Designing processors for implantable closed-loop neuromodulation systems presents a formidable challenge owing to the constrained operational environment, which requires low latency and high energy efficacy. Previous benchmarks have provided limited insights into power consumption and latency. However, this study introduces algorithmic metrics that capture the potential and limitations of neural decoders for closed-loop intra-cortical brain-computer interfaces in the context of energy and hardware constraints. This study benchmarks common decoding methods for predicting a primate’s finger kinematics from the motor cortex and explores their suitability for low latency and high energy efficient neural decoding. The study found that ANN-based decoders provide superior decoding accuracy, requiring high latency and many operations to effectively decode neural signals. Spiking neural networks have emerged as a solution, bridging this gap by achieving competitive decoding performance within sub-10ms while utilizing a fraction of computational resources. These distinctive advantages of neuromorphic spiking neural networks make them highly suitable for the challenging closed-loop neural modulation environment. Their capacity to balance decoding accuracy and operational efficiency offers immense potential in reshaping the landscape of neural decoders, fostering greater understanding, and opening new frontiers in closed-loop intra-cortical human-machine interaction.

Reconstructing seen images from functional magnetic resonance imaging (fMRI) brain scans has been a growing topic of interest in the field of neuroscience, fostered by innovation in machine learning and AI. This paper investigates the possible presence of personal features allowing the identification of subjects from their reconstructed images. Identifying the extent to which personal information is present is necessary to prevent privacy and data protection breaches. Additionally, personal features may reveal information about how people see the world, furthering work in computer-brain interfacing or helping people with neurological conditions that affect sight. In this paper, a CNN model is presented that allows to identify subjects from their reconstructed image with an average accuracy of 90.4%. An encoder-decoder model was used to produce the reconstructed images from the Generic Object Data set. The accuracy shows that personal features are indeed present in the reconstructed images, raising important ethical and legal considerations when using image reconstruction technology.

This study aims to investigate the impact of various denoising algorithms on the quality of visual stimulus reconstructions based on functional magnetic resonance imaging (fMRI) data. While fMRI provides a valuable, noninvasive method for assessing brain activity, the reliability of this data can be impaired by multiple noise types, including thermal, physiological, and scanner-related noise. Numerous denoising methods have been proposed, such as independent component analysis, confound regression and filtering, and GLM denoise. However, their efficiency, especially in the context of limited task fMRI data, remains largely unexplored. Using the Generic Objects Dataset (GOD), our study explores three primary research subquestions: the effectiveness of different denoising algorithms in improving reconstruction quality; the impact of artificially induced noise on these algorithms and whether combining different denoising algorithms can further enhance image reconstruction quality. The primary contributions of this research include the evaluation of various denoising methods with limited task fMRI data, determining the most effective denoising algorithms given a small dataset size, and analyzing how these algorithms perform in the presence of artificially introduced noise. The results of this investigation showed an improvement in performance of reconstruction models given multiple denoising algorithm, the best performer being kurtosis-based PCA used together with nuisanse regression with constant and linear terms bringing a 6.2% increase in score. The noise ceiling is the worst performer, bringing the score down by 4.4%. Denoising algorithms fail to improve reconstructions poisoned with gaussian noise, however, ICA manages to achieve a minor improvement of the quality of reconstructed images given noise sampled from the uniform distribution.

As technology advances, automated systems become more autonomous which leads to a higher interdependence between machine and human. Much research has been done about trust between humans and trust of humans regarding machines. An interesting question that remains is how the behavior of an agent influences human trustworthiness in a human-agent collaborative setting. The research presented by this paper contributes to the understanding of this area. It investigates a specific behavioral trait using the following hypothesis: friendly behavior of an agent improves human trustworthiness. Here, trustworthiness is broken up in the constructs: ability, benevolence and integrity. An experiment has been conducted using a collaborative Search and Rescue game. The following behaviors of the participants have been measured: - Ability: speed and effectiveness; - Benevolence: communication, willingness to help, agreeableness to advice, responsiveness; - Integrity: truthfulness. Furthermore, a likert scale has been used to measure the participants' own perception of their trustworthiness. The experiment is conducted with 20 participants in the control group, where the agent spoke in a neutral manner, and 20 in the experimental group, where the agent instilled empathy, stimulated collaboration, encouraged the participants and was affectionate. The research has shown a significant improvement in the experimental group only for communication and willingness to help. This gives some indication that a friendly agent only slightly improves the trustworthiness of a human. However, the research has some limitations that might also explain the lack of significant results. Firstly, it is unclear to what extent the measures truly measured the constructs of trustworthiness. Secondly, to create a friendly agent, theories from organizational and social psychology are used, which are mostly focussed on human-human relationships, instead of human-agent relationships. Finally, Some confounding variables may have had an impact, like lag in the game and the participant not properly reading the agent’s messages.

Deep learning has been widely implemented in industrial inspection, such as damage detection from images. However, training deep networks requires massive data, which is hard to collect and laborious to annotate, especially in the aviation scenario of aircraft engines. To alleviate the demand for annotated data, we create BladeSynth - a large synthetic image dataset for detecting damage from aircraft engines, and empirically evaluate the transferability of state-of-the-art Scaled-YOLOV4 from synthetic to real world by pre-training on synthetic data and fine-tuning on real data. Our experiments show that pre-training on synthetic data improves the performance in damage detection in aircraft engine images.

Data-driven approaches are a promising new addition to the list of available strategies for solving Partial Differential Equations (PDEs). One such approach, the Principal Component Analysis-based Neural Network PDE solver, can be used to learn a mapping between two function spaces, corresponding to a PDE. However, the practical limitations of this approach are unclear. This paper seeks to investigate for which types of inputs and outputs this type of solver gives useful results. Using a dataset with inputs sampled from Gaussian Random Fields with different parameters, and outputs for Poisson's equation and the Heat equation, obtained by using a Finite Element solver, neural networks are trained, and their performance is evaluated. The method performs adequately for the chosen inputs, and patterns are found in the resulting error, which differ for each set of input parameters. Thus, for these equations, it seems that this method performs differently for different input distributions, but further research is necessary to investigate if these patterns will hold for other equations.

Solving Partial Differential Equations (PDEs) in engineering such as Navier-Stokes is incredibly computationally expensive and complex. Without analytical solutions, numerical solutions can take ages to simulate at great expense. In order to reduce this cost, neural networks may be used to compute approximations of the solution for use during engineering processes. PCA-net is a neural network approach that reduces the dimensionality of the input and output data for PDEs in order to allow mapping from a high-dimensional input and output function with a fully connected neural network through the use of Principal Component Analysis (PCA). In this paper, PCA-net is applied to Navier-Stokes with varying viscosities to test the generalization of PCA-net on viscosity parameters. Training is done on four discrete viscosities, while testing is done on continuous viscosities, extrapolating and interpolating around the training set. Results shows good performance on low viscosities, both with interpolation and extrapolation. Mid-to-high viscosity interpolation shows lesser performance, with high viscosity extrapolation diverging to great error. Omitting high viscosities, performance over varying viscosities is close to that shown by previous research.

This paper presents a comprehensive exploration of a novel method combining Principal Component Analysis (PCA) and Neural Networks (NN) to efficiently solve Partial Differential Equations (PDEs), a fundamental challenge in modeling a wide range of real-world phenomena. Our research extends the work of Bhattacharya et al. by focusing on PCA for effective dimensionality reduction and utilizing NN for mapping in the reduced dimension. This approach addresses the significant computational challenges and inaccuracies often encountered with classical numerical techniques in solving PDEs. We specifically investigate the still-water equation, employing our PCA-NN method to learn a reduced order mapping of PDE solutions and evaluate its robustness in diverse noisy environments. Our findings reveal a notable relationship between noise intensity and error, indicating a linear trend for Gaussian and Salt and Pepper noise, and an exponential trend for Uniform noise. Furthermore, this study uncovers a critical weakness of the model in predicting points with a high rate of change. Overall, our research significantly contributes to understanding the practical applicability and limitations of PCA-NN methods in real-world, noisy settings, offering valuable insights for future applications in this domain.

Spiking neural networks (SNNs) aim to utilize mechanisms from biological neurons to bridge the computational and efficiency gaps between the human brain and machine learning systems. The widely used Leaky-Integrate-and-Fire (LIF) neuron model accumulates input spikes into an exponentially decaying membrane potential and generates a spike when this potential exceeds a set threshold. A LIF neuron is characterized by learnable input weights and a manually selected membrane time constant 𝜏, which determines the decay rate of a neuron's membrane potential. Previous work introduced the Parametric LIF (PLIF) neuron model with a learnable 𝜏. However, the published experiments only featured a single 𝜏 per spiking layer. This leaves space for exploration of the effect of having a learnable 𝜏 for each neuron. The importance of 𝜏 is given by the trade-off it inherently introduces, prioritizing the neuron's capability to convey information about spatial or temporal features. This work examines the effect of introducing a learnable 𝜏 per neuron with a new initialization method and a new regularization term which incentivizes low variance in each PLIF layer. The experiments are done using the DVS128 Gesture dataset and compared to a baseline model from the original paper introducing the PLIF neuron model. Results are inconclusive but suggest that introducing 𝜏 per neuron does not have a significant effect on the accuracy of a spiking neural network. Moreover, the evolution of 𝜏 during training exhibits interesting behavior and leads to two new hypotheses.

The increasing computational costs of training deep learning models have drawn more and more attention towards more power-efficient alternatives such as spiking neural networks (SNNs). SNNs are an artificial neural network that mimics the brain’s way of processing information. These models can be even more power-efficient when run on specialized hardware like digital neuromorphic chips. These chips are designed to handle the unique processing needs of SNNs like sparse and event-driven computations. A lot of the state-of-the-art performance of SNNs in recent research has been achieved through supervised learning models that leverage intricate error backpropagation techniques. These models impose specific constraints on the network and rely on continuous time to facilitate the backpropagation process. However, this fix imposes a new challenge when converting these mechanisms on a neuromorphic chip. Because time is discrete on hardware numerical errors can be introduced as we can not calculate the infinitely precise value of variables depending on time. This work proposes a time discretization technique that allows for fast and stable backpropagation and analyses the effects of it on the efficiency of the SNN. Specifically, we look at sparsity, latency, and accuracy as the main factors that explain the power efficiency of the model. The experiments show that choosing a suitable time step size can improve sparsity while maintaining a high level of accuracy. However, too large values affect the network’s ability to learn.

The increasing computational costs of training deep learning models have drawn more and more attention towards more power-efficient alternatives such as spiking neural networks (SNNs). SNNs are an artificial neural network that mimics the brain’s way of processing information. These models can be even more power-efficient when run on specialized hardware like digital neuromorphic chips. These chips are designed to handle the unique processing needs of SNNs like sparse and event-driven computations. A lot of the state-of-the-art performance of SNNs in recent research has been achieved through supervised learning models that leverage intricate error backpropagation techniques. These models impose specific constraints on the network and rely on continuous time to facilitate the backpropagation process. However, this fix imposes a new challenge when converting these mechanisms on a neuromorphic chip. Because time is discrete on hardware numerical errors can be introduced as we can not calculate the infinitely precise value of variables depending on time. This work proposes a time discretization technique that allows for fast and stable backpropagation and analyses the effects of it on the efficiency of the SNN. Specifically, we look at sparsity, latency, and accuracy as the main factors that explain the power efficiency of the model. The experiments show that choosing a suitable time step size can improve sparsity while maintaining a high level of accuracy. However, too large values affect the network’s ability to learn.

Spiking Neural Networks (SNN) represent a distinct class of neural network models that incorporate an additional temporal dimension. Neurons within SNN operate according to the Leaky Integrate-and-Fire principle, governed by ordinary differential equations. Inter-layer neuronal communication occurs through spike propagation when membrane potentials reach a specified threshold. This unique mechanism renders conventional Artificial Neural Network (ANN) design principles and learning rules inapplicable to SNN. This study presents a theoretical investigation into the impact of time discretization on SNN performance in real-world tasks. We present a network architecture based on the Error Backpropagation Through Spikes model. This approach allows for the transformation of the original system of differential equations into a system of difference equations across multiple time steps. We evaluate our model's performance on the MNIST dataset and identify several phenomena that influence accuracy. Our analysis primarily focuses on gradient dynamics and spectral properties of the voltage signals. These findings contribute to a deeper understanding of SNN behavior and potential optimization strategies.

Spiking Neural Networks (SNN) represent a distinct class of neural network models that incorporate an additional temporal dimension. Neurons within SNN operate according to the Leaky Integrate-and-Fire principle, governed by ordinary differential equations. Inter-layer neuronal communication occurs through spike propagation when membrane potentials reach a specified threshold. This unique mechanism renders conventional Artificial Neural Network (ANN) design principles and learning rules inapplicable to SNN. This study presents a theoretical investigation into the impact of time discretization on SNN performance in real-world tasks. We present a network architecture based on the Error Backpropagation Through Spikes model. This approach allows for the transformation of the original system of differential equations into a system of difference equations across multiple time steps. We evaluate our model's performance on the MNIST dataset and identify several phenomena that influence accuracy. Our analysis primarily focuses on gradient dynamics and spectral properties of the voltage signals. These findings contribute to a deeper understanding of SNN behavior and potential optimization strategies.

Event cameras are cameras that capture events asynchronously based on changes in lighting. They offer multiple benifits, but pose challenges in computer vision due to their asynchronous nature and hard to capture ground truth values to compare against. This paper shows the effects training of a state of the art unsupervised learning algorithm Taming Contrast Maximisation for predicting optical flow on a new dataset BlinkFlow which promises improvements in performance of supervised algorithms. This paper aims to see if these improved performances also happen for unsupervised models. Results of this research were inconclusive for the effectiveness of training unsupervised models, but it was shown that pretrained models on DSEC and MVSEC datasets did not perform well on this new dataset.

Event cameras are cameras that capture events asynchronously based on changes in lighting. They offer multiple benifits, but pose challenges in computer vision due to their asynchronous nature and hard to capture ground truth values to compare against. This paper shows the effects training of a state of the art unsupervised learning algorithm Taming Contrast Maximisation for predicting optical flow on a new dataset BlinkFlow which promises improvements in performance of supervised algorithms. This paper aims to see if these improved performances also happen for unsupervised models. Results of this research were inconclusive for the effectiveness of training unsupervised models, but it was shown that pretrained models on DSEC and MVSEC datasets did not perform well on this new dataset.

The promise of Artificial Neural Networks has lead to their immense usage intertwined with concerns over energy consumption. This has led to development of alternatives, such as Spiking Neural Networks (SNNs), which allows their implementation on neuromorphic hardware. In effect, the network must be time discretized. SNNs learn through backpropagation, similarly to ANNs, where two main methods exist: spike-based backpropagation and backpropagation through time. This study investigates and compares the effect of varying timesteps on the convergence rate of BATS, an example of spikebased backpropagation, and of SLAYER - an example of backpropagation through time on the NMNIST and MNIST dataset. The results conclude that BATS withstands growing timestep sizes. Although SLAYER maintains a good performance for small timestep sizes, it seems to self-destruct as they grow.

Event cameras are novel sensors whose high temporal resolution and bandwidth motivate their use for the optical flow estimation problem. However, the properties of event cameras also introduce a vulnerability to flickering. Flickering hurts the perceptibility of motion by overwhelming event data with unrelated information. The single existing event de-flicker method (EFR) is built for scenarios where the relative position of the camera and the flickering object is constant, which is uncommon in motion-heavy optical flow estimation scenarios. Our contribution is a new de-flickering method that incorporates spatial awareness of nearby pixels. We hypothesize this feature to increase robustness to movement, and thus to better improve optical flow accuracy. Compared to EFR our method falters at filtering intensely flickering surfaces, but better preserves the spatial coherence of edges. However, we observe that both de-flickering methods remove much geometric information, especially given slow motion or weak ambient illumination. Our benchmarking shows that neither our method nor EFR significantly affects optical flow estimation accuracy, despite reducing event counts by 50-65%. Overall, we conclude that the niche benefits of spatial filtering are nullified by the result that filtering hardly affects optical flow estimation.

Spiking neural networks have gained traction as both a tool for neuroscience research and a new frontier in machine learning. A plethora of neuroscience literature exists exploring the realistic simulation of neurons, with complex models re- quiring the formulation and integration of ordinary differential equations. Overcoming this challenge has led to the exploration of various numerical integration techniques with the goal of highly stable and accurate simulations. In contrast, training spiking neural networks is often done with simple leaky integrate-and-fire models and rudimentary integration methods such as the forward-Euler method. In this research we explore how more complex numerical integration methods, borrowed from neuroscience research, affect the training of networks based on current-based leaky integrate- and-fire neurons. We derive equations required for the integration process and suggest the use of spike time interpolation. Furthermore, we pro- vide insights into applying backpropagation on numerically integrated networks and highlight possible pitfalls of the process. We conclude that numerically integrated networks can achieve training accuracies close to their theoretical limits, with good convergence and training time characteristics. Specifically, high order integrations achieve robust and computationally viable training. Additionally, we explore the effects of spike time interpolation on network accuracy and use our findings to pro- vide insights into the role of different integration parameters on the effective training of spiking neural networks.

Spiking neural networks have gained traction as both a tool for neuroscience research and a new frontier in machine learning. A plethora of neuroscience literature exists exploring the realistic simulation of neurons, with complex models re- quiring the formulation and integration of ordinary differential equations. Overcoming this challenge has led to the exploration of various numerical integration techniques with the goal of highly stable and accurate simulations. In contrast, training spiking neural networks is often done with simple leaky integrate-and-fire models and rudimentary integration methods such as the forward-Euler method. In this research we explore how more complex numerical integration methods, borrowed from neuroscience research, affect the training of networks based on current-based leaky integrate- and-fire neurons. We derive equations required for the integration process and suggest the use of spike time interpolation. Furthermore, we pro- vide insights into applying backpropagation on numerically integrated networks and highlight possible pitfalls of the process. We conclude that numerically integrated networks can achieve training accuracies close to their theoretical limits, with good convergence and training time characteristics. Specifically, high order integrations achieve robust and computationally viable training. Additionally, we explore the effects of spike time interpolation on network accuracy and use our findings to pro- vide insights into the role of different integration parameters on the effective training of spiking neural networks.