Real-time Spiking Neural Network Research Articles

Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework

Interictal Epileptiform Discharges (IED) and High Frequency Oscillations (HFO) in intraoperative electrocorticography (ECoG) may guide the surgeon by delineating the epileptogenic zone. We designed a modular spiking neural network (SNN) in a mixed-signal neuromorphic device to process the ECoG in real-time. We exploit the variability of the inhomogeneous silicon neurons to achieve efficient sparse and decorrelated temporal signal encoding. We interface the full-custom SNN device to the BCI2000 real-time framework and configure the setup to detect HFO and IED co-occurring with HFO (IED-HFO). We validate the setup on pre-recorded data and obtain HFO rates that are concordant with a previously validated offline algorithm (Spearman’s ρ = 0.75, p = 1e-4), achieving the same postsurgical seizure freedom predictions for all patients. In a remote on-line analysis, intraoperative ECoG recorded in Utrecht was compressed and transferred to Zurich for SNN processing and successful IED-HFO detection in real-time. These results further demonstrate how automated remote real-time detection may enable the use of HFO in clinical practice.

A Bandwidth-Efficient Emulator of Biologically-Relevant Spiking Neural Networks on FPGA

Closed-loop experiments involving biological and artificial neural networks would improve the understanding of neural cells functioning principles and lead to the development of new generation neuroprosthesis. Several technological challenges require to be faced, as the development of real-time spiking neural network emulators which could bear the increasing amount of data provided by new generation High-Density Multielectrode Arrays. This work focuses on the development of a real-time spiking neural network emulator addressing fully-connected neural networks. This work presents a new way to increase the number of synapses supported by real-time neural network accelerators. The proposed solution has been implemented on the Xilinx Zynq 7020 All-Programmable SoC and can emulate fully connected spiking neural networks counting up to 3,098 Izhikevich neurons and 9.6e6 synapses in real-time, with a resolution of 0.1 ms.

Optimized Real-Time Biomimetic Neural Network on FPGA for Bio-hybridization.

Neurological diseases can be studied by performing bio-hybrid experiments using a real-time biomimetic Spiking Neural Network (SNN) platform. The Hodgkin-Huxley model offers a set of equations including biophysical parameters which can serve as a base to represent different classes of neurons and affected cells. Also, connecting the artificial neurons to the biological cells would allow us to understand the effect of the SNN stimulation using different parameters on nerve cells. Thus, designing a real-time SNN could useful for the study of simulations of some part of the brain. Here, we present a different approach to optimize the Hodgkin-Huxley equations adapted for Field Programmable Gate Array (FPGA) implementation. The equations of the conductance have been unified to allow the use of same functions with different parameters for all ionic channels. The low resources and high-speed implementation also include features, such as synaptic noise using the Ornstein–Uhlenbeck process and different synapse receptors including AMPA, GABAa, GABAb, and NMDA receptors. The platform allows real-time modification of the neuron parameters and can output different cortical neuron families like Fast Spiking (FS), Regular Spiking (RS), Intrinsically Bursting (IB), and Low Threshold Spiking (LTS) neurons using a Digital to Analog Converter (DAC). Gaussian distribution of the synaptic noise highlights similarities with the biological noise. Also, cross-correlation between the implementation and the model shows strong correlations, and bifurcation analysis reproduces similar behavior compared to the original Hodgkin-Huxley model. The implementation of one core of calculation uses 3% of resources of the FPGA and computes in real-time 500 neurons with 25,000 synapses and synaptic noise which can be scaled up to 15,000 using all resources. This is the first step toward neuromorphic system which can be used for the simulation of bio-hybridization and for the study of neurological disorders or the advanced research on neuroprosthesis to regain lost function.

Optimizing Network Traffic for Spiking Neural Network Simulations on Densely Interconnected Many-Core Neuromorphic Platforms

In this paper we present a new Partitioning and Placement methodology able to maps Spiking Neural Network on parallel neuromorphic platforms. This methodology improves scalability/reliability of Spiking Neural Network (SNN) simulations on many-core and densely interconnected platforms. SNNs mimic brain activity by emulating spikes sent between neuron populations. Many-core platforms are emerging computing targets that aim to achieve real-time SNN simulations. Neurons are mapped to parallel cores, and spikes are sent in the form of packets over the on-chip and off-chip network. However, the activity of neuron populations is heterogeneous and complex. Thus, achieving an efficient exploitation of platform resources is a challenge that often affects simulation scalability/reliability. To address this challenge, the proposed methodology uses customised SNN to profile the board bottlenecks and implements a SNN partitioning and placement (SNN-PP) algorithm for improving on-chip and off-chip communication efficiency. The cortical microcircuit SNN was simulated and performances of the developed SNN-PP algorithm were compared with performances of standard methods. These comparisons showed significant traffic reduction produced by the new method, that for some configurations reached up to 96X. Results demonstrate that it is possible to consistently reduce packet traffic and improve simulation scalability/reliability with an effective neuron placement.

A biologically plausible real-time spiking neuron simulation environment based on a multiple-FPGA platform

Neurological research has revealed that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of such neuronal systems. The spiking neuron models which are used in simulations can be described mathematically, but the continuous time involved in mathematical models needs to be replaced by discrete time steps. An alternative approach, hardware implementation, provides the possibility of generating independent spikes precisely and simultaneously output spike waves in real biological time, under the premise that the spiking neural network implemented in hardware can take full advantage of hardware-timed speed and reliability. In this work we propose a multi-layered biologically plausible real-time spiking neural network simulation platform that can be used to emulate the operation of biological neural systems (such as elements of the visual cortex) and computational models of such systems. The implementation of a layered spiking neural network using the Xilinx Virtex-4 family of Field Programmable Gate Array (FPGA) is presented.