Engineered Neuronal Circuits: A New Platform for Studying the Role of Modular Topology

Neuron–glia cultures serve as a valuable model system for exploring the bio-molecular activity of single cells. Since neurons in culture can be conveniently recorded with great fidelity from many sites simultaneously, it has long been suggested that uniform cultured neurons may also be used to investigate network-level mechanisms pertinent to information processing, activity propagation, memory, and learning. But how much of the functionality of neural circuits can be retained in vitro remains an open question. Recent studies utilizing patterned networks suggest that they provide a most useful platform to address fundamental questions in neuroscience. Here we review recent efforts in the realm of patterned networks’ activity investigations. We give a brief overview of the patterning methods and experimental approaches commonly employed in the field, and summarize the main results reported in the literature. The general picture that emerges from these reports indicates that patterned networks with uniform connectivity do not exhibit unique activity patterns. Rather, their activity is very similar to that of unpatterned uniform networks. However, by breaking the connectivity homogeneity, using a modular architecture, it is possible to introduce pronounced topology-related gating and delay effects. These findings suggest that patterned cultured networks may serve as a new platform for studying the role of modularity in neuronal circuits

Innate Synchronous Oscillations in Freely-Organized Small Neuronal Circuits

BACKGROUND: Information processing in neuronal networks relies on the network's ability to generate temporal patterns of action potentials. Although the nature of neuronal network activity has been intensively investigated in the past several decades at the individual neuron level, the underlying principles of the collective network activity, such as the synchronization and coordination between neurons, are largely unknown. Here we focus on isolated neuronal clusters in culture and address the following simple, yet fundamental questions: What is the minimal number of cells needed to exhibit collective dynamics? What are the internal temporal characteristics of such dynamics and how do the temporal features of network activity alternate upon crossover from minimal networks to large networks? METHODOLOGY/PRINCIPAL FINDINGS: We used network engineering techniques to induce self-organization of cultured networks into neuronal clusters of different sizes. We found that small clusters made of as few as 40 cells already exhibit spontaneous collective events characterized by innate synchronous network oscillations in the range of 25 to 100 Hz. The oscillation frequency of each network appeared to be independent of cluster size. The duration and rate of the network events scale with cluster size but converge to that of large uniform networks. Finally, the investigation of two coupled clusters revealed clear activity propagation with master/slave asymmetry. CONCLUSIONS/SIGNIFICANCE: The nature of the activity patterns observed in small networks, namely the consistent emergence of similar activity across networks of different size and morphology, suggests that neuronal clusters self-regulate their activity to sustain network bursts with internal oscillatory features. We therefore suggest that clusters of as few as tens of cells can serve as a minimal but sufficient functional network, capable of sustaining oscillatory activity. Interestingly, the frequencies of these oscillations are similar those observed in vivo

Locations of 96 areas used for entry rate analysis.

Each dot represents the center of an area with the same shape and size as the reinforced areas. Green, orange, and blue circles show the area of the feeder, the second and the third reinforced areas, respectively. Colored rectangles represent the areas neighboring each of the reinforced areas (marked in Fig 4G–4I). (PDF)</p

Excel sheet with individual data points presented in the figures.

The excel sheet contains different tabs, each including data for individual data points in all relevant panels of a specific figure. (XLSX)</p

ReptiLearn Web UI.

(A) Video settings window showing the parameters of an ImageSource. (B) New session dialog. The session uses the spatial learning Experiment class found in module/system/experiments/loclearn2.py. Session id determines the directory name in which data is to be stored. (C) The arena menu listing every configured arena controller interface. Feeder items can be clicked on to release a reward. Toggle interface items can be switched on or off. Sensor interface items display their most current measurement. (D) Session UI section displaying the current session name and the time of creation at the top. Located below is the session control bar that allows to start and stop the experiment and to control the current trial and block. Session and block parameters can be set using the editors in the bottom tabs. (PDF)</p

Price list for arena components.

All components, including the visible light cameras, are relatively low cost. An exception to this is the PC which is necessary to facilitate real-time processing. Another exception is the thermal camera that is not required if thermal monitoring is not a part of the experimental design. (DOCX)</p

ReptiLearn software architecture.

The software consists of an image processing and video recording system, an Experiment class controlling the current experiment session, a state store used for synchronizing different processes, data loggers, and an MQTT client responsible for communicating with the arena controller, touch screen app, and other external software. The HTTP/WebSocket server facilitates real-time monitoring and control of the software. The arena controller handles communication with Arduino boards that control arena hardware components. (PDF)</p

Statistics for entry rate correlations across animals.

Mann–Whitney U statistics for the distributions of correlation coefficients of the difference in entry rate as a function of distance from each area, for all simulated and real areas. Feeder areas were excluded from the correlation calculation. (DOCX)</p

Features and design of the ReptiLearn behavioral system.

(A) A schematic of the features supported by ReptiLearn. ReptiLearn is written in Python, provides an API for automating experimental tasks, runs real-time processing, controls arena hardware components including live food dispensers and a heat reward system, collects time-series data, features a web-based user interface for remote monitoring and control. (B) Diagram of hardware components in ReptiLearn. The arena includes synchronized visual and thermal cameras, temperature sensors, live prey feeders, a grid of 12 heat lamps covering the arena, illumination LEDs and a touchscreen. The hardware is controlled using Arduino boards and designed with generic interfaces for diverse research needs. ReptiLearn can run with different subsets of the above components. (C) A schematic illustrating the real-time closed loop processing in ReptiLearn. ReptiLearn allows implementing closed-loop behavioral tasks linking any of the following features in real-time: Animal and ambient temperature, animal position and posture, animal screen touches, live prey or heat reward, and visual stimulation on the screen. (D) Screenshot of the web-based user interface. The interface allows monitoring the cameras (top left) and the status of hardware (state panel on the right) as well as controlling the arena (top menu) and experiments remotely (experimental design panel on the right). Experiment events and system information appear in the log (bottom).</p