An optimised deep spiking neural network architecture without gradients

We present an end-to-end trainable modular event-driven neural architecture that uses local synaptic and threshold adaptation rules to perform transformations between arbitrary spatio-temporal spike patterns. The architecture represents a highly abstracted model of existing Spiking Neural Network (SNN) architectures. The proposed Optimized Deep Event-driven Spiking neural network Architecture (ODESA) can simultaneously learn hierarchical spatio-temporal features at multiple arbitrary time scales. ODESA performs online learning without the use of error back-propagation or the calculation of gradients. Through the use of simple local adaptive selection thresholds at each node, the network rapidly learns to appropriately allocate its neuronal resources at each layer for any given problem without using a real-valued error measure. These adaptive selection thresholds are the central feature of ODESA, ensuring network stability and remarkable robustness to noise as well as to the selection of initial system parameters. Network activations are inherently sparse due to a hard Winner-Take-All (WTA) constraint at each layer. We evaluate the architecture on existing spatio-temporal datasets, including the spike-encoded IRIS and TIDIGITS datasets, as well as a novel set of tasks based on International Morse Code that we created. These tests demonstrate the hierarchical spatio-temporal learning capabilities of ODESA. Through these tests, we demonstrate ODESA can optimally solve practical and highly challenging hierarchical spatio-temporal learning tasks with the minimum possible number of computing nodes.Comment: 18 pages, 6 figure

An optimized multi-layer spiking neural network implementation in FPGA without multipliers

This paper presents an expansion and evaluation of the hardware architecture for the Optimized Deep Event-driven Spiking Neural Network Architecture (ODESA). ODESA is a state-of-the-art, event-driven multi-layer Spiking Neural Network (SNN) architecture that offers an end-to-end, online, and local supervised training method. In previous work, ODESA was successfully implemented on Field-Programmable Gate Array (FPGA) hardware, showcasing its effectiveness in resource-constrained hardware environments. Building upon the previous implementation, this research focuses on optimizing the ODESA network hardware by introducing a novel approach. Specifically, we propose substituting the dot product multipliers in the Neurons with a low-cost shift-register design. This optimization strategy significantly reduces the hardware resources required for implementing a neuron, thereby enabling more complex SNNs to be accommodated within a single FPGA. Additionally, this optimization results in a reduction in power consumption, further enhancing the practicality and efficiency of the hardware implementation. To evaluate the effectiveness of the proposed optimization, extensive experiments and measurements were conducted. The results demonstrate the successful reduction in hardware resource utilization while maintaining the network's functionality and performance. Moreover, the power consumption reduction contributes to the overall energy efficiency of the hardware implementation

Neuromorphic engineering needs closed-loop benchmarks

Neuromorphic engineering aims to build (autonomous) systems by mimicking biological systems. It is motivated by the observation that biological organisms—from algae to primates—excel in sensing their environment, reacting promptly to their perils and opportunities. Furthermore, they do so more resiliently than our most advanced machines, at a fraction of the power consumption. It follows that the performance of neuromorphic systems should be evaluated in terms of real-time operation, power consumption, and resiliency to real-world perturbations and noise using task-relevant evaluation metrics. Yet, following in the footsteps of conventional machine learning, most neuromorphic benchmarks rely on recorded datasets that foster sensing accuracy as the primary measure for performance. Sensing accuracy is but an arbitrary proxy for the actual system's goal—taking a good decision in a timely manner. Moreover, static datasets hinder our ability to study and compare closed-loop sensing and control strategies that are central to survival for biological organisms. This article makes the case for a renewed focus on closed-loop benchmarks involving real-world tasks. Such benchmarks will be crucial in developing and progressing neuromorphic Intelligence. The shift towards dynamic real-world benchmarking tasks should usher in richer, more resilient, and robust artificially intelligent systems in the future

A Neuromorphic Architecture for Reinforcement Learning from Real-Valued Observations

Reinforcement Learning (RL) provides a powerful framework for decision-making in complex environments. However, implementing RL in hardware-efficient and bio-inspired ways remains a challenge. This paper presents a novel Spiking Neural Network (SNN) architecture for solving RL problems with real-valued observations. The proposed model incorporates multi-layered event-based clustering, with the addition of Temporal Difference (TD)-error modulation and eligibility traces, building upon prior work. An ablation study confirms the significant impact of these components on the proposed model's performance. A tabular actor-critic algorithm with eligibility traces and a state-of-the-art Proximal Policy Optimization (PPO) algorithm are used as benchmarks. Our network consistently outperforms the tabular approach and successfully discovers stable control policies on classic RL environments: mountain car, cart-pole, and acrobot. The proposed model offers an appealing trade-off in terms of computational and hardware implementation requirements. The model does not require an external memory buffer nor a global error gradient computation, and synaptic updates occur online, driven by local learning rules and a broadcasted TD-error signal. Thus, this work contributes to the development of more hardware-efficient RL solutions

SpikeTime: Spiking Neuronal Network Simulation in Julia Language

While there is much focus on hardware advances that accellerate the simulation of large scale spiking neural networks, it is worthwhile to shift our attention to language advances that may also support accelerated large scale spiking neural network simulation. Some gains in biologically faithful neuronal network simulation can be achieved by applying recent computer language features. For example, the Julia language supports Sparse Compressed Arrays, Static Arrays, furthermore Julia provides very extensive support for CUDA GPU, as well as a plethora of reduced precision types. Julia also provides a high-level syntax that facilitates high code reuse while simplifying plotting and data analysis. These features lend themselves towards high-performance large-scale Spiking Neural Network simulation. Therefore, we are using Julia to develop an open-source software package that enables the simulation of networks with millions to billions of synapses on a computer with a minimum of $64$GB of memory and an NVIDIA GPU. Another major advantage implementing SNN simulations in the Julia language is reduced technical debt. The simulation code we are developing is both faster and less complicated to read compared with some other simulation frameworks. The simplicity of the code base encompasses a simple installation process. Ease of installation is an important part of neuronal simulators that is often overlooked when evaluating simulation merit, GPU simulation environments are notoriously difficult to install and this big technical burden of installation harms model portability and reproducibility. The Julia language facilitates the ease of installation to solve the “two language problem” of scientific computing. The simulator encompasses a singular language environment, which includes a reliable, versatile, and monolithic package manager. Furthermore the simulator installation includes no external language compilation tools or steps. To demonstrate the veracity and performance of this new simulation approach, we compare the the Potjans and Diesmann model as implemented in the NEST and GENN simulators. In a pending analysis, we compare simulation execution speeds and spike train raster plots to NEST and GENN using the discussed models as benchmarks. A review of the literature suggests that there is a desire to modernize pre-existing large scale network simulators, but such efforts fall short of re-writing existing simulator code in the Julia language. @awile2022modernizing The discussed code repository started from using a the pre-existing GitHub code base @yaolu (https://github.com/AStupidBear/SpikingNeuralNetworks.jl), and is similar in other ways to @arthur2022scalable and @illing2019biologicall

Spike2Vec

Scalable methods for representing the transient behaviour of large populations of neurons are needed. In this work, we present an algorithm that can detect fine-grained repetitions quickly across large spiking datasets. The proposed method enables us to quantify both state and state transitions in spike trains. We were motivated to create this tool because existing tools that can also detect reoccurring patterns in spike trains are often not scalable or they are limited towards detecting only particular types of spike patterns. In this work, we demonstrate a geometric representation of complex spike patterns; we represented time-bound neural activity as simple geometric coordinates in a high dimensional space, as this will enable researchers to track the trajectory of the network between familiar and unfamiliar states. Working with geometric representations of chaotic spike trains facilitates state transition recordings in both biologically recorded spike trains and their digitally simulated counterparts. Quickly identifying repeated states in large-scale neuronal data and simulation is essential, as the degree of repetition should influence the mindset of scientists analysing spike trains. For instance, several established cortical network models have assumed that realistic cortical neuronal activity should be Asynchronous and Irregular Activity (AI) in character such as Brunel's balanced model of cortex @brunel1996hebbian. By ascertaining repetitions in large spiking datasets we can quantify the frequency of repetition and better understand a network's ability to revisit states. To this end, we represented time-bound neural activity as simple geometric coordinates in a high-dimensional space. Working with geometric representations of chaotic spike train recordings may enable researchers to find a standard RTSP set of RTSPs. In this work, we show the beginning contributions of compiled Vectorized library of neuronal spike train recordings, which contains recordings from different individuals and individuals from other mammal species. By compiling a Vectorized library of neuronal spike train recordings, we can transform vectors by swapping the axis order of the constituent vectors to find the vector axis order that maximises overlap between replayed events from different organisms

Event-driven spectrotemporal feature extraction and classification using a silicon cochlea model

This paper presents a reconfigurable digital implementation of an event-based binaural cochlear system on a Field Programmable Gate Array (FPGA). It consists of a pair of the Cascade of Asymmetric Resonators with Fast Acting Compression (CAR-FAC) cochlea models and leaky integrate-and-fire (LIF) neurons. Additionally, we propose an event-driven SpectroTemporal Receptive Field (STRF) Feature Extraction using Adaptive Selection Thresholds (FEAST). It is tested on the TIDIGTIS benchmark and compared with current event-based auditory signal processing approaches and neural networks