The need for processing at the edge the increasing amount of data that is being produced by multitudes of sensors has led to the demand for mode power-efficient computational systems, by exploring alternative computing paradigms and technologies. Neuromorphic engineering is a promising approach that can address this need by developing electronic systems that faithfully emulate the computational properties of animal brains. In particular, the hippocampus stands out as one of the most relevant brain region for implementing auto associative memories capable of learning large amounts of information quickly and recalling it efficiently. In this work, we present a computational spike-based memory model inspired by the hippocampus that takes advantage of the features of analog electronic circuits: energy efficiency, compactness, and real-time operation. This model can learn memories, recall them from a partial fragment and forget. It has been implemented as a Spiking Neural Networks directly on a mixed-signal neuromorphic chip. We describe the details of the hardware implementation and demonstrate its operation via a series of benchmark experiments, showing how this research prototype paves the way for the development of future robust and low-power mixed-signal neuromorphic processing systems.

In robotics, one of the most common tasks involves executing trajectories to reach a specific target in space; but, such movements, and consequently the trajectories, frequently exhibit oscillatory or non-uniform behavior around the target point within a continuous trajectory. Neuromorphic engineering seeks to integrate the computational mechanisms observed in animal brains into contemporary technological systems. Neuromorphic engineering has been successfully applied in fields such as robotics, autonomous systems, edge computing, and healthcare. In robotics, it efficiently addresses challenges like dynamic control in path planning. By adopting this strategy, in this work, we present a space-time smooth control mechanism for closed-loop spiking robot arms. The system is based on a spiking neural network inspired by the structure and function of the nervous system. The proposed neural network has been implemented on the mixed analog-digital signal special purpose hardware platform DYNAP-SE2. A set of experiments has been conducted to test the system on both forward and reverse reference trajectories for different waiting times in the interpolation carried out by the FPGA-based control. A tunable smoothing of the trajectory has been achieved for one joint of the robotic arm. Given the temporal limitations of the hardware setup, the optimal interval between interpolated points is 8 ms.

Neuromorphic engineering aims to incorporate the computational principles found in animal brains, into modern technological systems. Following this approach, in this work we propose a closed-loop neuromorphic control system for an event-based robotic arm. The proposed system consists of a shifted Winner-Take-All spiking network for interpolating a reference trajectory and a spiking comparator network responsible for controlling the flow continuity of the trajectory, which is fed back to the actual position of the robot. The comparator model is based on a differential position comparison neural network, which governs the execution of the next trajectory points to close the control loop between both components of the system. To evaluate the system, we implemented and deployed the model on a mixed-signal analog-digital neuromorphic platform, the DYNAP-SE2, to facilitate integration and communication with the ED-Scorbot robotic arm platform. Experimental results on one joint of the robot validate the use of this architecture and pave the way for future neuro-inspired control of the entire robot.

Neuromorphic engineering endeavors to integrate the computational prowess and efficiency inherent in biological neuronal systems, such as the brain, into contemporary technological systems, primarily through the deployment of spiking neural networks. This research delineates the development and implementation of a bio-inspired sequential hippocampus memory model, which can effectively learn and sequentially recall memories, within a robotic infrastructure. The hippocampus memory model, implemented on the SpiNNaker platform, has been tactically utilized to control a 4-joint event-based robot arm, the ED-ScorBot, by learning and then recalling trajectories via a sequence of memories regarding joint positions. The conveyed spiking information from SpiNNaker is interpreted by an FPGA in real-time to command the event-driven motors of the robotic arm, integrating learned trajectories into physical robotic movement. An empirical exploration validates the model’s capability to govern the robotic arm’s trajectory with precision and dependability while simultaneously demonstrating the potential for incorporating spike-based memory models in robotic applications. This synergistic convergence of neuromorphic engineering and robotics illustrates a viable pathway towards sophisticated, efficient, and adaptable robotic systems capable of learning and reproducing complex tasks, with significant implications for future developments in autonomous robotic applications.

The brain has computational capabilities that surpass those of modern systems, being able to solve complex problems efficiently in a simple way. Neuromorphic engineering aims to mimic biology in order to develop new systems capable of incorporating such capabilities. Bio-inspired learning systems continue to be a challenge that must be solved, and much work needs to be done in this regard. Among all brain regions, the hippocampus stands out as an autoassociative short-term memory with the capacity to learn and recall memories from any fragment of them. These characteristics make the hippocampus an ideal candidate for developing bio-inspired learning systems that, in addition, resemble content-addressable memories. Therefore, in this work we propose a bio-inspired spiking content-addressable memory model based on the CA3 region of the hippocampus with the ability to learn, forget and recall memories, both orthogonal and non-orthogonal, from any fragment of them. The model was implemented on the SpiNNaker hardware platform using Spiking Neural Networks. A set of experiments based on functional, stress and applicability tests were performed to demonstrate its correct functioning. This work presents the first hardware implementation of a fully-functional bio-inspired spiking hippocampal content-addressable memory model, paving the way for the development of future more complex neuromorphic systems.

The hippocampus acts as a short-term memory capable of recalling a previously-learned complete memory from a fragment of it. Inspired by the hippocampus and taking into account its input from the visual stream, a neuromorphic system capable of learning images and remembering them from a fragment of it in real time was developed. The system contains a bio-inspired hippocampal spiking memory that acts as an autoassociative network that, for each input image, associates the spatial activation patterns of the pixels of the image. A monitor has been designed to visualize the internal spiking activity of the network during the simulation.

One of the most interesting and still growing scientific fields is neuromorphic engineering, which is focused on studying and designing hardware and software with the purpose of mimicking the basic principles of biological nervous systems. Currently, there are many research groups developing practical applications based on neuroscientific knowledge. This work provides researchers with a novel toolkit of building blocks based on Spiking Neural Networks that emulate the behavior of different logic gates. These could be very useful in many spike-based applications, since logic gates are the basis of digital circuits. The designs and models proposed are presented and implemented on a SpiNNaker hardware platform. Different experiments were performed in order to validate the expected behavior, and the obtained results are discussed. The functionality of traditional logic gates and the proposed blocks is studied, and the feasibility of the presented approach is discussed.

The human brain is the most powerful and efficient machine in existence today, surpassing in many ways the capabilities of modern computers. Currently, lines of research in neuromorphic engineering are trying to develop hardware that mimics the functioning of the brain to acquire these superior capabilities. One of the areas still under development is the design of bio-inspired memories, where the hippocampus plays an important role. This region of the brain acts as a short-term memory with the ability to store associations of information from different sensory streams in the brain and recall them later. This is possible thanks to the recurrent collateral network architecture that constitutes CA3, the main sub-region of the hippocampus. In this work, we developed two spike-based computational models of fully functional hippocampal bio-inspired memories for the storage and recall of complex patterns implemented with spiking neural networks on the SpiNNaker hardware platform. These models present different levels of biological abstraction, with the first model having a constant oscillatory activity closer to the biological model, and the second one having an energy-efficient regulated activity, which, although it is still bio-inspired, opts for a more functional approach. Different experiments were performed for each of the models, in order to test their learning/recalling capabilities. A comprehensive comparison between the functionality and the biological plausibility of the presented models was carried out, showing their strengths and weaknesses. The two models, which are publicly available for researchers, could pave the way for future spike-based implementations and applications.