Carbon nanotube-based neurochips.

High-density carbon nanotube (CNT)-coated surfaces are highly neuro-adhesive. When shaped into regular arrays of isolated islands on a non-adhesive support substrate (such as a clean glass), CNTs can function as effective encoring sites for neurons and glia cells for in-vitro applications. Primarily, patterned CNT islands provide a means to form complex, engineered, interconnected neuronal networks with pre-designed geometry via utilizing the self-assembly process of neurons. Depositing these CNT islands onto multielectrode array chip can facilitate both cell anchoring but also electrical interfacing between the electrodes and the neurons.

One-to-one neuron-electrode interfacing.

The question of neuronal network development and organization is a principle one, which is closely related to aspects of neuronal and network form-function interactions. In-vitro two-dimensional neuronal cultures have proved to be an attractive and successful model for the study of these questions. Research is constraint however by the search for techniques aimed at culturing stable networks, whose electrical activity can be reliably and consistently monitored. A simple approach to form small interconnected neuronal circuits while achieving one-to-one neuron-electrode interfacing is presented. Locust neurons were cultured on a novel bio-chip consisting of carbon-nanotube multi-electrode-arrays. The cells self-organized to position themselves in close proximity to the bio-chip electrodes. The organization of the cells on the electrodes was analyzed using time lapse microscopy, fluorescence imaging and scanning electron microscopy. Electrical recordings from well identified cells is presented and discussed. The unique properties of the bio-chip and the specific neuron-nanotube interactions, together with the use of relatively large insect ganglion cells, allowed long-term stabilization (as long as 10 days) of predefined neural network topology as well as high fidelity electrical recording of individual neuron firing. This novel preparation opens ample opportunity for future investigation into key neurobiological questions and principles.

Carbon nanotube electrodes for effective interfacing with retinal tissue.

We have investigated the use of carbon nanotube coated microelectrodes as an interface material for retinal recording and stimulation applications. Test devices were micro-fabricated and consisted of 60, 30 mum diameter electrodes at spacing of 200 mum. These electrodes were coated via chemical vapor deposition of carbon nanotubes, resulting in conducting, three dimensional surfaces with a high interfacial area. These attributes are important both for the quality of the cell-surface coupling as well as for electro-chemical interfacing efficiency. The entire chip was packaged to fit a commercial multielectrode recording and stimulation system. Electrical recordings of spontaneous spikes from whole-mount neonatal mouse retinas were consistently obtained minutes after retinas were placed over the electrodes, exhibiting typical bursting and propagating waves. Most importantly, the signals obtained with carbon nanotube electrodes have exceptionally high signal to noise ratio, reaching values as high as 75. Moreover, spikes are marked by a conspicuous gradual increase in amplitude recorded over a period of minutes to hours, suggesting improvement in cell-electrode coupling. This phenomenon is not observed in conventional commercial electrodes. Electrical stimulation using carbon nanotube electrodes was also achieved. We attribute the superior performances of the carbon nanotube electrodes to their three dimensional nature and the strong neuro-carbon nanotube affinity. The results presented here show the great potential of carbon nanotube electrodes for retinal interfacing applications. Specifically, our results demonstrate a route to achieve a reduction of the electrode down to few micrometers in order to achieve high efficacy local stimulation needed in retinal prosthetic devices.

The emergence and properties of mutual synchronization in in vitro coupled cortical networks.

We have studied the emergence of mutual synchronization and activity propagation in coupled neural networks from rat cortical cells grown on a micro-electrode array for parallel activity recording of dozens of neurons. The activity of each sub-network by itself is marked by the formation of synchronized bursting events (SBE) - short time windows of rapid neuronal firing. The joint activity of two coupled networks is characterized by the formation of mutual synchronization, i.e. the formation of SBE whose activity starts at one sub-network and then propagates to the other. The sub-networks switch roles in initiating the mutual SBE. However, spontaneous propagation (initiation) asymmetry emerges - one of the sub-networks takes on the role of initiating substantially more mutual SBE than the other, despite the fact that the two are engineered to be similar in size and cell density. Analysis of the interneuron correlations in the SBE also reveals the emergence of activity (function) asymmetry - one sub-network develops a more organized structure of correlations. We also show activity propagation and mutual synchronization in four coupled networks. Using computer simulations, we propose that the function asymmetry reflects asymmetry between the internal connectivity of the two networks, whereas the propagation asymmetry reflects asymmetry in the connectivity between the sub-networks. These results agree with the experimental findings that the initiation and function asymmetry can be separately regulated, which implies that information transfer (activity propagation) and information processing (function) can be regulated separately in coupled neural networks.

Management of synchronized network activity by highly active neurons.

Increasing evidence supports the idea that spontaneous brain activity may have an important functional role. Cultured neuronal networks provide a suitable model system to search for the mechanisms by which neuronal spontaneous activity is maintained and regulated. This activity is marked by synchronized bursting events (SBEs)--short time windows (hundreds of milliseconds) of rapid neuronal firing separated by long quiescent periods (seconds). However, there exists a special subset of rapidly firing neurons whose activity also persists between SBEs. It has been proposed that these highly active (HA) neurons play an important role in the management (i.e. establishment, maintenance and regulation) of the synchronized network activity. Here, we studied the dynamical properties and the functional role of HA neurons in homogeneous and engineered networks, during early network development, upon recovery from chemical inhibition and in response to electrical stimulations. We found that their sequences of inter-spike intervals (ISI) exhibit long time correlations and a unimodal distribution. During the network's development and under intense inhibition, the observed activity follows a transition period during which mostly HA neurons are active. Studying networks with engineered geometry, we found that HA neurons are precursors (the first to fire) of the spontaneous SBEs and are more responsive to electrical stimulations.

Study of hypothermia on cultured neuronal networks using multi-electrode arrays.

Efficient and safe use of hypothermia during various neuro-medical procedures requires sound understanding of low temperature effects on the neuronal network's activity. In this report, we introduce the use of cultivated dissociated neuronal networks on temperature controlled multi-electrode arrays (MEAs) as a simple methodology for studying the long-term effects of hypothermia. The networks exhibit spontaneous activity in the form of synchronized bursting events (SBEs), followed by long intervals of sporadic firing. Through the use of our correlation method, these SBEs can be clustered into sub-groups of similar spatio-temporal patterns. Application of hypothermia to the network resulted in a reduction in the SBE rate, the spike intensity and an increase in inter-neuronal correlations. Within 2h following the cessation of hypothermia, the cultured network returned to its initial spatio-temporal SBE structure. These results suggest that the network survived cold exposure and demonstrate the feasibility of long-term continuous neural network recording during hypothermic conditions.

Functional holography analysis: simplifying the complexity of dynamical networks.

We present a novel functional holography (FH) analysis devised to study the dynamics of task-performing dynamical networks. The latter term refers to networks composed of dynamical systems or elements, like gene networks or neural networks. The new approach is based on the realization that task-performing networks follow some underlying principles that are reflected in their activity. Therefore, the analysis is designed to decipher the existence of simple causal motives that are expected to be embedded in the observed complex activity of the networks under study. First we evaluate the matrix of similarities (correlations) between the activities of the network's components. We then perform collective normalization of the similarities (or affinity transformation) to construct a matrix of functional correlations. Using dimension reduction algorithms on the affinity matrix, the matrix is projected onto a principal three-dimensional space of the leading eigenvectors computed by the algorithm. To retrieve back information that is lost in the dimension reduction, we connect the nodes by colored lines that represent the level of the similarities to construct a holographic network in the principal space. Next we calculate the activity propagation in the network (temporal ordering) using different methods like temporal center of mass and cross correlations. The causal information is superimposed on the holographic network by coloring the nodes locations according to the temporal ordering of their activities. First, we illustrate the analysis for simple, artificially constructed examples. Then we demonstrate that by applying the FH analysis to modeled and real neural networks as well as recorded brain activity, hidden causal manifolds with simple yet characteristic geometrical and topological features are deciphered in the complex activity. The term "functional holography" is used to indicate that the goal of the analysis is to extract the maximum amount of functional information about the dynamical network as a whole unit.