Emergent functional properties of neuronal networks with controlled topology

Structure, Function, and Propagation of Information across Living Two, Four, and Eight Node Degree Topologies

In this study, we created four network topologies composed of living cortical neurons and compared resultant structural-functional dynamics including the nature and quality of information transmission. Each living network was composed of living cortical neurons and were created using microstamping of adhesion promoting molecules and each was "designed" with different levels of convergence embedded within each structure. Networks were cultured over a grid of electrodes that permitted detailed measurements of neural activity at each node in the network. Of the topologies we tested, the "Random" networks in which neurons connect based on their own intrinsic properties transmitted information embedded within their spike trains with higher fidelity relative to any other topology we tested. Within our patterned topologies in which we explicitly manipulated structure, the effect of convergence on fidelity was dependent on both topology and time-scale (rate vs. temporal coding). A more detailed examination using tools from network analysis revealed that these changes in fidelity were also associated with a number of other structural properties including a node's degree, degree-degree correlations, path length, and clustering coefficients. Whereas information transmission was apparent among nodes with few connections, the greatest transmission fidelity was achieved among the few nodes possessing the highest number of connections (high degree nodes or putative hubs). These results provide a unique view into the relationship between structure and its affect on transmission fidelity, at least within these small neural populations with defined network topology. They also highlight the potential role of tools such as microstamp printing and microelectrode array recordings to construct and record from arbitrary network topologies to provide a new direction in which to advance the study of structure-function relationships.

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.

Cell Microarrays for Biomedical Applications

In this chapter the state of the art of live cell microarrays for high-throughput biological assays are reviewed. The fabrication of novel microarrays with respect to material science and cell patterning methods is included. A main focus of the chapter is on various aspects of the application of cell microarrays by providing selected examples in research fields such as biomaterials, stem cell biology and neuroscience. Additionally, the importance of microfluidic technologies for high-throughput on-chip live-cell microarrays is highlighted for single-cell and multi-cell assays as well as for 3D tissue constructs.

Modification of surface/neuron interfaces for neural cell-type specific responses: a review

Surface/neuron interfaces have played an important role in neural repair including neural prostheses and tissue engineered scaffolds. This comprehensive literature review covers recent studies on the modification of surface/neuron interfaces. These interfaces are identified in cases both where the surfaces of substrates or scaffolds were in direct contact with cells and where the surfaces were modified to facilitate cell adhesion and controlling cell-type specific responses. Different sources of cells for neural repair are described, such as pheochromocytoma neuronal-like cell, neural stem cell (NSC), embryonic stem cell (ESC), mesenchymal stem cell (MSC) and induced pluripotent stem cell (iPS). Commonly modified methods are discussed including patterned surfaces at micro- or nano-scale, surface modification with conducting coatings, and functionalized surfaces with immobilized bioactive molecules. These approaches to control cell-type specific responses have enormous potential implications in neural repair.

Response profiles of murine spiral ganglion neurons on multi-electrode arrays

OBJECTIVE

Cochlear implants (CIs) have become the gold standard treatment for deafness. These neuroprosthetic devices feature a linear electrode array, surgically inserted into the cochlea, and function by directly stimulating the auditory neurons located within the spiral ganglion, bypassing lost or not-functioning hair cells. Despite their success, some limitations still remain, including poor frequency resolution and high-energy consumption. In both cases, the anatomical gap between the electrode array and the spiral ganglion neurons (SGNs) is believed to be an important limiting factor. The final goal of the study is to characterize response profiles of SGNs growing in intimate contact with an electrode array, in view of designing novel CI devices and stimulation protocols, featuring a gapless interface with auditory neurons.

APPROACH

We have characterized SGN responses to extracellular stimulation using multi-electrode arrays (MEAs). This setup allows, in our view, to optimize in vitro many of the limiting interface aspects between CIs and SGNs.

MAIN RESULTS

Early postnatal mouse SGN explants were analyzed after 6-18 days in culture. Different stimulation protocols were compared with the aim to lower the stimulation threshold and the energy needed to elicit a response. In the best case, a four-fold reduction of the energy was obtained by lengthening the biphasic stimulus from 40 μs to 160 μs. Similarly, quasi monophasic pulses were more effective than biphasic pulses and the insertion of an interphase gap moderately improved efficiency. Finally, the stimulation with an external electrode mounted on a micromanipulator showed that the energy needed to elicit a response could be reduced by a factor of five with decreasing its distance from 40 μm to 0 μm from the auditory neurons.

SIGNIFICANCE

This study is the first to show electrical activity of SGNs on MEAs. Our findings may help to improve stimulation by and to reduce energy consumption of CIs and thereby contribute to the development of fully implantable devices with better auditory resolution in the future.

Functional connectivity in in vitro neuronal assemblies

Complex network topologies represent the necessary substrate to support complex brain functions. In this work, we reviewed in vitro neuronal networks coupled to Micro-Electrode Arrays (MEAs) as biological substrate. Networks of dissociated neurons developing in vitro and coupled to MEAs, represent a valid experimental model for studying the mechanisms governing the formation, organization and conservation of neuronal cell assemblies. In this review, we present some examples of the use of statistical Cluster Coefficients and Small World indices to infer topological rules underlying the dynamics exhibited by homogeneous and engineered neuronal networks.

Agarose-Based Substrate Modification Technique for Chemical and Physical Guiding of Neurons In Vitro

A new low cost and highly reproducible technique is presented that provides patterned cell culture substrates. These allow for selective positioning of cells and a chemically and mechanically directed guiding of their extensions. The patterned substrates consist of structured agarose hydrogels molded from reusable silicon micro templates. These templates consist of pins arranged equidistantly in squares, connected by bars, which mold corresponding wells and channels in the nonadhesive agarose hydrogel. Subsequent slice production with a standard vibratome, comprising the described template pattern, completes substrate production. Invertebrate neurons of locusts and pond snails are used for this application as they offer the advantage over vertebrate cells as being very large and suitable for cultivation in low cell density. Their neurons adhere to and grow only on the adhesive areas not covered by the agarose. Agarose slices of 50 μm thickness placed on glass, polystyrene, or MEA surfaces position and immobilize the neurons in the wells, and the channels guide their neurite outgrowth toward neighboring wells. In addition to the application with invertebrate neurons, the technique may also provide the potential for the application of a wide range of cell types. Long-term objective is the achievement of isolated low-density neuronal networks on MEAs or different culture substrates for various network analysis applications.

Towards a 'siliconeural computer': technological successes and challenges

Electronic signals govern the function of both nervous systems and computers, albeit in different ways. As such, hybridizing both systems to create an iono-electric brain-computer interface is a realistic goal; and one that promises exciting advances in both heterotic computing and neuroprosthetics capable of circumventing devastating neuropathology. 'Neural networks' were, in the 1980s, viewed naively as a potential panacea for all computational problems that did not fit well with conventional computing. The field bifurcated during the 1990s into a highly successful and much more realistic machine learning community and an equally pragmatic, biologically oriented 'neuromorphic computing' community. Algorithms found in nature that use the non-synchronous, spiking nature of neuronal signals have been found to be (i) implementable efficiently in silicon and (ii) computationally useful. As a result, interest has grown in techniques that could create mixed 'siliconeural' computers. Here, we discuss potential approaches and focus on one particular platform using parylene-patterned silicon dioxide.

Graphene-Based Materials in Regenerative Medicine

Graphene possesses many unique properties such as two-dimensional planar structure, super conductivity, chemical and mechanical stability, large surface area, and good biocompatibility. In the past few years, graphene-based materials have risen as a shining star on the path of researchers seeking new materials for future regenerative medicine. Herein, the recent research advances made in graphene-based materials mostly utilizing the mechanical and electrical properties of graphene are described. The most exciting findings addressing the impact of graphene-based materials on regenerative medicine are highlighted, with particular emphasis on their applications including nerve, bone, cartilage, skeletal muscle, cardiac, skin, adipose tissue regeneration, and their effects on the induced pluripotent stem cells. Future perspectives and emerging challenges are also addressed in this Review article.

A framework for analyzing the relationship between gene expression and morphological, topological, and dynamical patterns in neuronal networks

BACKGROUND

A key point in developmental biology is to understand how gene expression influences the morphological and dynamical patterns that are observed in living beings.

NEW METHOD

In this work we propose a methodology capable of addressing this problem that is based on estimating the mutual information and Pearson correlation between the intensity of gene expression and measurements of several morphological properties of the cells. A similar approach is applied in order to identify effects of gene expression over the system dynamics. Neuronal networks were artificially grown over a lattice by considering a reference model used to generate artificial neurons. The input parameters of the artificial neurons were determined according to two distinct patterns of gene expression and the dynamical response was assessed by considering the integrate-and-fire model.

RESULTS

As far as single gene dependence is concerned, we found that the interaction between the gene expression and the network topology, as well as between the former and the dynamics response, is strongly affected by the gene expression pattern. In addition, we observed a high correlation between the gene expression and some topological measurements of the neuronal network for particular patterns of gene expression.

COMPARISON WITH EXISTING METHODS

To our best understanding, there are no similar analyses to compare with.

CONCLUSIONS

A proper understanding of gene expression influence requires jointly studying the morphology, topology, and dynamics of neurons. The proposed framework represents a first step towards predicting gene expression patterns from morphology and connectivity.

From 2D to 3D: novel nanostructured scaffolds to investigate signalling in reconstructed neuronal networks

To recreate in vitro 3D neuronal circuits will ultimately increase the relevance of results from cultured to whole-brain networks and will promote enabling technologies for neuro-engineering applications. Here we fabricate novel elastomeric scaffolds able to instruct 3D growth of living primary neurons. Such systems allow investigating the emerging activity, in terms of calcium signals, of small clusters of neurons as a function of the interplay between the 2D or 3D architectures and network dynamics. We report the ability of 3D geometry to improve functional organization and synchronization in small neuronal assemblies. We propose a mathematical modelling of network dynamics that supports such a result. Entrapping carbon nanotubes in the scaffolds remarkably boosted synaptic activity, thus allowing for the first time to exploit nanomaterial/cell interfacing in 3D growth support. Our 3D system represents a simple and reliable construct, able to improve the complexity of current tissue culture models.

Combining microfluidics, optogenetics and calcium imaging to study neuronal communication in vitro

In this paper we report the combination of microfluidics, optogenetics and calcium imaging as a cheap and convenient platform to study synaptic communication between neuronal populations in vitro. We first show that Calcium Orange indicator is compatible in vitro with a commonly used Channelrhodopsine-2 (ChR2) variant, as standard calcium imaging conditions did not alter significantly the activity of transduced cultures of rodent primary neurons. A fast, robust and scalable process for micro-chip fabrication was developed in parallel to build micro-compartmented cultures. Coupling optical fibers to each micro-compartment allowed for the independent control of ChR2 activation in the different populations without crosstalk. By analyzing the post-stimuli activity across the different populations, we finally show how this platform can be used to evaluate quantitatively the effective connectivity between connected neuronal populations.

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

The brain operates through the coordinated activation and the dynamic communication of neuronal assemblies. A major open question is how a vast repertoire of dynamical motifs, which underlie most diverse brain functions, can emerge out of a fixed topological and modular organization of brain circuits. Compared to in vivo studies of neuronal circuits which present intrinsic experimental difficulties, in vitro preparations offer a much larger possibility to manipulate and probe the structural, dynamical and chemical properties of experimental neuronal systems. This work describes an in vitro experimental methodology which allows growing of modular networks composed by spatially distinct, functionally interconnected neuronal assemblies. The protocol allows controlling the two-dimensional (2D) architecture of the neuronal network at different levels of topological complexity. A desired network patterning can be achieved both on regular cover slips and substrate embedded micro electrode arrays. Micromachined structures are embossed on a silicon wafer and used to create biocompatible polymeric stencils, which incorporate the negative features of the desired network architecture. The stencils are placed on the culturing substrates during the surface coating procedure with a molecular layer for promoting cellular adhesion. After removal of the stencils, neurons are plated and they spontaneously redirected to the coated areas. By decreasing the inter-compartment distance, it is possible to obtain either isolated or interconnected neuronal circuits. To promote cell survival, cells are co-cultured with a supporting neuronal network which is located at the periphery of the culture dish. Electrophysiological and optical recordings of the activity of modular networks obtained respectively by using substrate embedded micro electrode arrays and calcium imaging are presented. While each module shows spontaneous global synchronizations, the occurrence of inter-module synchronization is regulated by the density of connection among the circuits.

Toward on-chip functional neuronal networks: computational study on the effect of synaptic connectivity on neural activity

This paper presents a new unified computational-experimental approach to study the role of the synaptic activity on the activity of neurons in the small neuronal networks (NNs). In a neuronal tissue/organ, this question is investigated with higher complexities by recording action potentials from population of neurons in order to find the relationship between connectivity and the recorded activities. In this approach, we study the dynamics of very small cortical neuronal networks, which can be experimentally synthesized on chip with constrained connectivity. Multi-compartmental Hodgkin-Huxley model is used in NEURON software to reproduce cells by extracting the experimental data from the synthesized NNs. We thereafter demonstrate how the type of synaptic activity affects the network response to specific spike train using the simulation results.

‘Body-on-a-Chip’ Technology and Supporting Microfluidics

In order to effectively streamline current drug development protocols, there is a need to generate high information content preclinical screens capable of generating data with a predictive power in relation to the activity of novel therapeutics in humans. Given the poor predictive power of animal models, and the lack of complexity and interconnectivity of standard in vitro culture methodologies, many investigators are now moving toward the development of physiologically and functionally accurate culture platforms composed of human cells to investigate cellular responses to drug compounds in high-throughput preclinical studies. The generation of complex, multi-organ in vitro platforms, built to recapitulate physiological dimensions, flow rates and shear stresses, is being investigated as the logical extension of this drive. Production and application of a biologically accurate multi-organ platform, or ‘body-on-a-chip’, would facilitate the correct modelling of the dynamic and interconnected state of living systems for high-throughput drug studies as well as basic and applied biomolecular research. This chapter will discuss current technologies aimed at producing ‘body-on-a-chip’ models, as well as highlighting recent advances and important challenges still to be met in the development of biomimetic single-organ systems for drug development purposes.

Nanotechnologies for the study of the central nervous system

The impact of central nervous system (CNS) disorders on the human population is significant, contributing almost €800 billion in annual European healthcare costs. These disorders not only have a disabling social impact but also a crippling economic drain on resources. Developing novel therapeutic strategies for these disorders requires a better understanding of events that underlie mechanisms of neural circuit physiology. Studying the relationship between genetic expression, synapse development and circuit physiology in CNS function is a challenging task, involving simultaneous analysis of multiple parameters and the convergence of several disciplines and technological approaches. However, current gold-standard techniques used to study the CNS have limitations that pose unique challenges to furthering our understanding of functional CNS development. The recent advancement in nanotechnologies for biomedical applications has seen the emergence of nanoscience as a key enabling technology for delivering a translational bridge between basic and clinical research. In particular, the development of neuroimaging and electrophysiology tools to identify the aetiology and progression of CNS disorders have led to new insights in our understanding of CNS physiology and the development of novel diagnostic modalities for therapeutic intervention. This review focuses on the latest applications of these nanotechnologies for investigating CNS function and the improved diagnosis of CNS disorders.

Emergence of bursting activity in connected neuronal sub-populations

Uniform and modular primary hippocampal cultures from embryonic rats were grown on commercially available micro-electrode arrays to investigate network activity with respect to development and integration of different neuronal populations. Modular networks consisting of two confined active and inter-connected sub-populations of neurons were realized by means of bi-compartmental polydimethylsiloxane structures. Spontaneous activity in both uniform and modular cultures was periodically monitored, from three up to eight weeks after plating. Compared to uniform cultures and despite lower cellular density, modular networks interestingly showed higher firing rates at earlier developmental stages, and network-wide firing and bursting statistics were less variable over time. Although globally less correlated than uniform cultures, modular networks exhibited also higher intra-cluster than inter-cluster correlations, thus demonstrating that segregation and integration of activity coexisted in this simple yet powerful in vitro model. Finally, the peculiar synchronized bursting activity shown by confined modular networks preferentially propagated within one of the two compartments (‘dominant’), even in cases of perfect balance of firing rate between the two sub-populations. This dominance was generally maintained during the entire monitored developmental frame, thus suggesting that the implementation of this hierarchy arose from early network development.

Local polymer replacement for neuron patterning and in situ neurite guidance

By locally dispensing poly-L-lysine (PLL) molecules with a FluidFM onto a protein and cell resistant poly-L-lysine-graft-polyethylene glycol (PLL-g-PEG) coated substrate, the antifouling layer can be replaced under the tip aperture by the cell adhesive PLL. We used this approach for guiding the adhesion and axonal outgrowth of embryonic hippocampal neurons in situ. Cultures of hippocampal neurons were chosen because they mostly contain pyramidal neurons. The hippocampus is known to be involved in memory formation, and the stages of network development are well characterized, which is an asset to fundamental research. After fabricating diffuse PLL spots with 10-250 μm diameter, seeded hippocampal cells stick preferentially onto the spots migrating toward the spot center along the PLL gradient. Cell clusters were formed depending on the lateral size of the PLL dots and the density of seeded cells. In a second step of this protocol, the FluidFM is used to connect in situ the obtained clusters. The outgrowth of neurites, which are known to grow preferentially on adhesive substrates, is tailored by writing PLL lines. Antibody staining confirms that the outgrowing neurites are mostly axons, while the activity of the neurons is assessed by a calcium indicator, proving cell viability. The calcium signal intensity of two actively interconnected clusters showed to be correlated, corroborating the formation of vectored and polarized interconnections.

Transfer entropy reconstruction and labeling of neuronal connections from simulated calcium imaging

Neuronal dynamics are fundamentally constrained by the underlying structural network architecture, yet much of the details of this synaptic connectivity are still unknown even in neuronal cultures in vitro. Here we extend a previous approach based on information theory, the Generalized Transfer Entropy, to the reconstruction of connectivity of simulated neuronal networks of both excitatory and inhibitory neurons. We show that, due to the model-free nature of the developed measure, both kinds of connections can be reliably inferred if the average firing rate between synchronous burst events exceeds a small minimum frequency. Furthermore, we suggest, based on systematic simulations, that even lower spontaneous inter-burst rates could be raised to meet the requirements of our reconstruction algorithm by applying a weak spatially homogeneous stimulation to the entire network. By combining multiple recordings of the same in silico network before and after pharmacologically blocking inhibitory synaptic transmission, we show then how it becomes possible to infer with high confidence the excitatory or inhibitory nature of each individual neuron.

Planar patch clamp for neuronal networks--considerations and future perspectives

The patch-clamp technique is generally accepted as the gold standard for studying ion channel activity allowing investigators to either "clamp" membrane voltage and directly measure transmembrane currents through ion channels, or to passively monitor spontaneously occurring intracellular voltage oscillations. However, this resulting high information content comes at a price. The technique is labor-intensive and requires highly trained personnel and expensive equipment. This seriously limits its application as an interrogation tool for drug development. Patch-clamp chips have been developed in the last decade to overcome the tedious manipulations associated with the use of glass pipettes in conventional patch-clamp experiments. In this chapter, we describe some of the main materials and fabrication protocols that have been developed to date for the production of patch-clamp chips. We also present the concept of a patch-clamp chip array providing high resolution patch-clamp recordings from individual cells at multiple sites in a network of communicating neurons. On this chip, the neurons are aligned with the aperture-probes using chemical patterning. In the discussion we review the potential use of this technology for pharmaceutical assays, neuronal physiology and synaptic plasticity studies.

Two-photon polymerization of sub-micrometric patterned surfaces: investigation of cell-substrate interactions and improved differentiation of neuron-like cells

Direct Laser Writing (DLW) is an innovative tool that allows the photofabrication of high resolution 3D structures, which can be successfully exploited for the study of the physical interactions between cells and substrates. In this work, we focused our attention on the topographical effects of submicrometric patterned surfaces fabricated via DLW on neuronal cell behavior. In particular, we designed, prepared, and characterized substrates based on aligned ridges for the promotion of axonal outgrowth and guidance. We demonstrated that both rat PC12 neuron-like cells and human SH-SY5Y derived neurons differentiate on parallel 2.5 μm spaced submicrometric ridges, being characterized by strongly aligned and significantly longer neurites with respect to those differentiated on flat control substrates, or on more spaced (5 and 10 μm) ridges. Furthermore, we detected an increased molecular differentiation toward neurons of the SH-SY5Y cells when grown on the submicrometric patterned substrates. Finally, we observed that the axons can exert forces able of bending the ridges, and we indirectly estimated the order of magnitude of these forces thanks to scanning probe techniques. Collectively, we showed as submicrometric structures fabricated by DLW can be used as a useful tool for the study of the axon mechanobiology.

Nano-volume drop patterning for rapid on-chip neuronal connect-ability assays

The ability of neurons to extend projections and to form physical connections among them (i.e., "connect-ability") is altered in several neuropathologies. The quantification of these alterations is an important read-out to investigate pathogenic mechanisms and for research and development of neuropharmacological therapies, however current morphological analysis methods are very time-intensive. Here, we present and characterize a novel on-chip approach that we propose as a rapid assay. Our approach is based on the definition on a neuronal cell culture substrate of discrete patterns of adhesion protein spots (poly-d-lysine, 23 ± 5 μm in diameter) characterized by controlled inter-spot separations of increasing distance (from 40 μm to 100 μm), locally adsorbed in an adhesion-repulsive agarose layer. Under these conditions, the connect-ability of wild type primary neurons from rodents is shown to be strictly dependent on the inter-spot distance, and can be rapidly documented by simple optical read-outs. Moreover, we applied our approach to identify connect-ability defects in neurons from a mouse model of 22q11.2 deletion syndrome/DiGeorge syndrome, by comparative trials with wild type preparations. The presented results demonstrate the sensitivity and reliability of this novel on-chip-based connect-ability approach and validate the use of this method for the rapid assessment of neuronal connect-ability defects in neuropathologies.

Structure-dynamics relationships in bursting neuronal networks revealed using a prediction framework

The question of how the structure of a neuronal network affects its functionality has gained a lot of attention in neuroscience. However, the vast majority of the studies on structure-dynamics relationships consider few types of network structures and assess limited numbers of structural measures. In this in silico study, we employ a wide diversity of network topologies and search among many possibilities the aspects of structure that have the greatest effect on the network excitability. The network activity is simulated using two point-neuron models, where the neurons are activated by noisy fluctuation of the membrane potential and their connections are described by chemical synapse models, and statistics on the number and quality of the emergent network bursts are collected for each network type. We apply a prediction framework to the obtained data in order to find out the most relevant aspects of network structure. In this framework, predictors that use different sets of graph-theoretic measures are trained to estimate the activity properties, such as burst count or burst length, of the networks. The performances of these predictors are compared with each other. We show that the best performance in prediction of activity properties for networks with sharp in-degree distribution is obtained when the prediction is based on clustering coefficient. By contrast, for networks with broad in-degree distribution, the maximum eigenvalue of the connectivity graph gives the most accurate prediction. The results shown for small () networks hold with few exceptions when different neuron models, different choices of neuron population and different average degrees are applied. We confirm our conclusions using larger () networks as well. Our findings reveal the relevance of different aspects of network structure from the viewpoint of network excitability, and our integrative method could serve as a general framework for structure-dynamics studies in biosciences.

In vitro large-scale experimental and theoretical studies for the realization of bi-directional brain-prostheses

Brain-machine interfaces (BMI) were born to control “actions from thoughts” in order to recover motor capability of patients with impaired functional connectivity between the central and peripheral nervous system. The final goal of our studies is the development of a new proof-of-concept BMI—a neuromorphic chip for brain repair—to reproduce the functional organization of a damaged part of the central nervous system. To reach this ambitious goal, we implemented a multidisciplinary “bottom-up” approach in which in vitro networks are the paradigm for the development of an in silico model to be incorporated into a neuromorphic device. In this paper we present the overall strategy and focus on the different building blocks of our studies: (i) the experimental characterization and modeling of “finite size networks” which represent the smallest and most general self-organized circuits capable of generating spontaneous collective dynamics; (ii) the induction of lesions in neuronal networks and the whole brain preparation with special attention on the impact on the functional organization of the circuits; (iii) the first production of a neuromorphic chip able to implement a real-time model of neuronal networks. A dynamical characterization of the finite size circuits with single cell resolution is provided. A neural network model based on Izhikevich neurons was able to replicate the experimental observations. Changes in the dynamics of the neuronal circuits induced by optical and ischemic lesions are presented respectively for in vitro neuronal networks and for a whole brain preparation. Finally the implementation of a neuromorphic chip reproducing the network dynamics in quasi-real time (10 ns precision) is presented.

Simple and effective graphene laser processing for neuron patterning application

A straightforward fabrication technique to obtain patterned substrates promoting ordered neuron growth is presented. Chemical vapor deposition (CVD) single layer graphene (SLG) was machined by means of single pulse UV laser ablation technique at the lowest effective laser fluence in order to minimize laser damage effects. Patterned substrates were then coated with poly-D-lysine by means of a simple immersion in solution. Primary embryonic hippocampal neurons were cultured on our substrate, demonstrating an ordered interconnected neuron pattern mimicking the pattern design. Surprisingly, the functionalization is more effective on the SLG, resulting in notably higher alignment for neuron adhesion and growth. Therefore the proposed technique should be considered a valuable candidate to realize a new generation of highly specialized biosensors.