Dopamine-Induced Dispersion of Correlations Between Action Potentials in Networks of Cortical Neurons

Human-Derived Cortical Neurospheroids Coupled to Passive, High-Density and 3D MEAs: A Valid Platform for Functional Tests

With the advent of human-induced pluripotent stem cells (hiPSCs) and differentiation protocols, methods to create in-vitro human-derived neuronal networks have been proposed. Although monolayer cultures represent a valid model, adding three-dimensionality (3D) would make them more representative of an in-vivo environment. Thus, human-derived 3D structures are becoming increasingly used for in-vitro disease modeling. Achieving control over the final cell composition and investigating the exhibited electrophysiological activity is still a challenge. Thence, methodologies to create 3D structures with controlled cellular density and composition and platforms capable of measuring and characterizing the functional aspects of these samples are needed. Here, we propose a method to rapidly generate neurospheroids of human origin with control over cell composition that can be used for functional investigations. We show a characterization of the electrophysiological activity exhibited by the neurospheroids by using micro-electrode arrays (MEAs) with different types (i.e., passive, C-MOS, and 3D) and number of electrodes. Neurospheroids grown in free culture and transferred on MEAs exhibited functional activity that can be chemically and electrically modulated. Our results indicate that this model holds great potential for an in-depth study of signal transmission to drug screening and disease modeling and offers a platform for in-vitro functional testing.

The effect of pharmacological inhibition of Serine Proteases on neuronal networks in vitro

Neurons are embedded in an extracellular matrix (ECM), which functions both as a scaffold and as a regulator of neuronal function. The ECM is in turn dynamically altered through the action of serine proteases, which break down its constituents. This pathway has been implicated in the regulation of synaptic plasticity and of neuronal intrinsic excitability. In this study, we determined the short-term effects of interfering with proteolytic processes in the ECM, with a newly developed serine protease inhibitor. We monitored the spontaneous electrophysiological activity of in vitro primary rat cortical cultures, using microelectrode arrays. While pharmacological inhibition at a low dosage had no significant effect, at elevated concentrations it altered significantly network synchronization and functional connectivity but left unaltered single-cell electrical properties. These results suggest that serine protease inhibition affects synaptic properties, likely through its actions on the ECM.

Inhibition increases response variability and reduces stimulus discrimination in random networks of cortical neurons

Much of what is known about the contribution of inhibition to stimulus discrimination is due to extensively studied sensory systems, which are highly structured neural circuits. The effect of inhibition on stimulus representation in less structured networks is not as clear. Here we exercise a biosynthetic approach in order to study the impacts of inhibition on stimulus representation in non-specialized network anatomy. Combining pharmacological manipulation, multisite electrical stimulation and recording from ex-vivo randomly rewired networks of cortical neurons, we quantified the effects of inhibition on response variability and stimulus discrimination at the population and single unit levels. We find that blocking inhibition quenches variability of responses evoked by repeated stimuli and enhances discrimination between stimuli that invade the network from different spatial loci. Enhanced stimulus discrimination is reserved for representation schemes that are based on temporal relation between spikes emitted in groups of neurons. Our data indicate that - under intact inhibition - the response to a given stimulus is a noisy version of the response evoked in the absence of inhibition. Spatial analysis suggests that the dispersion effect of inhibition is due to disruption of an otherwise coherent, wave-like propagation of activity.

SPICODYN: A Toolbox for the Analysis of Neuronal Network Dynamics and Connectivity from Multi-Site Spike Signal Recordings

We implemented an automated and efficient open-source software for the analysis of multi-site neuronal spike signals. The software package, named SPICODYN, has been developed as a standalone windows GUI application, using C# programming language with Microsoft Visual Studio based on .NET framework 4.5 development environment. Accepted input data formats are HDF5, level 5 MAT and text files, containing recorded or generated time series spike signals data. SPICODYN processes such electrophysiological signals focusing on: spiking and bursting dynamics and functional-effective connectivity analysis. In particular, for inferring network connectivity, a new implementation of the transfer entropy method is presented dealing with multiple time delays (temporal extension) and with multiple binary patterns (high order extension). SPICODYN is specifically tailored to process data coming from different Multi-Electrode Arrays setups, guarantying, in those specific cases, automated processing. The optimized implementation of the Delayed Transfer Entropy and the High-Order Transfer Entropy algorithms, allows performing accurate and rapid analysis on multiple spike trains from thousands of electrodes.

Identification of excitatory-inhibitory links and network topology in large-scale neuronal assemblies from multi-electrode recordings

Functional-effective connectivity and network topology are nowadays key issues for studying brain physiological functions and pathologies. Inferring neuronal connectivity from electrophysiological recordings presents open challenges and unsolved problems. In this work, we present a cross-correlation based method for reliably estimating not only excitatory but also inhibitory links, by analyzing multi-unit spike activity from large-scale neuronal networks. The method is validated by means of realistic simulations of large-scale neuronal populations. New results related to functional connectivity estimation and network topology identification obtained by experimental electrophysiological recordings from high-density and large-scale (i.e., 4096 electrodes) microtransducer arrays coupled to in vitro neural populations are presented. Specifically, we show that: (i) functional inhibitory connections are accurately identified in in vitro cortical networks, providing that a reasonable firing rate and recording length are achieved; (ii) small-world topology, with scale-free and rich-club features are reliably obtained, on condition that a minimum number of active recording sites are available. The method and procedure can be directly extended and applied to in vivo multi-units brain activity recordings.

The balance between excitation and inhibition is fundamental for proper brain functions and for this reason is precisely regulated in adult cortices. Impaired excitation/inhibition balance is often associated with several neurological disorders, such as epilepsy, autism and schizophrenia. However, estimating functional inhibitory connections is not an easy task and few methods are available to identify such connections from electrophysiological data. Here we present a cross-correlation based method to identify both excitatory and inhibitory functional connections in large-scale neuronal networks. The method is applicable to both in vitro and in vivo spike data recordings. Once a connectivity map (i.e. a graph) is obtained, we characterized the associated topology by means of classical graph theory metrics to unveil functional architecture. In this work, we analyze in vitro cortical networks probed by means of large-scale microelectrode arrays (i.e., 4096 sensors) and we derive network topologies from spike data. The functional organization found is called “small-world and scale-free” and is the same organization found in cortical in vivo brain regions by means of different experimental methods. We also show that to obtain reliable information about network architecture at least a network with a hundred of nodes-neurons is needed.

Restoration of Kv7 Channel-Mediated Inhibition Reduces Cued-Reinstatement of Cocaine Seeking

Cocaine addicts display increased sensitivity to drug-associated cues, due in part to changes in the prelimbic prefrontal cortex (PL-PFC). The cellular mechanisms underlying cue-induced reinstatement of cocaine seeking remain unknown. Reinforcement learning for addictive drugs may produce persistent maladaptations in intrinsic excitability within sparse subsets of PFC pyramidal neurons. Using a model of relapse in male rats, we sampled >600 neurons to examine spike frequency adaptation (SFA) and afterhyperpolarizations (AHPs), two systems that attenuate low-frequency inputs to regulate neuronal synchronization. We observed that training to self-administer cocaine or nondrug (sucrose) reinforcers decreased SFA and AHPs in a subpopulation of PL-PFC neurons. Only with cocaine did the resulting hyperexcitability persist through extinction training and increase during reinstatement. In neurons with intact SFA, dopamine enhanced excitability by inhibiting Kv7 potassium channels that mediate SFA. However, dopamine effects were occluded in neurons from cocaine-experienced rats, where SFA and AHPs were reduced. Pharmacological stabilization of Kv7 channels with retigabine restored SFA and Kv7 channel function in neuroadapted cells. When microinjected bilaterally into the PL-PFC 10 min before reinstatement testing, retigabine reduced cue-induced reinstatement of cocaine seeking. Last, using cFos-GFP transgenic rats, we found that the loss of SFA correlated with the expression of cFos-GFP following both extinction and re-exposure to drug-associated cues. Together, these data suggest that cocaine self-administration desensitizes inhibitory Kv7 channels in a subpopulation of PL-PFC neurons. This subpopulation of neurons may represent a persistent neural ensemble responsible for driving drug seeking in response to cues.SIGNIFICANCE STATEMENT Long after the cessation of drug use, cues associated with cocaine still elicit drug-seeking behavior, in part by activation of the prelimbic prefrontal cortex (PL-PFC). The underlying cellular mechanisms governing these activated neurons remain unclear. Using a rat model of relapse to cocaine seeking, we identified a population of PL-PFC neurons that become hyperexcitable following chronic cocaine self-administration. These neurons show persistent loss of spike frequency adaptation, reduced afterhyperpolarizations, decreased sensitivity to dopamine, and reduced Kv7 channel-mediated inhibition. Stabilization of Kv7 channel function with retigabine normalized neuronal excitability, restored Kv7 channel currents, and reduced drug-seeking behavior when administered into the PL-PFC before reinstatement. These data highlight a persistent adaptation in a subset of PL-PFC neurons that may contribute to relapse vulnerability.

Quantitative evaluation of extrinsic factors influencing electrical excitability in neuronal networks: Voltage Threshold Measurement Method (VTMM)

The electrical excitability of neural networks is influenced by different environmental factors. Effective and simple methods are required to objectively and quantitatively evaluate the influence of such factors, including variations in temperature and pharmaceutical dosage. The aim of this paper was to introduce ‘the voltage threshold measurement method’, which is a new method using microelectrode arrays that can quantitatively evaluate the influence of different factors on the electrical excitability of neural networks. We sought to verify the feasibility and efficacy of the method by studying the effects of acetylcholine, ethanol, and temperature on hippocampal neuronal networks and hippocampal brain slices. First, we determined the voltage of the stimulation pulse signal that elicited action potentials in the two types of neural networks under normal conditions. Second, we obtained the voltage thresholds for the two types of neural networks under different concentrations of acetylcholine, ethanol, and different temperatures. Finally, we obtained the relationship between voltage threshold and the three influential factors. Our results indicated that the normal voltage thresholds of the hippocampal neuronal network and hippocampal slice preparation were 56 and 31 mV, respectively. The voltage thresholds of the two types of neural networks were inversely proportional to acetylcholine concentration, and had an exponential dependency on ethanol concentration. The curves of the voltage threshold and the temperature of the medium for the two types of neural networks were U-shaped. The hippocampal neuronal network and hippocampal slice preparations lost their excitability when the temperature of the medium decreased below 34 and 33°C or increased above 42 and 43°C, respectively. These results demonstrate that the voltage threshold measurement method is effective and simple for examining the performance/excitability of neuronal networks.

ToolConnect: A Functional Connectivity Toolbox for In vitro Networks

Nowadays, the use of in vitro reduced models of neuronal networks to investigate the interplay between structural-functional connectivity and the emerging collective dynamics is a widely accepted approach. In this respect, a relevant advance for this kind of studies has been given by the recent introduction of high-density large-scale Micro-Electrode Arrays (MEAs) which have favored the mapping of functional connections and the recordings of the neuronal electrical activity. Although, several toolboxes have been implemented to characterize network dynamics and derive functional links, no specifically dedicated software for the management of huge amount of data and direct estimation of functional connectivity maps has been developed. toolconnect offers the implementation of up to date algorithms and a user-friendly Graphical User Interface (GUI) to analyze recorded data from large scale networks. It has been specifically conceived as a computationally efficient open-source software tailored to infer functional connectivity by analyzing the spike trains acquired from in vitro networks coupled to MEAs. In the current version, toolconnect implements correlation- (cross-correlation, partial-correlation) and information theory (joint entropy, transfer entropy) based core algorithms, as well as useful and practical add-ons to visualize functional connectivity graphs and extract some topological features. In this work, we present the software, its main features and capabilities together with some demonstrative applications on hippocampal recordings.

From functional to structural connectivity using partial correlation in neuronal assemblies

OBJECTIVE

Our goal is to re-introduce an optimized version of the partial correlation to infer structural connections from functional-effective ones in dissociated neuronal cultures coupled to microelectrode arrays.

APPROACH

We first validate our partialization procedure on in silico networks, mimicking different experimental conditions (i.e., different connectivity degrees and number of nodes) and comparing the partial correlation's performance with two gold-standard methods: cross-correlation and transfer entropy. Afterwards, to infer the structural connections in in vitro neuronal networks where the ground truth is unknown, we propose a thresholding heuristic approach. Then, to validate whether the partialization process correctly reconstructs macroscopic features of the network structure, we extract a modularity index from segregated in silico and in vitro models. Finally, as a case study, we apply our partialization procedure to analyze connectivity and topology on spontaneous developing and electrically stimulated in vitro cultures.

MAIN RESULTS

In simulated networks, partial correlation outperforms cross-correlation and transfer entropy at low and medium connectivity degrees, not only in relatively small (60 nodes) but also in larger (120-240 nodes) assemblies. Furthermore, partial correlation correctly identifies interconnected neuronal sub-populations and allows one to derive network topology in in vitro cortical networks.

SIGNIFICANCE

Our results support the idea that partial correlation is a good method for connectivity studies and can be applied to derive topological and structural features of neuronal assemblies.

Universality, complexity and the praxis of biology: Two case studies

The phenomenon of biology provides a prime example for a naturally occurring complex system. The approach to this complexity reflects the tension between a reductionist, reverse-engineering stance, and more abstract, systemic ones. Both of us are reductionists, but our observations challenge reductionism, at least the naive version of it. Here we describe the challenge, focusing on two universal characteristics of biological complexity: two-way microscopic-macroscopic degeneracy, and lack of time scale separation within and between levels of organization. These two features and their consequences for the praxis of experimental biology, reflect inherent difficulties in separating the dynamics of any given level of organization from the coupled dynamics of all other levels, including the environment within which the system is embedded. Where these difficulties are not deeply acknowledged, the impacts of fallacies that are inherent to naive reductionism are significant. In an era where technology enables experimental high-resolution access to numerous observables, the challenge faced by the mature reductionist-identification of relevant microscopic variables-becomes more demanding than ever. The demonstrations provided here are taken from two very different biological realizations: populations of microorganisms and populations of neurons, thus making the lesson potentially general.

Detecting pairwise correlations in spike trains: an objective comparison of methods and application to the study of retinal waves

Correlations in neuronal spike times are thought to be key to processing in many neural systems. Many measures have been proposed to summarize these correlations and of these the correlation index is widely used and is the standard in studies of spontaneous retinal activity. We show that this measure has two undesirable properties: it is unbounded above and confounded by firing rate. We list properties needed for a measure to fairly quantify and compare correlations and we propose a novel measure of correlation-the spike time tiling coefficient. This coefficient, the correlation index, and 33 other measures of correlation of spike times are blindly tested for the required properties on synthetic and experimental data. Based on this, we propose a measure (the spike time tiling coefficient) to replace the correlation index. To demonstrate the benefits of this measure, we reanalyze data from seven key studies, which previously used the correlation index to investigate the nature of spontaneous activity. We reanalyze data from β2(KO) and β2(TG) mutants, mutants lacking connexin isoforms, and also the age-dependent changes in wild-type and β2(KO) correlations. Reanalysis of the data using the proposed measure can significantly change the conclusions. It leads to better quantification of correlations and therefore better inference from the data. We hope that the proposed measure will have wide applications, and will help clarify the role of activity in retinotopic map formation.

Parameters for burst detection

Bursts of action potentials within neurons and throughout networks are believed to serve roles in how neurons handle and store information, both in vivo and in vitro. Accurate detection of burst occurrences and durations are therefore crucial for many studies. A number of algorithms have been proposed to do so, but a standard method has not been adopted. This is due, in part, to many algorithms requiring the adjustment of multiple ad-hoc parameters and further post-hoc criteria in order to produce satisfactory results. Here, we broadly catalog existing approaches and present a new approach requiring the selection of only a single parameter: the number of spikes N comprising the smallest burst to consider. A burst was identified if N spikes occurred in less than T ms, where the threshold T was automatically determined from observing a probability distribution of inter-spike-intervals. Performance was compared vs. different classes of detectors on data gathered from in vitro neuronal networks grown over microelectrode arrays. Our approach offered a number of useful features including: a simple implementation, no need for ad-hoc or post-hoc criteria, and precise assignment of burst boundary time points. Unlike existing approaches, detection was not biased toward larger bursts, allowing identification and analysis of a greater range of neuronal and network dynamics.

Modular neuronal assemblies embodied in a closed-loop environment: toward future integration of brains and machines

Behaviors, from simple to most complex, require a two-way interaction with the environment and the contribution of different brain areas depending on the orchestrated activation of neuronal assemblies. In this work we present a new hybrid neuro-robotic architecture based on a neural controller bi-directionally connected to a virtual robot implementing a Braitenberg vehicle aimed at avoiding obstacles. The robot is characterized by proximity sensors and wheels, allowing it to navigate into a circular arena with obstacles of different sizes. As neural controller, we used hippocampal cultures dissociated from embryonic rats and kept alive over Micro Electrode Arrays (MEAs) for 3-8 weeks. The developed software architecture guarantees a bi-directional exchange of information between the natural and the artificial part by means of simple linear coding/decoding schemes. We used two different kinds of experimental preparation: "random" and "modular" populations. In the second case, the confinement was assured by a polydimethylsiloxane (PDMS) mask placed over the surface of the MEA device, thus defining two populations interconnected via specific microchannels. The main results of our study are: (i) neuronal cultures can be successfully interfaced to an artificial agent; (ii) modular networks show a different dynamics with respect to random culture, both in terms of spontaneous and evoked electrophysiological patterns; (iii) the robot performs better if a reinforcement learning paradigm (i.e., a tetanic stimulation delivered to the network following each collision) is activated, regardless of the modularity of the culture; (iv) the robot controlled by the modular network further enhances its capabilities in avoiding obstacles during the short-term plasticity trial. The developed paradigm offers a new framework for studying, in simplified model systems, neuro-artificial bi-directional interfaces for the development of new strategies for brain-machine interaction.

A "spike-based" grammar underlies directional modification in network connectivity: effect on bursting activity and implications for bio-hybrids systems

Developed biological systems are endowed with the ability of interacting with the environment; they sense the external state and react to it by changing their own internal state. Many attempts have been made to build ‘hybrids’ with the ability of perceiving, modifying and reacting to external modifications. Investigation of the rules that govern network changes in a hybrid system may lead to finding effective methods for ‘programming’ the neural tissue toward a desired task. Here we show a new perspective in the use of cortical neuronal cultures from embryonic mouse as a working platform to study targeted synaptic modifications. Differently from the common timing-based methods applied in bio-hybrids robotics, here we evaluated the importance of endogenous spike timing in the information processing. We characterized the influence of a spike-patterned stimulus in determining changes in neuronal synchronization (connectivity strength and precision) of the evoked spiking and bursting activity in the network. We show that tailoring the stimulation pattern upon a neuronal spike timing induces the network to respond stronger and more precisely to the stimulation. Interestingly, the induced modifications are conveyed more consistently in the burst timing. This increase in strength and precision may be a key in the interaction of the network with the external world and may be used to induce directional changes in bio-hybrid systems.

Identification of feedback loops in neural networks based on multi-step Granger causality

MOTIVATION

Feedback circuits are crucial network motifs, ubiquitously found in many intra- and inter-cellular regulatory networks, and also act as basic building blocks for inducing synchronized bursting behaviors in neural network dynamics. Therefore, the system-level identification of feedback circuits using time-series measurements is critical to understand the underlying regulatory mechanism of synchronized bursting behaviors.

RESULTS

Multi-Step Granger Causality Method (MSGCM) was developed to identify feedback loops embedded in biological networks using time-series experimental measurements. Based on multivariate time-series analysis, MSGCM used a modified Wald test to infer the existence of multi-step Granger causality between a pair of network nodes. A significant bi-directional multi-step Granger causality between two nodes indicated the existence of a feedback loop. This new identification method resolved the drawback of the previous non-causal impulse response component method which was only applicable to networks containing no co-regulatory forward path. MSGCM also significantly improved the ratio of correct identification of feedback loops. In this study, the MSGCM was testified using synthetic pulsed neural network models and also in vitro cultured rat neural networks using multi-electrode array. As a result, we found a large number of feedback loops in the in vitro cultured neural networks with apparent synchronized oscillation, indicating a close relationship between synchronized oscillatory bursting behavior and underlying feedback loops. The MSGCM is an efficient method to investigate feedback loops embedded in in vitro cultured neural networks. The identified feedback loop motifs are considered as an important design principle responsible for the synchronized bursting behavior in neural networks.

Microfabricated electrochemical cell-based biosensors for analysis of living cells in vitro

Cellular biochemical parameters can be used to reveal the physiological and functional information of various cells. Due to demonstrated high accuracy and non-invasiveness, electrochemical detection methods have been used for cell-based investigation. When combined with improved biosensor design and advanced measurement systems, the on-line biochemical analysis of living cells in vitro has been applied for biological mechanism study, drug screening and even environmental monitoring. In recent decades, new types of miniaturized electrochemical biosensor are emerging with the development of microfabrication technology. This review aims to give an overview of the microfabricated electrochemical cell-based biosensors, such as microelectrode arrays (MEA), the electric cell-substrate impedance sensing (ECIS) technique, and the light addressable potentiometric sensor (LAPS). The details in their working principles, measurement systems, and applications in cell monitoring are covered. Driven by the need for high throughput and multi-parameter detection proposed by biomedicine, the development trends of electrochemical cell-based biosensors are also introduced, including newly developed integrated biosensors, and the application of nanotechnology and microfluidic technology.

Multiscale functional connectivity estimation on low-density neuronal cultures recorded by high-density CMOS Micro Electrode Arrays

We used electrophysiological signals recorded by CMOS Micro Electrode Arrays (MEAs) at high spatial resolution to estimate the functional-effective connectivity of sparse hippocampal neuronal networks in vitro by applying a cross-correlation (CC) based method and ad hoc developed spatio-temporal filtering. Low-density cultures were recorded by a recently introduced CMOS-MEA device providing simultaneous multi-site acquisition at high-spatial (21 μm inter-electrode separation) as well as high-temporal resolution (8 kHz per channel). The method is applied to estimate functional connections in different cultures and it is refined by applying spatio-temporal filters that allow pruning of those functional connections not compatible with signal propagation. This approach permits to discriminate between possible causal influence and spurious co-activation, and to obtain detailed maps down to cellular resolution. Further, a thorough analysis of the links strength and time delays (i.e., amplitude and peak position of the CC function) allows characterizing the inferred interconnected networks and supports a possible discrimination of fast mono-synaptic propagations, and slow poly-synaptic pathways. By focusing on specific regions of interest we could observe and analyze microcircuits involving connections among a few cells. Finally, the use of the high-density MEA with low density cultures analyzed with the proposed approach enables to compare the inferred effective links with the network structure obtained by staining procedures.

NETWORK DYNAMICS AND SYNCHRONOUS ACTIVITY IN CULTURED CORTICAL NEURONS

Neurons extracted from specific areas of the Central Nervous System (CNS), such as the hippocampus, the cortex and the spinal cord, can be cultured in vitro and coupled with a micro-electrode array (MEA) for months. After a few days, neurons connect each other with functionally active synapses, forming a random network and displaying spontaneous electrophysiological activity. In spite of their simplified level of organization, they represent an useful framework to study general information processing properties and specific basic learning mechanisms in the nervous system. These experimental preparations show patterns of collective rhythmic activity characterized by burst and spike firing. The patterns of electrophysiological activity may change as a consequence of external stimulation (i.e., chemical and/or electrical inputs) and by partly modifying the "randomness" of the network architecture (i.e., confining neuronal sub-populations in clusters with micro-machined barriers). In particular we investigated how the spontaneous rhythmic and synchronous activity can be modulated or drastically changed by focal electrical stimulation, pharmacological manipulation and network segregation. Our results show that burst firing and global synchronization can be enhanced or reduced; and that the degree of synchronous activity in the network can be characterized by simple parameters such as cross-correlation on burst events.

Representation and learning in neuronal networks: a conceptual nervous system approach

The work presented in this review describes the use of large cortical networks developing ex vivo, in a culture dish, to study principles underlying synchronization, adaptation, learning, and representation in neuronal assemblies. The motivation to study neuronal networks ex vivo is outlined together with a short description of recent results in this field. Following a short description of the experimental system, a set of basic results will be presented that concern self-organization of activity, dynamical and functional properties of neurons and networks in response to external stimulation. This short review ends with an outline of future questions and research directions.

A new cross-correlation algorithm for the analysis of "in vitro" neuronal network activity aimed at pharmacological studies

Modern drug discovery for Central Nervous System pathologies has recently focused its attention to in vitro neuronal networks as models for the study of neuronal activities. Micro Electrode Arrays (MEAs), a widely recognized tool for pharmacological investigations, enable the simultaneous study of the spiking activity of discrete regions of a neuronal culture, providing an insight into the dynamics of networks. Taking advantage of MEAs features and making the most of the cross-correlation analysis to assess internal parameters of a neuronal system, we provide an efficient method for the evaluation of comprehensive neuronal network activity. We developed an intra network burst correlation algorithm, we evaluated its sensitivity and we explored its potential use in pharmacological studies. Our results demonstrate the high sensitivity of this algorithm and the efficacy of this methodology in pharmacological dose-response studies, with the advantage of analyzing the effect of drugs on the comprehensive correlative properties of integrated neuronal networks.

Extracellular recording of spatiotemporal patterning in response to odors in the olfactory epithelium by microelectrode arrays

In olfactory biosensors, microelectronic sensor chips combined with biological olfactory cells are a promising platform for odor detection. In our investigation, olfactory epithelium stripped from rat was fixed on the surface of microelectrode arrays (MEAs). Electrophysiological activities of olfactory receptor neurons in intact epithelium were measured in the form of extracellular potentials. Based on multi-channel recording performance of MEA and structural and functional integrality of native olfactory epithelium, the spatiotemporal analysis was carried out to study the extracellular activity pattern of neurons in the tissue. The variation of spatiotemporal patterns corresponding to different odors displayed the signals firing image characteristic intuitionally. It is an effective method in the form of patterns for monitoring the state of tissue both in time and space domain, promoting the platform for olfactory sensing mechanism research.

An olfactory bulb slice-based biosensor for multi-site extracellular recording of neural networks

Multi-site recording is the important component for studies of the neural networks. In order to investigate the electrophysiological properties of the olfactory bulb neural networks, we developed a novel slice-based biosensor for synchronous measurement with multi-sites. In the present study, the horizontal olfactory bulb slices with legible layered structures were prepared as the sensing element to construct a tissue-based biosensor with the microelectrode array. This olfactory bulb slice-based biosensor was used to simultaneously record the extracellular potentials from multi-positions. Spike detection and cross-correlation analysis were applied to evaluate the electrophysiological activities. The spontaneous potentials as well as the induced responses by glutamic acid took on different electrophysiological characteristics and firing patterns at the different sites of the olfactory bulb slice. This slice-based biosensor can realize multi-site synchronous monitoring and is advantageous for searching after the firing patterns and synaptic connections in the olfactory bulb neural networks. It is also helpful for further probing into olfactory information encoding of the olfactory neural networks.

The role of dopamine in the maintenance of working memory in prefrontal cortex neurons: input-driven versus internally-driven networks

How do organisms select and organize relevant sensory input in working memory (WM) in order to deal with constantly changing environmental cues? Once information has been stored in WM, how is it protected from and altered by the continuous stream of sensory input and internally generated planning? The present study proposes a novel role for dopamine (DA) in the maintenance of WM in the prefrontal cortex (Pfc) neurons that begins to address these issues. In particular, DA mediates the alternation of the Pfc network between input-driven and internally-driven states, which in turn drives WM updates and storage. A biologically inspired neural network model of Pfc is formulated to provide a link between the mechanisms of state switching and the biophysical properties of Pfc neurons. This model belongs to the recurrent competitive fields(33) class of dynamical systems which have been extensively mathematically characterized and exhibit the two functional states of interest: input-driven and internally-driven. This hypothesis was tested with two working memory tasks of increasing difficulty: a simple working memory task and a delayed alternation task. The results suggest that optimal WM storage in spite of noise is achieved with a phasic DA input followed by a lower DA sustained activity. Hypo and hyper-dopaminergic activity that alter this ideal pattern lead to increased distractibility from non-relevant pattern and prolonged perseverations on presented patterns, respectively.

A generic framework for real-time multi-channel neuronal signal analysis, telemetry control, and sub-millisecond latency feedback generation

Distinct modules of the neural circuitry interact with each other and (through the motor-sensory loop) with the environment, forming a complex dynamic system. Neuro-prosthetic devices seeking to modulate or restore CNS function need to interact with the information flow at the level of neural modules electrically, bi-directionally and in real-time. A set of freely available generic tools is presented that allow computationally demanding multi-channel short-latency bi-directional interactions to be realized in in vivo and in vitro preparations using standard PC data acquisition and processing hardware and software (Mathworks Matlab and Simulink). A commercially available 60-channel extracellular multi-electrode recording and stimulation set-up connected to an ex vivo developing cortical neuronal culture is used as a model system to validate the method. We demonstrate how complex high-bandwidth (>10 MBit/s) neural recording data can be analyzed in real-time while simultaneously generating specific complex electrical stimulation feedback with deterministically timed responses at sub-millisecond resolution.

Extracellular potentials recording in intact olfactory epithelium by microelectrode array for a bioelectronic nose

Human beings and animals have sensitive olfactory systems that can sense and identify a variety of odors. The purpose of this study is to combine biological cells with micro-chips to establish a novel bioelectronic nose system for odor detection by electrophysiological sensing measurements of olfactory tissue. In our experiments, 36-channel microelectrode arrays (MEAs) with the diameter of 30 microm were fabricated on the glass substrate, and olfactory epithelium was stripped from rats and fixed on the surface of MEA. Electrophysiological activities of olfactory receptor neurons in intact epithelium were measured through the multi-channel recording system. The extracellular potentials of cell networks could be effectively analyzed by correlation analysis between different channels. After being stimulated by odorants, such as acetic acid and butanedione, the olfactory cells generate different firing modes. These firing characteristics can be derived by time-domain and frequency-domain analysis, and they were different from spontaneous potentials. The investigation of olfactory epithelium can provide more information of olfactory system for artificial olfaction biomimetic design.

Low-frequency stimulation enhances burst activity in cortical cultures during development

The intact brain is continuously targeted by a wealth of stimuli with distinct spatio-temporal patterns which modify, since the very beginning of development, the activity and the connectivity of neuronal networks. In this paper, we used dissociated neuronal cultures coupled to microelectrode arrays (MEAs) to study the response of cortical neuron assemblies to low-frequency stimuli constantly delivered over weeks in vitro. We monitored the spontaneous activity of the cultures before and after the stimulation sessions, as well as their evoked response to the stimulus. During in vitro development, the vast majority of the cultures responded to the stimulation by significantly increasing the bursting activity and a widespread stabilization of electrical activity was observed after the third week of age. A similar trend was present between the spontaneous activity of the networks observed over 30 min after the stimulus and the responses evoked by the stimulus itself, although no significant differences in spontaneous activity were detected between stimulated and non-stimulated cultures belonging to the same preparations. The data indicate that the stimulation had a delayed effect modulating responsiveness capability of the network without directly affecting its intrinsic in vitro development.

Latency-related development of functional connections in cultured cortical networks

To study plasticity, we cultured cortical networks on multielectrode arrays, enabling simultaneous recording from multiple neurons. We used conditional firing probabilities to describe functional network connections by their strength and latency. These are abstract representations of neuronal pathways and may arise from direct pathways between two neurons or from a common input. Functional connections based on direct pathways should reflect synaptic properties. Therefore, we searched for long-term potentiation (this mechanism occurs in vivo when presynaptic action potentials precede postsynaptic ones with interspike intervals up to approximately 20 ms) in vitro. To investigate if the strength of functional connections showed a similar latency-related development, we selected periods of monotonously increasing or decreasing strength. We observed increased incidence of short latencies (5-30 ms) during strengthening, whereas these rarely occurred during weakening. Furthermore, we saw an increased incidence of 40-65 ms latencies during weakening. Conversely, functional connections tended to strengthen in periods with short latency, whereas strengthening was significantly less than average during long latency. Our data suggest that functional connections contain information about synaptic connections, that conditional firing probability analysis is sensitive enough to detect it and that a substantial fraction of all functional connections is based on direct pathways.

Systematic analysis of synchronized oscillatory neuronal networks reveals an enrichment for coupled direct and indirect feedback motifs

MOTIVATION

Synchronized bursting behavior is a remarkable phenomenon in neural dynamics. So, identification of the underlying functional structure is crucial to understand its regulatory mechanism at a system level. On the other hand, we noted that feedback loops (FBLs) are commonly used basic building blocks in engineering circuit design, especially for synchronization, and they have also been considered as important regulatory network motifs in systems biology. From these motivations, we have investigated the relationship between synchronized bursting behavior and feedback motifs in neural networks.

RESULTS

Through extensive simulations of synthetic spike oscillation models, we found that a particular structure of FBLs, coupled direct and indirect positive feedback loops (PFLs), can induce robust synchronized bursting behaviors. To further investigate this, we have developed a novel FBL identification method based on sampled time-series data and applied it to synchronized spiking records measured from cultured neural networks of rat by using multi-electrode array. As a result, we have identified coupled direct and indirect PFLs.

CONCLUSION

We therefore conclude that coupled direct and indirect PFLs might be an important design principle that causes the synchronized bursting behavior in neuronal networks although an extrapolation of this result to in vivo brain dynamics still remains an unanswered question.

Order-based representation in random networks of cortical neurons

The wide range of time scales involved in neural excitability and synaptic transmission might lead to ongoing change in the temporal structure of responses to recurring stimulus presentations on a trial-to-trial basis. This is probably the most severe biophysical constraint on putative time-based primitives of stimulus representation in neuronal networks. Here we show that in spontaneously developing large-scale random networks of cortical neurons in vitro the order in which neurons are recruited following each stimulus is a naturally emerging representation primitive that is invariant to significant temporal changes in spike times. With a relatively small number of randomly sampled neurons, the information about stimulus position is fully retrievable from the recruitment order. The effective connectivity that makes order-based representation invariant to time warping is characterized by the existence of stations through which activity is required to pass in order to propagate further into the network. This study uncovers a simple invariant in a noisy biological network in vitro; its applicability under in vivo constraints remains to be seen.

Extracellular recordings from locally dense microelectrode arrays coupled to dissociated cortical cultures

High-density microelectrode arrays (MEAs) enabled by recent developments of microelectronic circuits (CMOS-MEA) and providing spatial resolutions down to the cellular level open the perspective to access simultaneously local and overall neuronal network activities expressed by in vitro preparations. The short inter-electrode separation results in a gain of information on the micro-circuit neuronal dynamics and signal propagation, but requires the careful evaluation of the time resolution as well as the assessment of possible cross-talk artifacts. In this respect, we have realized and tested Pt high-density (HD)-MEAs featuring four local areas with 10microm inter-electrode spacing and providing a suitable noise level for the assessment of the high-density approach. First, simulated results show how possible artifacts (duplicated spikes) can be theoretically observed on nearby microelectrodes only for very high-shunt resistance values (e.g. R(sh)=50 kOmega generates up to 60% of false positives). This limiting condition is not compatible with typical experimental conditions (i.e. dense but not confluent cultures). Experiments performed on spontaneously active cortical neuronal networks show that spike synchronicity decreases by increasing the time resolution and analysis results show that the detected synchronous spikes on nearby electrodes are likely to be unresolved (in time) fast local propagations. Finally, functional connectivity analysis results show stronger local connections than long connections spread homogeneously over the whole network demonstrating the expected gain in detail provided by the spatial resolution.

Spontaneous neuronal burst discharges as dependent and independent variables in the maturation of cerebral cortex tissue cultured in vitro: a review of activity-dependent studies in live 'model' systems for the development of intrinsically generated bioelectric slow-wave sleep patterns

A survey is presented of recent experiments which utilize spontaneous neuronal spike trains as dependent and/or independent variables in developing cerebral cortex cultures when synaptic transmission is interfered with for varying periods of time. Special attention is given to current difficulties in selecting suitable preparations for carrying out biologically relevant developmental studies, and in applying spike-train analysis methods with sufficient resolution to detect activity-dependent age and treatment effects. A hierarchy of synchronized nested burst discharges which approximate early slow-wave sleep patterns in the intact organism is established as a stable basis for isolated cortex function. The complexity of reported long- and short-term homeostatic responses to experimental interference with synaptic transmission is reviewed, and the crucial role played by intrinsically generated bioelectric activity in the maturation of cortical networks is emphasized.

Self-organization and neuronal avalanches in networks of dissociated cortical neurons

Dissociated cortical neurons from rat embryos cultured onto micro-electrode arrays exhibit characteristic patterns of electrophysiological activity, ranging from isolated spikes in the first days of development to highly synchronized bursts after 3-4 weeks in vitro. In this work we analyzed these features by considering the approach proposed by the self-organized criticality theory: we found that networks of dissociated cortical neurons also generate spontaneous events of spreading activity, previously observed in cortical slices, in the form of neuronal avalanches. Choosing an appropriate time scale of observation to detect such neuronal avalanches, we studied the dynamics by considering the spontaneous activity during acute recordings in mature cultures and following the development of the network. We observed different behaviors, i.e. sub-critical, critical or super-critical distributions of avalanche sizes and durations, depending on both the age and the development of cultures. In order to clarify this variability, neuronal avalanches were correlated with other statistical parameters describing the global activity of the network. Criticality was found in correspondence to medium synchronization among bursts and high ratio between bursting and spiking activity. Then, the action of specific drugs affecting global bursting dynamics (i.e. acetylcholine and bicuculline) was investigated to confirm the correlation between criticality and regulated balance between synchronization and variability in the bursting activity. Finally, a computational model of neuronal network was developed in order to interpret the experimental results and understand which parameters (e.g. connectivity, excitability) influence the distribution of avalanches. In summary, cortical neurons preserve their capability to self-organize in an effective network even when dissociated and cultured in vitro. The distribution of avalanche features seems to be critical in those cultures displaying medium synchronization among bursts and poor random spiking activity, as confirmed by chemical manipulation experiments and modeling studies.

Towards neuro-memory-chip: imprinting multiple memories in cultured neural networks

We show that using local chemical stimulations it is possible to imprint persisting (days) multiple memories (collective modes of neuron firing) in the activity of cultured neural networks. Microdroplets of inhibitory antagonist are injected at a location selected based on real-time analysis of the recorded activity. The neurons at the stimulated locations turn into a focus for initiating synchronized bursting events (the collective modes) each with its own specific spatiotemporal pattern of neuron firing.

Conditional firing probabilities in cultured neuronal networks: a stable underlying structure in widely varying spontaneous activity patterns

To properly observe induced connectivity changes after training sessions, one needs a network model that describes individual relationships in sufficient detail to enable observation of induced changes and yet reveals some kind of stability in these relationships. We analyzed spontaneous firing activity in dissociated rat cortical networks cultured on multi-electrode arrays by means of the conditional firing probability. For all pairs (i, j) of the 60 electrodes, we calculated conditional firing probability (CFP(i,j)[tau]) as the probability of an action potential at electrode j at t = tau, given that one was detected at electrode i at t = 0. If a CFP(i,j)[tau] distribution clearly deviated from a flat one, electrodes i and j were considered to be related. For all related electrode pairs, a function was fitted to the CFP-curve to obtain parameters for 'strength' and 'delay' (i.e. maximum and latency of the maximum of the curve) of each relationship. In young cultures the set of identified relationships changed rather quickly. At 16 days in vitro (DIV) 50% of the set changed within 2 days. Beyond 25 DIV this set stabilized: during a week more than 50% of the set remained intact. Most individual relationships developed rather gradually. Moreover, beyond 25 DIV relational strength appeared quite stable, with coefficients of variation (100 x SD/mean) around 25% in periods of approximately 10 h. CFP analysis provides a robust method to describe the underlying probabilistic structure of highly varying spontaneous activity in cultured cortical networks. It may offer a suitable basis for plasticity studies, in the case of changes in the probabilistic structure. CFP analysis monitors all pairs of electrodes instead of just a selected one. Still, it is likely to describe the network in sufficient detail to detect subtle changes in individual relationships.

Dynamics and effective topology underlying synchronization in networks of cortical neurons

Cognitive processes depend on synchronization and propagation of electrical activity within and between neuronal assemblies. In vivo measurements show that the size of individual assemblies depends on their function and varies considerably, but the timescale of assembly activation is in the range of 0.1-0.2 s and is primarily independent of assembly size. Here we use an in vitro experimental model of cortical assemblies to characterize the process underlying the timescale of synchronization, its relationship to the effective topology of connectivity within an assembly, and its impact on propagation of activity within and between assemblies. We show that the basic mode of assembly activation, "network spike," is a threshold-governed, synchronized population event of 0.1-0.2 s duration and follows the logistics of neuronal recruitment in an effectively scale-free connected network. Accordingly, the sequence of neuronal activation within a network spike is nonrandom and hierarchical; a small subset of neurons is consistently recruited tens of milliseconds before others. Theory predicts that scale-free topology allows for synchronization time that does not increase markedly with network size; our experiments with networks of different densities support this prediction. The activity of early-to-fire neurons reliably forecasts an upcoming network spike and provides means for expedited propagation between assemblies. We demonstrate this capacity by observing the dynamics of two artificially coupled assemblies in vitro, using neuronal activity of one as a trigger for electrical stimulation of the other.

Inverted-U profile of dopamine-NMDA-mediated spontaneous avalanche recurrence in superficial layers of rat prefrontal cortex

Prefrontal cortex (PFC) functions, such as working memory, attention selection, and memory retrieval, depend critically on dopamine and NMDA receptor activation by way of an inverted-U-shaped pharmacological profile. Although single neuron responses in the PFC have shown some aspects of this profile, a network dynamic that follows the dopamine-NMDA dependence has not been identified. We studied neuronal network activity in acute medial PFC slices of adult rats by recording local field potentials (LFPs) with microelectrode arrays. Bath application of dopamine or the dopamine D1 agonist SKF38393 [(+/-)-1-phenyl-2,3,4,5-tetrahydro-(1H)-3-benzazepine-7,8-diol hydrochloride] in combination with NMDA induced spontaneous LFPs predominantly in superficial cortex layers. The LFPs at single electrodes were characterized by sharp negative peaks that were clustered in time across electrodes revealing diverse spatiotemporal patterns on the array. The pattern formation required fast GABAergic transmission, coactivation of the dopamine D1 and NMDA receptor, and depended in an inverted-U profile on dopamine. At moderate concentrations of dopamine or the dopamine D1 agonist, the pattern size distribution formed a power law with exponent alpha = -1.5, indicating that patterns are organized in the form of neuronal avalanches, thereby maximizing spatial correlations in the network. At lower or higher concentrations, alpha was more negative than -1.5, indicating reduced spatial correlations. Likewise, at moderate dopamine concentrations, the avalanche rate and recurrence of specific avalanches was maximal with recurrence frequencies after a "power law"-like heavy-tail distribution with a slope of -2.4. We suggest that the dopamine-NMDA-dependent spontaneous recurrence of specific avalanches in superficial cortical layers might facilitate integrative and associative aspects of PFC functions.

Dissociated cortical networks show spontaneously correlated activity patterns during in vitro development

In vitro cultured neuronal networks coupled to microelectrode arrays (MEAs) constitute a valuable experimental model for studying changes in the neuronal dynamics at different stages of development. After a few days in culture, neurons start to connect each other with functionally active synapses, forming a random network and displaying spontaneous electrophysiological activity. The patterns of collective rhythmic activity change in time spontaneously during in vitro development. Such activity-dependent modifications play a key role in the maturation of the network and reflect changes in the synaptic efficacy, fact widely recognized as a cellular basis of learning, memory and developmental plasticity. Getting advantage from the possibilities offered by the MEAs, the aim of our study is to analyze and characterize the natural changes in dynamics of the electrophysiological activity at different ages of the culture, identifying peculiar steps of the spontaneous evolution of the network. The main finding is that between the second and the third week of culture, the network completely changes its electrophysiological patterns, both in terms of spiking and bursting activity and in terms of cross-correlation between pairs of active channels. Then the maturation process can be characterized by two main phases: modulation and shaping in the synaptic functional connectivity of the network (within the first and second week) and general moderate correlated activity, spread over the entire network, with connections properly formed and stabilized (within the fourth and fifth week).

Spatio-temporal cholinergic modulation in cultured networks of rat cortical neurons: spontaneous activity

Activation of the cholinergic innervation of the cortex has been implicated in sensory processing, learning, and memory. At the cellular level, acetylcholine both increases excitability and depresses synaptic transmission, and its effects on network firing are hard to predict. We studied the effects of carbachol, a cholinergic agonist, on network firing in cultures of rat cortical neurons, using electrode arrays to monitor the activity of large numbers of neurons simultaneously. These cultures show stable spontaneous synchronized burst firing which propagates through dense synaptic connections. Carbachol (10-50 microM), acting through muscarinic receptors, was found to induce a switch to asynchronous single-spike firing and to result in a loss of regularity and fragmentation of the burst structure. To obtain a quantitative measure of cholinergic actions on cortical networks, we applied a cluster Poisson-process model to sets of paralleled spike-trains in the presence and absence of carbachol. This revealed that the time series can be well-characterized by such a simple model, consistent with the observed 1/f(b)-like spectra (0.04<b<2.08). After applying higher concentrations of carbachol the property of the spectra shifted toward a Poisson-process (white) spectrum. These results indicate that cholinergic neurotransmitters have a strong and reliable desynchronizing action on cortical neural activity.

Investigating neuronal activity with planar microelectrode arrays: achievements and new perspectives

Neuronal networks underlie memory storage and information processing in the human brain, and ultimately participate in what Eccles referred to as "the creation of consciousness". Moreover, as physiological dysfunctions of neurons almost always translate into serious health issues, the study of the dynamics of neuronal networks has become a major avenue of research, as well as their response to pharmacological tampering. Planar microelectrode arrays represent a unique tool to investigate such dynamics and interferences, as they allow one to observe the activity of neuronal networks spread in both space and time. We will here review the major results obtained with microelectrode arrays and give an overview of the latest technological developments in the field, including our own efforts to develop the potential of this already powerful technology.

Spatio-temporal cholinergic modulation in cultured networks of rat cortical neurons: Evoked activity

We studied the effects of carbachol, a cholinergic agonist, on extracellularly evoked firing of networks in mature cultures of rat cortical neurons, using multi-electrode arrays to monitor the activity of large numbers of neurons simultaneously. These cultures show evoked burst firing which propagates through dense synaptic connections. When a brief voltage pulse was applied to one extracellular electrode, spiking electrical responses were evoked in neurons throughout the network. The response had two components: an early phase, terminating within 30-80 ms, and a late phase which could last several hundreds of milliseconds. Action potentials evoked during the early phase were precisely timed, with only small jitter. In contrast, the late phase characteristically showed clusters of electrical activity with significant spatio-temporal fluctuations. The late phase was suppressed by applying a relatively small amount of carbachol (5 microM) in the external solution, even though the spontaneous firing rate was not significantly changed. Carbachol increased both the spike-timing precision and the speed of propagation of population spikes, and selectively increased the firing coincidence in a subset of neuron pairs in the network, while suppressing late variable firing in responses. Hence, the results give quantitative support for the idea that cholinergic activation in the cortex has a general role of focusing or enhancing significant associative firing of neurons.

Learning in ex-vivo developing networks of cortical neurons

This contribution describes the use of multi-site interaction with large cortical networks in the study of learning. The general physiological properties of the network are described, and the concept of learning is mapped to the experimental network preparation. Learning is then analyzed in terms of exploration (defined as changes in the configuration of associations within the biological network) and recognition (the stabilization of "worthy" associations).