Spiking neural networks (SNNs) with Time-to-First-Spike (TTFS) coding promise rapid, sparse, and energy-efficient inference. However, the impact of sample difficulty on TTFS dynamics remains underexplored. We investigate (i) how input hardness influences first-spike timing and (ii) whether training on hard samples expedites inference. By quantifying difficulty via geometric margins and Gaussian-noise perturbations, and modeling leaky integrate-and-fire dynamics as Gaussian random walks, we derive first-hitting-time predictions. We further show that training-time noise, akin to ridge regularization, reduces weight variance and increases expected spike latencies. Empirical results on a synthetic task, MNIST, NMNIST, and CIFAR-10 with spiking MLPs/CNNs confirm that harder inputs slow inference and noise-trained models trade robustness for latency. Our findings align TTFS behavior with drift-diffusion models and provide a framework for balancing speed and robustness in neuromorphic SNNs.

In recent years the emergence of Spiking Neural Net- works (SNNs) has shown that these networks are a promis- ing alternative to traditional Artificial Neural Networks (ANNs) due to their low-power computing capabilities and noise robustness. Nevertheless, in recent approaches, they have either strayed away from spike time backpropaga- tion, used discrete time, single spike neurons, and/or limited themselves to shallow networks. These approaches limit the potential of SNNs by sacrificing significant aspects of what makes them special in order to have a functional network. It is for this reason that we believe that, the implemen- tation of residual connections, allowing a model that has multi-spiking neurons, precise time and spike time back- propagation is the path to allow these networks to truly shine. As it will solve one of this model’s most severe lim- itations which prevent it from being used to build deeper networks, the banishing of spikes at a deeper depth. Hence we aim to remedy this problem by feeding inputs from fur- ther up the network to revitalize the spike counts at deeper layers. In this paper, we explore the implementation of residual connections in precise time multi-spiking neural networks as a way of solving the disappearing spikes problem that inhibits spike time backpropagation. We explore two al- ternatives in the implementation of the residual connection and we analyze how they affect the accuracy of the network as the depth increases in both Multi-Layer Perceptrons and Convolutional Neural Networks. We also developed an ar- chitecture to allow for swapping of the fuse function in or- der to take full advantage of the flexibility that precise time provides us. Results show that the implemented residual connections allow for deeper training, which has the poten- tial to aid in the network’s performance, although in some cases some hurdles remain.

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.

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 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.

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.

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.