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.

Predictive maintenance (PdM) applications are becoming more and more popular, in part because Artificial Intelligence (AI) algorithms are becoming more powerful and affordable. However, this method requires a lot of data which need to be specific to the application. Only a few audio datasets on the topic are available, most of them are focused on generic systems, and none to the best of our knowledge has been created from a neuromorphic engineering approach, which offers benefits in terms of power consumption and robustness among others. We used a Neuromorphic Auditory Sensor (NAS) to record a neuromorphic audio dataset that covers two different scenarios of PdM in actual equipment from a commercial device. We focused on faults due to deterioration of the peristaltic pumps caused by aging and also on detecting the presence of undesired air bubbles in the hydraulic circuit. The audio information was processed in Address Event Representation (AER) format, which was then converted into a set of labeled sonogram images of 0.5 s each. We used a window overlap of 50%, which enlarges the number of samples and improves training results. All of these data are now available as a public dataset. We did a classification test for both scenarios by training a DenseNet121 model that achieved a F1 weighted average of 0.99 for the aging scenario and 0.71 for the bubble scenario. Further processing should be done in the future to improve the results on the bubble scenario.

Melanoma is considered a global public health challenge and is responsible for more than 90% deaths related to skin cancer. Although the diagnosis of early melanoma is the main goal of dermoscopy, the discrimination between dermoscopic images of in situ and invasive melanomas can be a difficult task even for experienced dermatologists. Recent advances in artificial intelligence in the field of medical image analysis show that its application to dermoscopy with the aim of supporting and providing a second opinion to the medical expert could be of great interest. In this work, four datasets from different sources were used to train and evaluate deep learning models on in situ versus invasive melanoma classification and on Breslow thickness prediction. Supervised learning and semi-supervised learning using a multi-teacher ensemble knowledge distillation approach were considered and evaluated using a stratified 5-fold cross-validation scheme. The best models achieved AUCs of 0.80850.0242 and of 0.82320.0666 on the former and latter classification tasks, respectively. The best results were obtained using semi-supervised learning, with the best model achieving 0.8547 and 0.8768 AUC, respectively. An external test set was also evaluated, where semi-supervision achieved higher performance in all the classification tasks. The results obtained show that semi-supervised learning could improve the performance of trained models in different melanoma classification tasks compared to supervised learning. Automatic deep learning-based diagnosis systems could support medical professionals in their decision, serving as a second opinion or as a triage tool for medical centers.

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.

Prostate cancer is the second most frequent cancer in men worldwide after lung cancer. Its diagnosis is based on the identification of the Gleason score that evaluates the abnormality of cells in glands through the analysis of the different Gleason patterns within tissue samples. The recent advancements in computational pathology, a domain aiming at developing algorithms to automatically analyze digitized histopathology images, lead to a large variety and availability of datasets and algorithms for Gleason grading and scoring. However, there is no clear consensus on which methods are best suited for each problem in relation to the characteristics of data and labels. This paper provides a systematic comparison on nine datasets with state-of-the-art training approaches for deep neural networks (including fully-supervised learning, weakly-supervised learning, semi-supervised learning, Additive-MIL, Attention-Based MIL, Dual-Stream MIL, TransMIL and CLAM) applied to Gleason grading and scoring tasks. The nine datasets are collected from pathology institutes and openly accessible repositories. The results show that the best methods for Gleason grading and Gleason scoring tasks are fully supervised learning and CLAM, respectively, guiding researchers to the best practice to adopt depending on the task to solve and the labels that are available.

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.

Abstract Air pollutants harm human health and the environment. Nowadays, deploying an air pollution monitoring network in many urban areas could provide real-time air quality assessment. However, these networks are usually sparsely distributed and the sensor calibration problems that may appear over time lead to missing and wrong measurements. There is an increasing interest in developing air quality modelling methods to minimize measurement errors, predict spatial and temporal air quality, and support more spatially-resolved health effect analysis. This research aims to evaluate the ability of three feed-forward neural network architectures for the spatial prediction of air pollutant concentrations using the measures of an air quality monitoring network. In addition to these architectures, Support Vector Machines and geostatistical methods (Inverse Distance Weighting and Ordinary Kriging) were also implemented to compare the performance of neural network models. The evaluation of the methods was performed using the historical values of seven air pollutants (Nitrogen monoxide, Nitrogen dioxide, Sulphur dioxide, Carbon monoxide, Ozone, and particulate matters with size less than or equal to 2.5 $$\upmu $$ μ m and to 10 $$\upmu $$ μ m) from an urban air quality monitoring network located at the metropolitan area of Madrid (Spain). To assess and compare the predictive ability of the models, three estimation accuracy indicators were calculated: the Root Mean Squared Error, the Mean Absolute Error, and the coefficient of determination. FFNN-based models are superior to geostatistical methods and slightly better than Support Vector Machines for fitting the spatial correlation of air pollutant measurements. Graphical abstract

Abstract Neuromorphic computation processes sensors output in the spiking domain, which presents constraints in many cases when converting information to spikes, loosing, as example, temporal accuracy. This paper presents a spike-based system to adapt audio information from low-power pulse-density modulation (PDM) microelectromechanical systems microphones into rate coded spike frequencies. These spikes could be directly used by the neuromorphic auditory sensor (NAS) for frequency decomposition in different bands, avoiding the analog or digital conversion to spike streams. This improves the time response of the NAS, allowing its use in more time restrictive applications. This adaptation was conducted in VHDL as an interface for PDM microphones, converting their pulses into temporal distributed spikes following a pulse-frequency modulation scheme with an accurate inter-spike-interval, known as PDM to spikes interface (PSI). We introduce a new architecture of spike-based band-pass filter to reject DC components and distribute spikes in time. This was tested in two scenarios, first as a stand-alone circuit for its characterization, and then integrated with a NAS for verification. The PSI achieves a total harmonic distortion of $$-$$ - 46.18 dB and a signal-to-noise ratio of 63.47 dB, demands less than 1% of the resources of a Spartan-6 FPGA and its power consumption is around 7 mW.

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.

In recent years, locomotion mechanisms exhibited by vertebrate animals have been the inspiration for the improvement in the performance of robotic systems. These mechanisms include the adaptability of their locomotion to any change registered in the environment through their biological sensors. In this regard, we aim to replicate such kind of adaptability through a sCPG. This sCPG generates different locomotion (rhythmic) patterns which are driven by an external stimulus, that is, the output of a FSR sensor to provide feedback. The sCPG consists of a network of five populations of LIF neurons designed with a specific topology in such a way that the rhythmic patterns can be generated and driven by the aforementioned external stimulus. Therefore, eventually, the locomotion of an end robotic platform could be adapted to the terrain by using any sensor as input. The sCPG with adaptation has been numerically validated at software and hardware level, using the Brian 2 simulator and the SpiNNaker neuromorphic platform for the latest. In particular, our experiments clearly show an adaptation in the oscillation frequencies between the spikes produced in the populations of the sCPG while the input stimulus varies. To validate the robustness and adaptability of the sCPG, we have performed several tests by variating the output of the sensor. These experiments were carried out in Brian 2 and SpiNNaker; both implementations showed a similar behavior with a Pearson correlation coefficient of 0.905.

Abstract Nowadays, sensors play a major role in several fields, such as science, industry and everyday technology. Therefore, the information received from the sensors must be reliable. If the sensors present any anomalies, serious problems can arise, such as publishing wrong theories in scientific papers, or causing production delays in industry. One of the most common anomalies are uncalibrations. An uncalibration occurs when the sensor is not adjusted or standardized by calibration according to a ground truth value. In this work, an online machine-learning based uncalibration detector for temperature, humidity and pressure sensors is presented. This development integrates an artificial neural network as the main component which learns from the behavior of the sensors under calibrated conditions. Then, after being trained and deployed, it detects uncalibrations once they take place. The obtained results show that the proposed system is able to detect the 100% of the presented uncalibration events, although the time response in the detection depends on the resolution of the model for the specific location, i.e., the minimum statistically significant variation in the sensor behavior that the system is able to detect. This architecture can be adapted to different contexts by applying transfer learning, such as adding new sensors or having different environments by re-training the model with minimum amount of data.

Neuromorphic systems are a viable alternative to conventional systems for real-time tasks with constrained resources. Their low power consumption, compact hardware realization, and low-latency response characteristics are the key ingredients of such systems. Furthermore, the event-based signal processing approach can be exploited for reducing the computational load and avoiding data loss due to its inherently sparse representation of sensed data and adaptive sampling time. In event-based systems, the information is commonly coded by the number of spikes within a specific temporal window. However, the temporal information of event-based signals can be difficult to extract when using rate coding. In this work, we present a novel digital implementation of the model, called time difference encoder (TDE), for temporal encoding on event-based signals, which translates the time difference between two consecutive input events into a burst of output events. The number of output events along with the time between them encodes the temporal information. The proposed model has been implemented as a digital circuit with a configurable time constant, allowing it to be used in a wide range of sensing tasks that require the encoding of the time difference between events, such as optical flow-based obstacle avoidance, sound source localization, and gas source localization. This proposed bioinspired model offers an alternative to the Jeffress model for the interaural time difference estimation, which is validated in this work with a sound source lateralization proof-of-concept system. The model was simulated and implemented on a field-programmable gate array (FPGA), requiring 122 slice registers of hardware resources and less than 1 mW of power consumption.

The brain is able to solve complex tasks in real time by combining different sensory cues with previously acquired knowledge. Inspired by the brain, we designed a neuromorphic demonstrator which combines auditory and visual input to find an obstacle free direction closest to the sound source. The system consists of two event-based sensors (the eDVS for vision and the NAS for audition) mounted onto a pan-tilt unit and a spiking neural network implemented on the SpiNNaker platform. By combining the different sensory information, the demonstrator is able to point at a sound source direction while avoiding obstacles in real time.

Spiking convolutional neural networks have become a novel approach for machine vision tasks, due to the latency to process an input stimulus from a scene, and the low power consumption of these kind of solutions. Event-based systems only perform sum operations instead of sum of products of frame-based systems. In this work an upgrade of a neuromorphic event-based convolution accelerator for SCNN, which is able to perform multiple layers with different kernel sizes, is presented. The system has a latency per layer from 1.44 us to 9.98us for kernel sizes from 1x1 to 7x7.

Glioma is a type of brain tumor that causes mortality in many cases. Early diagnosis is an important factor. Typically, it is detected through MRI and then either a treatment is applied, or it is removed through surgery. Deep-learning techniques are becoming popular in medical applications and image-based diagnosis. Convolutional Neural Networks are the preferred architecture for object detection and classification in images. In this paper, we present a study to evaluate the efficiency of using CNNs for diagnosis aids in glioma detection and the improvement of the method when using a clustering method (Fuzzy C-means) for preprocessing the input MRI dataset. Results offered an accuracy improvement from 0.77 to 0.81 when using Fuzzy C-Means.

Deep Learning tools are widely used for medical image segmentation. The results produced by these techniques depend to a great extent on the data sets used to train the used network. Nowadays many cloud service providers offer the required resources to train networks and deploy deep learning networks. This makes the idea of segmentation as a cloud-based service attractive. In this paper we study the possibility of training, a generalized configurable, Keras U-Net to test the feasibility of training with images acquired, with specific instruments, to perform predictions on data from other instruments. We use, as our application example, the segmentation of Optic Disc and Cup which can be applied to glaucoma detection. We use two publicly available data sets (RIM-One V3 and DRISHTI) to train either independently or combining their data.

Breast cancer is one of the most frequent causes of mortality in women. For the early detection of breast cancer, the mammography is used as the most efficient technique to identify abnormalities such as tumors. Automatic detection of tumors in mammograms has become a big challenge and can play a crucial role to assist doctors in order to achieve an accurate diagnosis. State-of-the-art Deep Learning algorithms such as Faster Regional Convolutional Neural Networks are able to determine the presence of an object and also its position inside the image in a reduced computation time. In this work, we evaluate these algorithms to detect tumors in mammogram images and propose a detection system that contains: (1) a preprocessing step performed on mammograms taken from the Digital Database for Screening Mammography (DDSM) and (2) the Neural Network model, which performs feature extraction over the mammograms in order to locate tumors within each image and classify them as malignant or benign. The results obtained show that the proposed algorithm has an accuracy of 97.375%. These results show that the system could be very useful for aiding physicians when detecting tumors from mammogram images.

Along with the proliferation of mobile devices and wireless signal coverage, IoT devices, such as smart wristbands \nfor monitoring its owner’s activity or sleep patterns, get great popularity. Wearable technology in human \nlife has become quite useful due to the information given (sleep hours, heart rate, etc). However, wearables \nfor animals does not give information about behaviour directly: they collect raw data that is sent to a server \nwhere, after a post-processing step, the behaviour is known. In this work, we present a smart IoT device that \nclassifies different animal behaviours from the information obtained from on-board sensors using an embedded \nneural network running in the device. This information is uploaded to a server through a wireless sensor \nnetwork based on Zigbee communication. The architecture of the device allows an easy assembly in a reduced \ndimension wearable case. The firmware allows a modular functionality by activating or deactivating \nmodules independently, which improve the power efficiency of the device. The power consumption has been \nanalyzed, allowing the 1Ah battery to work the system during several days. A novel localization and distance \nestimation technique (for 802.15.4 networks) is presented to recover a lost device in Do˜nana National Park \nwith unidirectional antennas and log-normalization distance estimation over RSSI.

Falls are one of the most frequent causes of injuries in elderly people. Wearable Fall Detection Systems provided a ubiquitous tool for monitoring and alert when these events happen. Recurrent Neural Networks (RNN) are algorithms that demonstrates a great accuracy in some problems analyzing sequential inputs, such as temporal signal values. However, their computational complexity are an obstacle for the implementation in IoT devices. This work shows a performance analysis of a set of RNN architectures when trained with data obtained using different sampling frequencies. These architectures were trained to detect both fall and fall hazards by using accelerometers and were tested with 10-fold cross validation, using the F1-score metric. The results obtained show that sampling with a frequency of 25Hz does not affect the effectiveness, based on the F1-score, which implies a substantial increase in the performance in terms of computational cost. The architectures with two RNN layers and without a first dense layer had slightly better results than the smallest architectures. In future works, the best architectures obtained will be integrated in an IoT solution to determine the effectiveness empirically.

This live demonstration presents an audio-guided neuromorphic robot: from a Neuromorphic Auditory Sensor (NAS) to locomotion using Spiking Central Pattern Generators (sCPGs). Several gaits are generated by sCPGs implemented on a SpiNNaker board. The output of these sCPGs is sent in a real-time manner to an Field Programmable Gate Array (FPGA) board using an AER-to-SpiNN interface. The control of the hexapod robot joints is performed by the FPGA board. The robot behavior can be changed in real-time by means of the NAS. The audio information is sent to the SpiNNaker board which classifies it using a Spiking Neural Network (SNN). Thus, the input sound will activate a specific gait pattern which will eventually modify the behavior of the robot.

In this demonstration a spiking neural network architecture for vision recognition using an FPGA spiking convolution processor, based on leaky integrate and fire neurons (LIF) and a SpiNNaker board is presented. The network has been trained with Poker-DVS dataset in order to classify the four different card symbols. The spiking convolution processor extracts features from images in form of spikes, computes by one layer of 64 convolutions. These features are sent to an OKAERtool board that converts from AER to 2-7 protocol to be classified by a spiking neural network deployed on a SpiNNaker platform.

Speech recognition has become an important task to improve the human-machine interface. Taking into account the limitations of current automatic speech recognition systems, like non-real time cloud-based solutions or power demand, recent interest for neural networks and bio-inspired systems has motivated the implementation of new techniques. Among them, a combination of spiking neural networks and neuromorphic auditory sensors offer an alternative to carry out the human-like speech processing task. In this approach, a spiking convolutional neural network model was implemented, in which the weights of connections were calculated by training a convolutional neural network with specific activation functions, using firing rate-based static images with the spiking information obtained from a neuromorphic cochlea. The system was trained and tested with a large dataset that contains ”left” and ”right” speech commands, achieving 89.90% accuracy. A novel spiking neural network model has been proposed to adapt the network that has been trained with static images to a non-static processing approach, making it possible to classify audio signals and time series in real time.

Neural networks algorithms are commonly used to recognize patterns from different data sources such as audio or vision. In image recognition, Convolutional Neural Networks are one of the most effective techniques due to the high accuracy they achieve. This kind of algorithms require billions of addition and multiplication operations over all pixels of an image. However, it is possible to reduce the number of operations using other computer vision techniques rather than frame-based ones, e.g. neuromorphic frame-free techniques. There exists many neuromorphic vision sensors that detect pixels that have changed their luminosity. In this study, an event-based convolution engine for FPGA is presented. This engine models an array of leaky integrate and fire neurons. It is able to apply different kernel sizes, from 1x1 to 7x7, which are computed row by row, with a maximum number of 64 different convolution kernels. The design presented is able to process 64 feature maps of 7x7 with a latency of 8.98μs.

Several studies have focused on classifying behavioral patterns in wildlife and captive species to monitor their activities and so to understanding the interactions of animals and control their welfare, for biological research or commercial purposes. The use of pattern recognition techniques, statistical methods and Overall Dynamic Body Acceleration (ODBA) are well known for animal behavior recognition tasks. The reconfigurability and scalability of these methods are not trivial, since a new study has to be done when changing any of the configuration parameters. In recent years, the use of Artificial Neural Networks (ANN) has increased for this purpose due to the fact that they can be easily adapted when new animals or patterns are required. In this context, a comparative study between a theoretical research is presented, where statistical and spectral analyses were performed and an embedded implementation of an ANN on a smart collar device was placed on semi-wild animals. This system is part of a project whose main aim is to monitor wildlife in real time using a wireless sensor network infrastructure. Different classifiers were tested and compared for three different horse gaits. Experimental results in a real time scenario achieved an accuracy of up to 90.7%, proving the efficiency of the embedded ANN implementation.

In this demonstration we present a spiking neural network architecture for audio samples classification using SpiNNaker. The network consists of different leaky integrate-and-fire neuron layers. The connections between them are trained using firing rate based algorithms. Tests use sets of pure tones with frequencies that range from 130.813 to 1396.91 Hz. Audio signals coming from the computer are converted to spikes using a Neuromorphic Auditory Sensor and, after that, this information is sent to the SpiNNaker board through a PCB that translates from AER to 2-of-7 protocol. The classification output obtained in the spiking neural network deployed on SpiNNaker is then shown in the computer screen. Different levels of random noise are added to the original audio signals in order to test the robustness of the classification system.