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

Investigating the reliability of the evoked response in human iPSCs-derived neuronal networks coupled to micro-electrode arrays

In vitro models of neuronal networks have emerged as a potent instrument for gaining deeper insights into the intricate mechanisms governing the human brain. Notably, the integration of human-induced pluripotent stem cells (hiPSCs) with micro-electrode arrays offers a means to replicate and dissect both the structural and functional elements of the human brain within a controlled in vitro environment. Given that neuronal communication relies on the emission of electrical (and chemical) stimuli, the employment of electrical stimulation stands as a mean to comprehensively interrogate neuronal assemblies, to better understand their inherent electrophysiological dynamics. However, the establishment of standardized stimulation protocols for cultures derived from hiPSCs is still lacking, thereby hindering the precise delineation of efficacious parameters to elicit responses. To fill this gap, the primary objective of this study resides in delineating effective parameters for the electrical stimulation of hiPSCs-derived neuronal networks, encompassing the determination of voltage amplitude and stimulation frequency able to evoke reliable and stable responses. This study represents a stepping-stone in the exploration of efficacious stimulation parameters, thus broadening the electrophysiological activity profiling of neural networks sourced from human-induced pluripotent stem cells.

Biomechanics, motor control and dynamic models of the soft limbs of the octopus and other cephalopods

Muscular hydrostats are organs composed entirely of packed arrays of incompressible muscles and lacking any skeletal support. Found in both vertebrates and invertebrates, they are of great interest for comparative biomechanics from engineering and evolutionary perspectives. The arms of cephalopods (e.g. octopus and squid) are particularly interesting muscular hydrostats because of their flexibility and ability to generate complex behaviors exploiting elaborate nervous systems. Several lines of evidence from octopus studies point to the use of both brain and arm-embedded motor control strategies that have evolved to simplify the complexities associated with the control of flexible and hyper-redundant limbs and bodies. Here, we review earlier and more recent experimental studies on octopus arm biomechanics and neural motor control. We review several dynamic models used to predict the kinematic characteristics of several basic motion primitives, noting the shortcomings of the current models in accounting for behavioral observations. We also discuss the significance of impedance (stiffness and viscosity) in controlling the octopus's motor behavior. These factors are considered in light of several new models of muscle biomechanics that could be used in future research to gain a better understanding of motor control in the octopus. There is also a need for updated models that encompass stiffness and viscosity for designing and controlling soft robotic arms. The field of soft robotics has boomed over the past 15 years and would benefit significantly from further progress in biomechanical and motor control studies on octopus and other muscular hydrostats.

How octopus arm muscle contractile properties and anatomical organization contribute to the arm functional specialization

Octopus arms are highly flexible structures capable of complex motions and are used in a wide repertoire of behaviors. Movements are generated by the coordinated summation of innervation signals to packed arrays of muscles oriented in different directions and moving based on their anatomical relationships. In this study, we investigated the interplay between muscle biomechanics and anatomical organization in the Octopus vulgaris arm to elucidate their role in different arm movements. We performed isometric and isotonic force measurements on isolated longitudinal (L) and transverse (T) arm muscles and showed that L has a higher rate of activation and relaxation, lower twitch-to-tetanus ratio, and lower passive tension than T muscles, thus prompting their use as faster and slower muscles, respectively. This points to the use of L in more graded responses, such as those involved in precise actions, and T in intense and sustained actions, such as motion stabilization and posture maintenance. Once activated, the arm muscles exert forces that cause deformations of the entire arm, which are determined by the amount, location, properties and orientation of their fibers. Here, we show that, although continuous, the arm manifests a certain degree of morphological specialization, where the arm muscles have a different aspect ratio along the arm. This possibly supports the functional specialization of arm portion observed in various motions, such as fetching and crawling. Hence, the octopus arm as a whole can be seen as a 'reservoir' of possibilities where different types of motion may emerge at the limb level through the co-option of the muscle contractile properties and structural arrangement.

Beyond muscles: role of intramuscular connective tissue elasticity and passive stiffness in octopus arm muscle function

The octopus arm is a 'one of a kind' muscular hydrostat, as demonstrated by its high maneuverability and complexity of motions. It is composed of a complex array of muscles and intramuscular connective tissue, allowing force and shape production. In this study, we investigated the organization of the intramuscular elastic fibers in two main muscles composing the arm bulk: the longitudinal (L) and the transverse (T) muscles. We assessed their contribution to the muscles' passive elasticity and stiffness and inferred their possible roles in limb deformation. First, we performed confocal imaging of whole-arm samples and provided evidence of a muscle-specific organization of elastic fibers (more chaotic and less coiled in T than in L). We next showed that in an arm at rest, L muscles are maintained under 20% compression and T muscles under 30% stretching. Hence, tensional stresses are inherently present in the arm and affect the strain of elastic fibers. Because connective tissue in muscles is used to transmit stress and store elastic energy, we investigated the contribution of elastic fibers to passive forces using step-stretch and sinusoidal length-change protocols. We observed a higher viscoelasticity of L and a higher stiffness of T muscles, in line with their elastic fiber configurations. This suggests that L might be involved in energy storage and damping, whereas T is involved in posture maintenance and resistance to deformation. The elastic fiber configuration thus supports the specific role of muscles during movement and may contribute to the mechanics, energetics and control of arm motion.

Plasticity and Adaptation in Neuromorphic Biohybrid Systems

Neuromorphic systems take inspiration from the principles of biological information processing to form hardware platforms that enable the large-scale implementation of neural networks. The recent years have seen both advances in the theoretical aspects of spiking neural networks for their use in classification and control tasks and a progress in electrophysiological methods that is pushing the frontiers of intelligent neural interfacing and signal processing technologies. At the forefront of these new technologies, artificial and biological neural networks are tightly coupled, offering a novel "biohybrid" experimental framework for engineers and neurophysiologists. Indeed, biohybrid systems can constitute a new class of neuroprostheses opening important perspectives in the treatment of neurological disorders. Moreover, the use of biologically plausible learning rules allows forming an overall fault-tolerant system of co-developing subsystems. To identify opportunities and challenges in neuromorphic biohybrid systems, we discuss the field from the perspectives of neurobiology, computational neuroscience, and neuromorphic engineering.

Slow-Wave Recordings From Micro-Sized Neural Clusters Using Multiwell Type Microelectrode Arrays

OBJECTIVE

The use of microelectrode array (MEA) recordings is a very effective neurophysiological method because it is able to continuously and noninvasively obtain the spatiotemporal information of electrical activity from many neurons constituting a neural network. Very recently, studies have been published that used MEAs for the measurement of a low-frequency component of electrical activity as an indicator of diverse activity of cultured neurons. The occurrence of low-frequency activities has electrophysiological information that does not include the information from fast spikes. However, there is no in vitro experimental model suitable for measuring the low-frequency activities (slow-waves) for further study.

METHODS

Neural clusters consisting of dozens of neurons were placed directly onto each electrode of an MEA from which fast spikes and slow-waves were measured.

RESULTS

We obtained sufficient data on the early development patterns of the slow-waves and the spikes measured from many independent neural clusters confirming that the slow-waves occurred first before the emergence of the spikes in the neural clusters. We also showed that changes in the occurrence frequency of the slow-waves for synaptic blockers were measured from a large number of independent cultures.

CONCLUSION

Microsized neural cluster arrays, which can be combined with conventional MEAs, are suitable for multiple simultaneous recordings of slow-waves.

SIGNIFICANCE

Our technology provides a simple but useful method to study the generation of a low-frequency component of the electrical activity in cultured neural networks that are not yet well known as well as to expand the use of conventional MEAs.

In vitro studies of neuronal networks and synaptic plasticity in invertebrates and in mammals using multielectrode arrays

Brain functions are strictly dependent on neural connections formed during development and modified during life. The cellular and molecular mechanisms underlying synaptogenesis and plastic changes involved in learning and memory have been analyzed in detail in simple animals such as invertebrates and in circuits of mammalian brains mainly by intracellular recordings of neuronal activity. In the last decades, the evolution of techniques such as microelectrode arrays (MEAs) that allow simultaneous, long-lasting, noninvasive, extracellular recordings from a large number of neurons has proven very useful to study long-term processes in neuronal networks in vivo and in vitro. In this work, we start off by briefly reviewing the microelectrode array technology and the optimization of the coupling between neurons and microtransducers to detect subthreshold synaptic signals. Then, we report MEA studies of circuit formation and activity in invertebrate models such as Lymnaea, Aplysia, and Helix. In the following sections, we analyze plasticity and connectivity in cultures of mammalian dissociated neurons, focusing on spontaneous activity and electrical stimulation. We conclude by discussing plasticity in closed-loop experiments.

Long-term optical stimulation of channelrhodopsin-expressing neurons to study network plasticity

Neuronal plasticity produces changes in excitability, synaptic transmission, and network architecture in response to external stimuli. Network adaptation to environmental conditions takes place in time scales ranging from few seconds to days, and modulates the entire network dynamics. To study the network response to defined long-term experimental protocols, we setup a system that combines optical and electrophysiological tools embedded in a cell incubator. Primary hippocampal neurons transduced with lentiviruses expressing channelrhodopsin-2/H134R were subjected to various photostimulation protocols in a time window in the order of days. To monitor the effects of light-induced gating of network activity, stimulated transduced neurons were simultaneously recorded using multi-electrode arrays (MEAs). The developed experimental model allows discerning short-term, long-lasting, and adaptive plasticity responses of the same neuronal network to distinct stimulation frequencies applied over different temporal windows.