state with the main model (original one). Really, if the high-cost terms of the original model, and its functions were removed, a low-error and high-performance (in case of frequency and speed-up) new model will be extracted compared to the original model. For testing and validating the new model in hardware state, Xilinx Spartan-3 FPGA board has been considered and used. Hardware results show the high-degree of similarity between the original and proposed models (in terms of neuronal behaviors) and also higher frequency and low-cost condition have been achieved. The implementation results show that the overall saving is more than other papers and also the original model. Moreover, frequency of the proposed neuronal model is about 168 MHz, which is significantly higher than the original model frequency, 63 MHz.

An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach

The main required organ of the biological system is the Central Nervous System (CNS), which can influence the other basic organs in the human body. The basic elements of this important organ are neurons, synapses, and glias (such as astrocytes, which are the highest percentage of glias in the human brain). Investigating, modeling, simulation, and hardware implementation (realization) of different parts of the CNS are important in case of achieving a comprehensive neuronal system that is capable of emulating all aspects of the real nervous system. This paper uses a basic neuron model called the Izhikevich neuronal model to achieve a high copy of the primary nervous block, which is capable of regenerating the behaviors of the human brain. The proposed approach can regenerate all aspects of the Izhikevich neuron in high similarity degree and performances. The new model is based on Look-Up Table (LUT) modeling of the mathematical neuromorphic systems, which can be realized in a high degree of correlation with the original model. The proposed procedure is considered in three cases: 100 points LUT modeling, 1000 points LUT modeling, and 10,000 points LUT modeling. Indeed, by removing the high-cost functions in the original model, the presented model can be implemented in a low-error, high-speed, and low-area resources state in comparison with the original system. To test and validate the proposed final hardware, a digital FPGA board (Xilinx Virtex-II FPGA board) is used. Digital hardware synthesis illustrates that our presented approach can follow the Izhikevich neuron in a high-speed state (more than the original model), increase efficiency, and also reduce overhead costs. Implementation results show the overall saving of 84.30% in FPGA and also the higher frequency of the proposed model of about 264 MHz, which is significantly higher than the original model, 28 MHz.

Astrocytes mediate analogous memory in a multi-layer neuron–astrocyte network

Modeling the neuronal processes underlying short-term working memory remains the focus of many theoretical studies in neuroscience. In this paper, we propose a mathematical model of a spiking neural network (SNN) which simulates the way a fragment of information is maintained as a robust activity pattern for several seconds and the way it completely disappears if no other stimuli are fed to the system. Such short-term memory traces are preserved due to the activation of astrocytes accompanying the SNN. The astrocytes exhibit calcium transients at a time scale of seconds. These transients further modulate the efficiency of synaptic transmission and, hence, the firing rate of neighboring neurons at diverse timescales through gliotransmitter release. We demonstrate how such transients continuously encode frequencies of neuronal discharges and provide robust short-term storage of analogous information. This kind of short-term memory can store relevant information for seconds and then completely forget it to avoid overlapping with forthcoming patterns. The SNN is inter-connected with the astrocytic layer by local inter-cellular diffusive connections. The astrocytes are activated only when the neighboring neurons fire synchronously, e.g., when an information pattern is loaded. For illustration, we took grayscale photographs of people’s faces where the shades of gray correspond to the level of applied current which stimulates the neurons. The astrocyte feedback modulates (facilitates) synaptic transmission by varying the frequency of neuronal firing. We show how arbitrary patterns can be loaded, then stored for a certain interval of time, and retrieved if the appropriate clue pattern is applied to the input.

CORDIC-Astrocyte: Tripartite Glutamate-IP3-Ca 2+ Interaction Dynamics on FPGA

Real-time, large-scale simulation of biological systems is challenging due to different types of nonlinear functions describing biochemical reactions in the cells. The promise of the high speed, cost effectiveness, and power efficiency in addition to parallel processing has made application-specific hardware an attractive simulation platform. This paper proposes high-speed and low-cost digital hardware to emulate a biological-plausible astrocyte and glutamate-release mechanism. The nonlinear terms of these models were calculated using a high-precision and cost-effective algorithm. Subsequently, the modified models were simulated to study and validate their functions. We developed several hardware versions by setting different constraints to investigate trade-offs and find the best possible design. FPGA implementation results confirmed the ability of the design to emulate biological cell behaviours in detail with high accuracy. As for performance, the proposed design turned out to be faster and more efficient than previously published works that targeted digital hardware for biological-plausible astrocytes.

A Neuromorphic Digital Circuit for Neuronal Information Encoding Using Astrocytic Calcium Oscillations

Neurophysiological observations are clarifying how astrocytes can actively participate in information processing and how they can encode information through frequency and amplitude modulation of intracellular Ca²⁺ signals. Consequently, hardware realization of astrocytes is important for developing the next generation of bio-inspired computing systems. In this paper, astrocytic calcium oscillations and neuronal firing dynamics are presented by De Pittà and IF (Integrated & Fire) models, respectively. Considering highly nonlinear equations of the astrocyte model, linear approximation and single constant multiplication (SCM) techniques are employed for efficient hardware execution while maintaining the dynamic of the original models. This low-cost hardware architecture for the astrocyte model is able to show the essential features of different types of Ca²⁺ modulation such as amplitude modulation (AM), frequency modulation (FM), or both modes (AFM). To show good agreement between the results of original models simulated in MATLAB and the proposed digital circuits executed on FPGA, quantitative, and qualitative analyses including phase plane are done. This new neuromorphic circuit of astrocyte is able to successfully demonstrate AM/FM/AFM calcium signaling in its real operation on FPGA and has applications in self-repairing systems. It also can be employed as a subsystem for linking biological cells to artificial neuronal networks using astrocytic calcium oscillations in future research.

Multiplierless Implementation of Noisy Izhikevich Neuron With Low-Cost Digital Design

Fast speed and a high accuracy implementation of biological plausible neural networks are vital key objectives to achieve new solutions to model, simulate and cure the brain diseases. Efficient hardware implementation of spiking neural networks is a significant approach in biological neural networks. This paper presents a multiplierless noisy Izhikevich neuron (MNIN) model, which is used for the digital implementation of biological neural networks in large scale. Simulation results show that the MNIN model reproduces the same operations of the original noisy Izhikevich neuron. The proposed model has a low-cost hardware implementation property compared with the original neuron model. The field-programmable gate array realization results demonstrated that the MNIN model follows the different spiking patterns appropriately.

Effect of dietary incorporation of silkworm pupae meal on in vitro rumen fermentation and digestibility

This experiment was conducted to study the effect of supplementation of different inclusion levels of defatted silkworm pupae meal (DSWP) on in vitro rumen fermentation and digestibility. Eleven concentrate mixtures were formulated with graded levels of DSWP by replacing 0 (T0), 10 (T1), 20 (T2), 30 (T3), 40 (T4), 50 (T5), 60 (T6), 70, (T7), 80 (T8), 90 (T9) and 100% (T10) of soybean meal (SBM) protein of the control concentrate mixture. In vitro experiments were conducted to study the effect of supplementation of different levels of DSWP in the concentrate mixture on finger millet straw (FMS) based diets (30:70). In vitro total gas production (IVTGP), pH, ammonia nitrogen (NH3-N), total volatile fatty acids (TVFA), partitioning factor (PF), microbial biomass production (MBB), metabolizable energy (ME), in vitro dry matter digestibility (IVDMD) and in vitro organic matter digestibility (IVOMD) were determined. No significant difference was observed in pH, NH3-N, TVFA, PF, MBB, ME, IVDMD and IVOMD among treatments (T0 to T10). The results indicated that DSWP can be safely incorporated in the concentrate mixture by replacing 100% conventional protein without affecting the rumen fermentation and digestibility. Hence, it was concluded that supplementation of DSWP up to 100% had no significant effect on in vitro rumen fermentation and digestibility on FMS based ration.

Computational Models for Calcium-Mediated Astrocyte Functions

The computational neuroscience field has heavily concentrated on the modeling of neuronal functions, largely ignoring other brain cells, including one type of glial cell, the astrocytes. Despite the short history of modeling astrocytic functions, we were delighted about the hundreds of models developed so far to study the role of astrocytes, most often in calcium dynamics, synchronization, information transfer, and plasticity in vitro, but also in vascular events, hyperexcitability, and homeostasis. Our goal here is to present the state-of-the-art in computational modeling of astrocytes in order to facilitate better understanding of the functions and dynamics of astrocytes in the brain. Due to the large number of models, we concentrated on a hundred models that include biophysical descriptions for calcium signaling and dynamics in astrocytes. We categorized the models into four groups: single astrocyte models, astrocyte network models, neuron-astrocyte synapse models, and neuron-astrocyte network models to ease their use in future modeling projects. We characterized the models based on which earlier models were used for building the models and which type of biological entities were described in the astrocyte models. Features of the models were compared and contrasted so that similarities and differences were more readily apparent. We discovered that most of the models were basically generated from a small set of previously published models with small variations. However, neither citations to all the previous models with similar core structure nor explanations of what was built on top of the previous models were provided, which made it possible, in some cases, to have the same models published several times without an explicit intention to make new predictions about the roles of astrocytes in brain functions. Furthermore, only a few of the models are available online which makes it difficult to reproduce the simulation results and further develop the models. Thus, we would like to emphasize that only via reproducible research are we able to build better computational models for astrocytes, which truly advance science. Our study is the first to characterize in detail the biophysical and biochemical mechanisms that have been modeled for astrocytes.

SPANNER: A Self-Repairing Spiking Neural Network Hardware Architecture

Recent research has shown that a glial cell of astrocyte underpins a self-repair mechanism in the human brain, where spiking neurons provide direct and indirect feedbacks to presynaptic terminals. These feedbacks modulate the synaptic transmission probability of release (PR). When synaptic faults occur, the neuron becomes silent or near silent due to the low PR of synapses; whereby the PRs of remaining healthy synapses are then increased by the indirect feedback from the astrocyte cell. In this paper, a novel hardware architecture of Self-rePAiring spiking Neural NEtwoRk (SPANNER) is proposed, which mimics this self-repairing capability in the human brain. This paper demonstrates that the hardware can self-detect and self-repair synaptic faults without the conventional components for the fault detection and fault repairing. Experimental results show that SPANNER can maintain the system performance with fault densities of up to 40%, and more importantly SPANNER has only a 20% performance degradation when the self-repairing architecture is significantly damaged at a fault density of 80%.

Complete Neuron-Astrocyte Interaction Model: Digital Multiplierless Design and Networking Mechanism

Glial cells, also known as neuroglia or glia, are non-neuronal cells providing support and protection for neurons in the central nervous system (CNS). They also act as supportive cells in the brain. Among a variety of glial cells, the star-shaped glial cells, i.e., astrocytes, are the largest cell population in the brain. The important role of astrocyte such as neuronal synchronization, synaptic information regulation, feedback to neural activity and extracellular regulation make the astrocytes play a vital role in brain disease. This paper presents a modified complete neuron-astrocyte interaction model that is more suitable for efficient and large scale biological neural network realization on digital platforms. Simulation results show that the modified complete interaction model can reproduce biological-like behavior of the original neuron-astrocyte mechanism. The modified interaction model is investigated in terms of digital realization feasibility and cost targeting a low cost hardware implementation. Networking behavior of this interaction is investigated and compared between two cases: i) the neuron spiking mechanism without astrocyte effects, and ii) the effect of astrocyte in regulating the neurons behavior and synaptic transmission via controlling the LTP and LTD processes. Hardware implementation on FPGA shows that the modified model mimics the main mechanism of neuron-astrocyte communication with higher performance and considerably lower hardware overhead cost compared with the original interaction model.

Human astrocytes are distinct contributors to the complexity of synaptic function

Cellular components of synaptic circuits have been adjusted for increased human brain size, neural cell density, energy consumption and developmental duration. How does the human brain make these accommodations? There is evidence that astrocytes are one of the most divergent neural cell types in primate brain evolution and it is now becoming clear that they have critical roles in controlling synaptic development, function and plasticity. Yet, we still do not know how the precise developmental appearance of these cells and subsequent astrocyte-derived signals modulate diverse neuronal circuit subtypes. Here, we discuss what is currently known about the influence of glial factors on synaptic maturation and focus on unique features of human astrocytes including their potential roles in regenerative and translational medicine. Human astrocyte distinctiveness may be a major contributor to high level neuronal processing of the human brain and act in novel ways during various neuropathies ranging from autism spectrum disorders, viral infection, injury and neurodegenerative conditions.

A Digital Realization of Astrocyte and Neural Glial Interactions

The implementation of biological neural networks is a key objective of the neuromorphic research field. Astrocytes are the largest cell population in the brain. With the discovery of calcium wave propagation through astrocyte networks, now it is more evident that neuronal networks alone may not explain functionality of the strongest natural computer, the brain. Models of cortical function must now account for astrocyte activities as well as their relationships with neurons in encoding and manipulation of sensory information. From an engineering viewpoint, astrocytes provide feedback to both presynaptic and postsynaptic neurons to regulate their signaling behaviors. This paper presents a modified neural glial interaction model that allows a convenient digital implementation. This model can reproduce relevant biological astrocyte behaviors, which provide appropriate feedback control in regulating neuronal activities in the central nervous system (CNS). Accordingly, we investigate the feasibility of a digital implementation for a single astrocyte constructed by connecting a two coupled FitzHugh Nagumo (FHN) neuron model to an implementation of the proposed astrocyte model using neuron-astrocyte interactions. Hardware synthesis, physical implementation on FPGA, and theoretical analysis confirm that the proposed neuron astrocyte model, with significantly low hardware cost, can mimic biological behavior such as the regulation of postsynaptic neuron activity and the synaptic transmission mechanisms.

Generalized reconfigurable memristive dynamical system (MDS) for neuromorphic applications

This study firstly presents (i) a novel general cellular mapping scheme for two dimensional neuromorphic dynamical systems such as bio-inspired neuron models, and (ii) an efficient mixed analog-digital circuit, which can be conveniently implemented on a hybrid memristor-crossbar/CMOS platform, for hardware implementation of the scheme. This approach employs 4n memristors and no switch for implementing an n-cell system in comparison with 2n² memristors and 2n switches of a Cellular Memristive Dynamical System (CMDS). Moreover, this approach allows for dynamical variables with both analog and one-hot digital values opening a wide range of choices for interconnections and networking schemes. Dynamical response analyses show that this circuit exhibits various responses based on the underlying bifurcation scenarios which determine the main characteristics of the neuromorphic dynamical systems. Due to high programmability of the circuit, it can be applied to a variety of learning systems, real-time applications, and analytically indescribable dynamical systems. We simulate the FitzHugh-Nagumo (FHN), Adaptive Exponential (AdEx) integrate and fire, and Izhikevich neuron models on our platform, and investigate the dynamical behaviors of these circuits as case studies. Moreover, error analysis shows that our approach is suitably accurate. We also develop a simple hardware prototype for experimental demonstration of our approach.