Biological Neuronal Networks Research Articles

Long-range temporal correlations in scale-free neuromorphic networks.

Biological neuronal networks are the computing engines of the mammalian brain. These networks exhibit structural characteristics such as hierarchical architectures, small-world attributes, and scale-free topologies, providing the basis for the emergence of rich temporal characteristics such as scale-free dynamics and long-range temporal correlations. Devices that have both the topological and the temporal features of a neuronal network would be a significant step toward constructing a neuromorphic system that can emulate the computational ability and energy efficiency of the human brain. Here we use numerical simulations to show that percolating networks of nanoparticles exhibit structural properties that are reminiscent of biological neuronal networks, and then show experimentally that stimulation of percolating networks by an external voltage stimulus produces temporal dynamics that are self-similar, follow power-law scaling, and exhibit long-range temporal correlations. These results are expected to have important implications for the development of neuromorphic devices, especially for those based on the concept of reservoir computing.

Adaptive control of synchronization for the heterogeneous Hindmarsh-Rose network

Abstract The paper is devoted to the adaptive control problem of synchronization in the network of Hindmarsh-Rose neurons. The case of identical neurons was predominantly considered in the previous studies, which are concerned with the synchronization of the biological neuron models. However, the natural networks of biological neurons are heterogeneous, since living neurons can have different shapes and characteristics. In order to take into account the effect of the natural network heterogeneity, the neuronal models are considered with different parameters in our paper. It was shown that the adaptive synchronization of the heterogeneous Hindmarsh-Rose network is possible, and it is achieved by using the developed controller. The obtained results were mathematically proved and confirmed by the simulations.

Spontaneous Functional Recovery after Focal Damage in Neuronal Cultures.

Damage in biological neuronal networks triggers a complex functional reorganization whose mechanisms are still poorly understood. To delineate this reorganization process, here we investigate the functional alterations of in vitro rat cortical circuits following localized laser ablation. The analysis of the functional network configuration before and after ablation allowed us to quantify the extent of functional alterations and the characteristic spatial and temporal scales along recovery. We observed that damage precipitated a fast rerouting of information flow that restored network’s communicability in about 15 min. Functional restoration was led by the immediate neighbors around trauma but was orchestrated by the entire network. Our in vitro setup exposes the ability of neuronal circuits to articulate fast responses to acute damage, and may serve as a proxy to devise recovery strategies in actual brain circuits. Moreover, this biological setup can become a benchmark to empirically test network theories about the spontaneous recovery in dynamical networks.

Deterministic networks for probabilistic computing

Neuronal network models of high-level brain functions such as memory recall and reasoning often rely on the presence of some form of noise. The majority of these models assumes that each neuron in the functional network is equipped with its own private source of randomness, often in the form of uncorrelated external noise. In vivo, synaptic background input has been suggested to serve as the main source of noise in biological neuronal networks. However, the finiteness of the number of such noise sources constitutes a challenge to this idea. Here, we show that shared-noise correlations resulting from a finite number of independent noise sources can substantially impair the performance of stochastic network models. We demonstrate that this problem is naturally overcome by replacing the ensemble of independent noise sources by a deterministic recurrent neuronal network. By virtue of inhibitory feedback, such networks can generate small residual spatial correlations in their activity which, counter to intuition, suppress the detrimental effect of shared input. We exploit this mechanism to show that a single recurrent network of a few hundred neurons can serve as a natural noise source for a large ensemble of functional networks performing probabilistic computations, each comprising thousands of units.

The Concept of Synapse against Popular Opinion

Emulation of the operation process in the human brain was performed by Artificial Neural Network (ANN). The new comments of this study with the concept of progressive tense like an action to new Comparison ANN with Biological Neuron Network were stated against popular opinions. However, their opinions just pointed out the role of Synapse as the weights in the framework of ANN. In this paper, another concept was better suggested. The role of Synapse should be treated as the weights of ANN, which connect two neurons of two hidden different layers. There was a new proposed opinion in this study when an accurate ANN model was built with optimal weights. The role of Synapse should be both the converting the action potential into electrical energy and chemical energy and synaptic strengthening corresponding to long-term potentiation (LTP) in Biological Neuron Network. From the concept of pharmacology, the action of updating weights with optimal values after training more data, was similar as keeping a normal converting for LTP just using medicaments for resisting some ageing brain diseases e.g. Dementia. The new proposed opinion by comparing both Neural Networks should be reasonable in this study.

Dimensional reduction in networks of non-Markovian spiking neurons: Equivalence of synaptic filtering and heterogeneous propagation delays.

Message passing between components of a distributed physical system is non-instantaneous and contributes to determine the time scales of the emerging collective dynamics. In biological neuron networks this is due in part to local synaptic filtering of exchanged spikes, and in part to the distribution of the axonal transmission delays. How differently these two kinds of communication protocols affect the network dynamics is still an open issue due to the difficulties in dealing with the non-Markovian nature of synaptic transmission. Here, we develop a mean-field dimensional reduction yielding to an effective Markovian dynamics of the population density of the neuronal membrane potential, valid under the hypothesis of small fluctuations of the synaptic current. Within this limit, the resulting theory allows us to prove the formal equivalence between the two transmission mechanisms, holding for any synaptic time scale, integrate-and-fire neuron model, spike emission regimes and for different network states even when the neuron number is finite. The equivalence holds even for larger fluctuations of the synaptic input, if white noise currents are incorporated to model other possible biological features such as ionic channel stochasticity.

Document Informed Neural Autoregressive Topic Models with Distributional Prior

We address two challenges in topic models: (1) Context information around words helps in determining their actual meaning, e.g., “networks” used in the contexts artificial neural networks vs. biological neuron networks. Generative topic models infer topic-word distributions, taking no or only little context into account. Here, we extend a neural autoregressive topic model to exploit the full context information around words in a document in a language modeling fashion. The proposed model is named as iDocNADE. (2) Due to the small number of word occurrences (i.e., lack of context) in short text and data sparsity in a corpus of few documents, the application of topic models is challenging on such texts. Therefore, we propose a simple and efficient way of incorporating external knowledge into neural autoregressive topic models: we use embeddings as a distributional prior. The proposed variants are named as DocNADEe and iDocNADEe. We present novel neural autoregressive topic model variants that consistently outperform state-of-the-art generative topic models in terms of generalization, interpretability (topic coherence) and applicability (retrieval and classification) over 7 long-text and 8 short-text datasets from diverse domains.

VORONOI-BASED SKELETONIZATION ALGORITHM FOR SEGMENTING THE NETWORK OF BIOLOGICAL NEURONS

Context. The problem of automated processing and analysis of microscopy image data is of high relevance due to its extreme impact on the research and recent developments in the field of biology and medicine. Efficient image processing algorithms facilitate the development of new medical diagnostic tools and therapy processes. They help us to broaden our knowledge of underlying mechanisms and processes inside living organisms. The primal focus of this paper is the processing of the microscopy images of the biological neural network. This aims to facilitate further studies of biological neural network that would lead to the development of better methods for diagnosis, prevention and cure of the related deceases. Objective. The goal of the work is to development of an efficient image processing algorithm for segmenting the network of biological neurons based on the fluorescent microscopy image data. Method. The introduced algorithm for segmenting the network of biological neurons comprises several steps. Firstly, we apply image processing routines, which aim to enhance the quality of the image data and extract the contours of the biological neural network. Then we construct the skeleton of the network applying the Voronoi diagram for line segments extracted from the object’s contours. We employ Voronoi skeleton to identify the cellular somas and differentiate them from axons and dendrites. Results. The developed Voronoi-based algorithm allows us to segment individual neurons, localize their somas, axons and dendrites and extract graph representation of the neural network. The underlying Voronoi diagram data structure allows us to compute such graph efficiently in O(N log N) operations (where N is a number of contour points). The proposed segmentation method was implemented in the C++/Python programming language and evaluated on the fluorescent images from CellImageLibrary (CIL). Conclusions. The proposed segmentation method aims to facilitate studies of biological neural networks. It computes segmentation of the network of biological neurons in O(N log N) operations using the Voronoi diagram data structure. This data structure, in turn, gives us an attributed graph representation of the segmented network. Therefore, classical graph processing algorithms can be applied to analyze the neural and compute such network’s characteristics as the number of connections between individual neurons, the shortest signal transduction path between two neurons, etc.

Closed-Loop Systems and In Vitro Neuronal Cultures: Overview and Applications.

One of the main limitations preventing the realization of a successful dialogue between the brain and a putative enabling device is the intricacy of brain signals. In this perspective, closed-loop in vitro systems can be used to investigate the interactions between a network of neurons and an external system, such as an interacting environment or an artificial device. In this chapter, we provide an overview of closed-loop in vitro systems, which have been developed for investigating potential neuroprosthetic applications. In particular, we first explore how to modify or set a target dynamical behavior in a network of neurons. We then analyze the behavior of in vitro systems connected to artificial devices, such as robots. Finally, we provide an overview of biological neuronal networks interacting with artificial neuronal networks, a configuration currently offering a promising solution for clinical applications.

Dendritic and Axonal Propagation Delays May Shape Neuronal Networks With Plastic Synapses.

Biological neuronal networks are highly adaptive and plastic. For instance, spike-timing-dependent plasticity (STDP) is a core mechanism which adapts the synaptic strengths based on the relative timing of pre- and postsynaptic spikes. In various fields of physiology, time delays cause a plethora of biologically relevant dynamical phenomena. However, time delays increase the complexity of model systems together with the computational and theoretical analysis burden. Accordingly, in computational neuronal network studies propagation delays were often neglected. As a downside, a classic STDP rule in oscillatory neurons without propagation delays is unable to give rise to bidirectional synaptic couplings, i.e., loops or uncoupled states. This is at variance with basic experimental results. In this mini review, we focus on recent theoretical studies focusing on how things change in the presence of propagation delays. Realistic propagation delays may lead to the emergence of neuronal activity and synaptic connectivity patterns, which cannot be captured by classic STDP models. In fact, propagation delays determine the inventory of attractor states and shape their basins of attractions. The results reviewed here enable to overcome fundamental discrepancies between theory and experiments. Furthermore, these findings are relevant for the development of therapeutic brain stimulation techniques aiming at shifting the diseased brain to more favorable attractor states.

Organizing Sequential Memory in a Neuromorphic Device Using Dynamic Neural Fields.

Neuromorphic Very Large Scale Integration (VLSI) devices emulate the activation dynamics of biological neuronal networks using either mixed-signal analog/digital or purely digital electronic circuits. Using analog circuits in silicon to physically emulate the functionality of biological neurons and synapses enables faithful modeling of neural and synaptic dynamics at ultra low power consumption in real-time, and thus may serve as computational substrate for a new generation of efficient neural controllers for artificial intelligent systems. Although one of the main advantages of neural networks is their ability to perform on-line learning, only a small number of neuromorphic hardware devices implement this feature on-chip. In this work, we use a reconfigurable on-line learning spiking (ROLLS) neuromorphic processor chip to build a neuronal architecture for sequence learning. The proposed neuronal architecture uses the attractor properties of winner-takes-all (WTA) dynamics to cope with mismatch and noise in the ROLLS analog computing elements, and it uses its on-chip plasticity features to store sequences of states. We demonstrate, with a proof-of-concept feasibility study how this architecture can store, replay, and update sequences of states, induced by external inputs. Controlled by the attractor dynamics and an explicit destabilizing signal, the items in a sequence can last for varying amounts of time and thus reliable sequence learning and replay can be robustly implemented in a real sensorimotor system.

Automatically Selecting a Suitable Integration Scheme for Systems of Differential Equations in Neuron Models

On the level of the spiking activity, the integrate-and-fire neuron is one of the most commonly used descriptions of neural activity. A multitude of variants has been proposed to cope with the huge diversity of behaviors observed in biological nerve cells. The main appeal of this class of model is that it can be defined in terms of a hybrid model, where a set of mathematical equations describes the sub-threshold dynamics of the membrane potential and the generation of action potentials is often only added algorithmically without the shape of spikes being part of the equations. In contrast to more detailed biophysical models, this simple description of neuron models allows the routine simulation of large biological neuronal networks on standard hardware widely available in most laboratories these days. The time evolution of the relevant state variables is usually defined by a small set of ordinary differential equations (ODEs). A small number of evolution schemes for the corresponding systems of ODEs are commonly used for many neuron models, and form the basis of the neuron model implementations built into commonly used simulators like Brian, NEST and NEURON. However, an often neglected problem is that the implemented evolution schemes are only rarely selected through a structured process based on numerical criteria. This practice cannot guarantee accurate and stable solutions for the equations and the actual quality of the solution depends largely on the parametrization of the model. In this article, we give an overview of typical equations and state descriptions for the dynamics of the relevant variables in integrate-and-fire models. We then describe a formal mathematical process to automate the design or selection of a suitable evolution scheme for this large class of models. Finally, we present the reference implementation of our symbolic analysis toolbox for ODEs that can guide modelers during the implementation of custom neuron models.

A single-cell based hybrid neuronal network configured by integration of cell micropatterning and dynamic patch-clamp

We integrate cell micropatterning technology with dynamic clamp electrophysiology to realize a hybrid neuronal network of biological and computational neurons for investigating the effect of neuronal firing properties on the network function. A simple convergent neuronal network unit, consisting of three neurons connected in a two-input one-output architecture, is considered. We first show computationally that the output of the convergent unit changes from a logical AND gate-like state to an OR gate-like state with a minor perturbation in synaptic weight which is physiologically plausible. Then, using a hybrid network of a primary rat hippocampal neuron and model neurons, we demonstrate that this functional tuning can be physically embedded. The nonlinearity of neuronal activation underlies the ability to abruptly switch between the two output states, which is also verified by conductance modulation experiments. Our work demonstrates that dynamic clamp technology extends the constructive approach using cell micropatterning for investigating the cellular mechanisms of signal processing within well-defined neuronal networks.

Firing-rate models for neurons with a broad repertoire of spiking behaviors

Capturing the response behavior of spiking neuron models with rate-based models facilitates the investigation of neuronal networks using powerful methods for rate-based network dynamics. To this end, we investigate the responses of two widely used neuron model types, the Izhikevich and augmented multi-adapative threshold (AMAT) models, to a range of spiking inputs ranging from step responses to natural spike data. We find (i) that linear-nonlinear firing rate models fitted to test data can be used to describe the firing-rate responses of AMAT and Izhikevich spiking neuron models in many cases; (ii) that firing-rate responses are generally too complex to be captured by first-order low-pass filters but require bandpass filters instead; (iii) that linear-nonlinear models capture the response of AMAT models better than of Izhikevich models; (iv) that the wide range of response types evoked by current-injection experiments collapses to few response types when neurons are driven by stationary or sinusoidally modulated Poisson input; and (v) that AMAT and Izhikevich models show different responses to spike input despite identical responses to current injections. Together, these findings suggest that rate-based models of network dynamics may capture a wider range of neuronal response properties by incorporating second-order bandpass filters fitted to responses of spiking model neurons. These models may contribute to bringing rate-based network modeling closer to the reality of biological neuronal networks.

Dependence and Homeostasis of Membrane Impedance on Cell Morphology in Cultured Hippocampal Neurons

The electrical impedance of cell membranes is important for excitable cells, such as neurons, because it strongly influences the amount of membrane potential change upon a flow of ionic current across the membrane. Here, we report on an investigation of how neuronal morphology affects membrane impedance of cultured hippocampal neurons. Microfabricated substrates with patterned scaffolding molecules were used to restrict the neurite growth of hippocampal neurons, and the impedance was measured via whole-cell patch-clamp recording under the inhibition of voltage-dependent ion channels. Membrane impedance was found to depend inversely on the dendrite length and soma area, as would be expected from the fact that its electrical property is equivalent to a parallel RC circuit. Moreover, we found that in biological neurons, the membrane impedance is homeostatically regulated to impede changes in the membrane area. The findings provide direct evidence on cell-autonomous regulation of neuronal impedance and pave the way towards elucidating the mechanism responsible for the resilience of biological neuronal networks.

Application of Artificial Neural Networks for Identification of Catalysts Used in Thermogravimetry Lignocellulosic Biomass

In recent decades there has been a growing need to develop efficient processes in economic and energetic terms for the sustainable production of fuels and chemical products. In this context, it is important to understand the thermal degradation behaviour of different biomass in an inert atmosphere to investigate the breakdown of polymer chains into smaller chains, which could be converted into new products. Catalysts can be used in these breaking processes, since they generate good effects in accelerating the degradation of organic structures and with this increases the yield of bio-oil production. In this work the fiber of crushed and sifted green coconut shell, submitted to the thermogravimetry analysis (TG), was used as biomass. Types of a catalyst were incorporated into the biomass, based on cobalt ferrite, Fe2CoO4. The analysis revealed distinct profiles of mass loss of the green coconut fiber with and without the catalysts. To detect possible instrumentation and/or preparation failures of the experimental samples, models were developed with the help of artificial intelligence. The strategy chosen was the use of artificial neural networks (ANN) feedforward because it is a set of mathematical procedures that seek an intelligent way (inspired in by the network of biological neurons) to manage and analyze systems. The RNA model was developed from the import of the TG data to the MATLAB R2017a software, identifying data inputs/outputs. The inputs were loss of mass (mg), derived from the loss of mass and temperature (°C). The training algorithms were: Levenberg-Marquardt (trainlm) and Levenberg-Marquardt with Bayesian regularization (trainbr) that avoids over-adjustment. The activation functions used were the hyperbolic tangent (tansig) and logistic (logsig) to activate the intermediate layers of the model. The results of the model involving mass loss information were satisfactory to identify the type of catalyst with a topology of 3-6-4-1. The evaluation of the SSE error rates of 0.882 and the MSE of 6.85E-04, confirmed the good fit of the neural network, since the values are close to zero. Thus, the model can be used in conjunction with TG analysis, preventing possible measurement failures.

Brain-inspired computing with resistive switching memory (RRAM): Devices, synapses and neural networks

The human brain can perform advanced computing tasks, such as learning, recognition, and cognition, with extremely low power consumption and low frequency of neuronal spiking. This is attributed to the highly-parallel and the event-driven scheme of computation, where energy is used only when and where it is needed for processing the information. To mimic the human brain, the fundamental challenges are the replication of the time-dependent plasticity of synapses and the achievement of the high connectivity in biological neuron networks, where the ratio between synapses and neurons is around 104. This combination of high computing capability and density scalability can be obtained with the nanodevice technology, notably by resistive-switching memory (RRAM) devices. In this work, the recent advances in RRAM device technology for memory and synaptic applications are reviewed. First, RRAM devices with improved window and reliability thanks to SiOx dielectric layer are discussed. Then, the application of RRAM in neuromorphic computing are addressed, presenting hybrid synapses capable of spike-timing dependent plasticity (STDP). Brain-inspired hardware featuring learning and recognition of input patterns are finally presented.

On electrophysiological signal complexity during biological neuronal network development and maturation.

Developing neuronal populations are assumed to increase their synaptic interactions and generate synchronized activity, such as bursting, during maturation. These effects may arise from increasing interactions of neuronal populations and increasing simultaneous intra-population activity in developing networks. In this paper, we investigated the neuronal network activity and its complexity by means of self-similarity during neuronal network development. We studied the phenomena using computational neuronal network models and actual in vitro microelectrode array data measured from a developing neuronal network of dissociated mouse cortical neurons. To achieve this, we assessed the spiking and bursting characteristics of the networks, and computed the signal complexity with Sample Entropy. The results show that we can relate increasing simultaneous activity in a neuronal population with decreasing entropy, and track the network development and maturation using this. We can conclude that the complexity of neuronal network signals decreases during the maturation. This can emerge from the fact that as networks mature, they exhibit more synchronous activity, thus decreasing the complexity of its signaling. However, increasing the number of interacting populations has lesser effect on the signal complexity. The entropy based measure provides a tool to assess the complexity of the neuronal network activity, and can be useful in the assessment of developing networks or the effects of drugs and toxins on their functioning.

Biomimetic neural network for modifying biological dynamics during hybrid experiments

Electrical stimulation of nerve tissue and recording of neural electrical activity are the basis of emerging prostheses and treatments for many neurological disorders. Here we present closed-loop bio-hybrid experiment using in vitro biological neuronal network (BNN) with an artificial neural network (ANN) implemented in a neuromorphic board. We adopted a neuromorphic board which is able to perform real-time event detection and trigger an electrical stimulation of the BNN. This system embeds an ANN, based on Izhikevich neurons which can be put in uni- and bi-directional communication with the BNN. The ANN used in the following experiments was made up of 20 excitatory neurons with inhibition synapse and with synaptic plasticity to design central pattern generator. Open-loop and closed-loop hybrid experiments show that the biological dynamics can be modified. This work can be seen as the first step towards the realization of an innovative neuroprosthesis.

An FPGA Platform for Real-Time Simulation of Spiking Neuronal Networks.

In the last years, the idea to dynamically interface biological neurons with artificial ones has become more and more urgent. The reason is essentially due to the design of innovative neuroprostheses where biological cell assemblies of the brain can be substituted by artificial ones. For closed-loop experiments with biological neuronal networks interfaced with in silico modeled networks, several technological challenges need to be faced, from the low-level interfacing between the living tissue and the computational model to the implementation of the latter in a suitable form for real-time processing. Field programmable gate arrays (FPGAs) can improve flexibility when simple neuronal models are required, obtaining good accuracy, real-time performance, and the possibility to create a hybrid system without any custom hardware, just programming the hardware to achieve the required functionality. In this paper, this possibility is explored presenting a modular and efficient FPGA design of an in silico spiking neural network exploiting the Izhikevich model. The proposed system, prototypically implemented on a Xilinx Virtex 6 device, is able to simulate a fully connected network counting up to 1,440 neurons, in real-time, at a sampling rate of 10 kHz, which is reasonable for small to medium scale extra-cellular closed-loop experiments.