NeuroPod: a real-time neuromorphic spiking CPG applied to robotics

Initially, robots were developed with the aim of making our life easier, carrying out repetitive or dangerous tasks for humans. Although they were able to perform these tasks, the latest generation of robots are being designed to take a step further, by performing more complex tasks that have been carried out by smart animals or humans up to date. To this end, inspiration needs to be taken from biological examples. For instance, insects are able to optimally solve complex environment navigation problems, and many researchers have started to mimic how these insects behave. Recent interest in neuromorphic engineering has motivated us to present a real-time, neuromorphic, spike-based Central Pattern Generator of application in neurorobotics, using an arthropod-like robot. A Spiking Neural Network was designed and implemented on SpiNNaker. The network models a complex, online-change capable Central Pattern Generator which generates three gaits for a hexapod robot locomotion. Recon gurable hardware was used to manage both the motors of the robot and the real-time communication interface with the Spiking Neural Networks. Real-time measurements con rm the simulation results, and locomotion tests show that NeuroPod can perform the gaits without any balance loss or added delay.Ministerio de Economía y Competitividad TEC2016-77785-

Real-time biomimetic Central Pattern Generators in an FPGA for hybrid experiments

This investigation of the leech heartbeat neural network system led to the development of a low resources, real-time, biomimetic digital hardware for use in hybrid experiments. The leech heartbeat neural network is one of the simplest central pattern generators (CPG). In biology, CPG provide the rhythmic bursts of spikes that form the basis for all muscle contraction orders (heartbeat) and locomotion (walking, running, etc.). The leech neural network system was previously investigated and this CPG formalized in the Hodgkin–Huxley neural model (HH), the most complex devised to date. However, the resources required for a neural model are proportional to its complexity. In response to this issue, this article describes a biomimetic implementation of a network of 240 CPGs in an FPGA (Field Programmable Gate Array), using a simple model (Izhikevich) and proposes a new synapse model: activity-dependent depression synapse. The network implementation architecture operates on a single computation core. This digital system works in real-time, requires few resources, and has the same bursting activity behavior as the complex model. The implementation of this CPG was initially validated by comparing it with a simulation of the complex model. Its activity was then matched with pharmacological data from the rat spinal cord activity. This digital system opens the way for future hybrid experiments and represents an important step toward hybridization of biological tissue and artificial neural networks. This CPG network is also likely to be useful for mimicking the locomotion activity of various animals and developing hybrid experiments for neuroprosthesis development

An in vivo Assay for Simultaneous Monitoring of Neuronal Activity and Behavioral Output in the Stomatogastric Nervous System of Decapod Crustaceans

Central pattern generators (CPGs) generate rhythmic output patterns and drive vital behaviors such as breathing, swallowing, locomotion and chewing 1-10. While most insights into the rhythm generating mechanisms of CPGs have been derived from isolated nervous system preparations, the relationship between neural activity and corresponding behavioral expression is often unclear. The stomatogastric system of decapod crustaceans is one of the best characterized neural system for motor pattern generation 9-12 and many mechanisms of motor pattern generation and selection have been discovered in this system. Since most studies are limited to the isolated nervous system, little is known about the actual behavioral output of this system. For example, it is unknown whether the observed flexibility in the motor patterns is present in vivo and whether distinct motor activities drive corresponding behavioral patterns. We present a method which allows electrophysiological recordings of CPG neurons and the simultaneous monitoring of the behavioral output of the stomatogastric nervous system. For this, we use extracellular hook electrodes either for recording or stimulation of neurons in the gastric mill CPG that drive the chewing movements of three teeth in the foregut of the animal. Electrodes are applied in tethered, but otherwise fully intact crabs (Cancer pagurus) and an endoscope is used to monitor tooth movements. Nerve and video recordings of the endoscopic camera are synchronized and motion tracking techniques are used to analyze gastric mill movements. This approach thus allows testing the behavioral relevance of the neural activity patterns produced by central pattern generators

Optical Dissection of Neural Circuits Responsible for Drosophila Larval Locomotion with Halorhodopsin

Halorhodopsin (NpHR), a light-driven microbial chloride pump, enables silencing of neuronal function with superb temporal and spatial resolution. Here, we generated a transgenic line of Drosophila that drives expression of NpHR under control of the Gal4/UAS system. Then, we used it to dissect the functional properties of neural circuits that regulate larval peristalsis, a continuous wave of muscular contraction from posterior to anterior segments. We first demonstrate the effectiveness of NpHR by showing that global and continuous NpHR-mediated optical inhibition of motor neurons or sensory feedback neurons induce the same behavioral responses in crawling larvae to those elicited when the function of these neurons are inhibited by Shibirets, namely complete paralyses or slowed locomotion, respectively. We then applied transient and/or focused light stimuli to inhibit the activity of motor neurons in a more temporally and spatially restricted manner and studied the effects of the optical inhibition on peristalsis. When a brief light stimulus (1–10 sec) was applied to a crawling larva, the wave of muscular contraction stopped transiently but resumed from the halted position when the light was turned off. Similarly, when a focused light stimulus was applied to inhibit motor neurons in one or a few segments which were about to be activated in a dissected larva undergoing fictive locomotion, the propagation of muscular constriction paused during the light stimulus but resumed from the halted position when the inhibition (>5 sec) was removed. These results suggest that (1) Firing of motor neurons at the forefront of the wave is required for the wave to proceed to more anterior segments, and (2) The information about the phase of the wave, namely which segment is active at a given time, can be memorized in the neural circuits for several seconds

Dendritic Cell Migration and Traction Force Generation in Engineered Microenvironments

Dendritic cells (DCs) are potent initiators of the adaptive immune response. Their trafficking from sites of inflammation to lymphoid tissue is essential to their function. Exactly how dendritic cells integrate multiple chemotactic cues to organize an accurate migratory path is not fully understood. We first characterize DC random motility (chemokinesis) on extracellular matrix proteins in the presence of chemokines. Then, using a microfluidic device, we present both single and competing chemokine gradients to murine bone-marrow derived DCs in a controlled, time-invariant microenvironment. We show that in counter gradients, CCL19 is 10 to 100 fold more potent than other chemokines CCL21 or CXCL12. Interestingly, when the chemoattractive potencies of opposing gradients are matched, cells home to a central region in which the signals from multiple chemokines are balanced. These results provide fundamental insight into the processes that DCs use to migrate toward and position themselves within secondary lymphoid organs. We extended this work to a combination of the microfluidic gradient generator and micropost array detectors to develop a novel method for probing traction forces during chemotaxis. We find DC migration is driven by short-lived traction stresses at the leading edge or filopodia. We illustrate that spatiotemporal pattern of traction stresses can be used to predict changes in the direction of DC motion. Additionally, we determine the characteristic duration of local dendritic cell traction forces and correlate this duration with force. Overall, DCs show a mode of migration distinct from both mesenchymal cells and other leukocytes, characterized by rapid turnover of traction forces in leading filopodia. In this thesis, we extend the current understanding of DC motility to include signal integration and traction forces

Digital neural circuits : from ions to networks

PhD ThesisThe biological neural computational mechanism is always fascinating to human beings since it shows several state-of-the-art characteristics: strong fault tolerance, high power efficiency and self-learning capability. These behaviours lead the developing trend of designing the next-generation digital computation platform. Thus investigating and understanding how the neurons talk with each other is the key to replicating these calculation features. In this work I emphasize using tailor-designed digital circuits for exactly implementing bio-realistic neural network behaviours, which can be considered a novel approach to cognitive neural computation. The first advance is that biological real-time computing performances allow the presented circuits to be readily adapted for real-time closed-loop in vitro or in vivo experiments, and the second one is a transistor-based circuit that can be directly translated into an impalpable chip for high-level neurologic disorder rehabilitations. In terms of the methodology, first I focus on designing a heterogeneous or multiple-layer-based architecture for reproducing the finest neuron activities both in voltage-and calcium-dependent ion channels. In particular, a digital optoelectronic neuron is developed as a case study. Second, I focus on designing a network-on-chip architecture for implementing a very large-scale neural network (e.g. more than 100,000) with human cognitive functions (e.g. timing control mechanism). Finally, I present a reliable hybrid bio-silicon closed-loop system for central pattern generator prosthetics, which can be considered as a framework for digital neural circuit-based neuro-prosthesis implications. At the end, I present the general digital neural circuit design principles and the long-term social impacts of the presented work