Brian: a simulator for spiking neural networks in Python

Goodman, Dan F. M.; Brette, Romain

doi:10.1186/1471-2202-9-s1-p92

Cited by 154 publications

(167 citation statements)

References 1 publication

Supporting

Mentioning

165

Contrasting

Unclassified

Order By: Relevance

“…The simulations were run with Brian (http://www. briansimulator.org/), a new Python-based clock-driven spiking neural network simulator (Goodman and Brette, 2008). The code has been made available on ModelDB (http://senselab.med.yale.edu/ShowModel.…”

Section: Methodsmentioning

confidence: 99%

Oscillations, Phase-of-Firing Coding, and Spike Timing-Dependent Plasticity: An Efficient Learning Scheme

Masquelier¹,

Hugues²,

Deco³

et al. 2009

J. Neurosci.

149

153

View full text Add to dashboard Cite

Recent experiments have established that information can be encoded in the spike times of neurons relative to the phase of a background oscillation in the local field potential-a phenomenon referred to as "phase-of-firing coding" (PoFC). These firing phase preferences could result from combining an oscillation in the input current with a stimulus-dependent static component that would produce the variations in preferred phase, but it remains unclear whether these phases are an epiphenomenon or really affect neuronal interactionsonly then could they have a functional role. Here we show that PoFC has a major impact on downstream learning and decoding with the now well established spike timing-dependent plasticity (STDP). To be precise, we demonstrate with simulations how a single neuron equipped with STDP robustly detects a pattern of input currents automatically encoded in the phases of a subset of its afferents, and repeating at random intervals. Remarkably, learning is possible even when only a small fraction of the afferents (ϳ10%) exhibits PoFC. The ability of STDP to detect repeating patterns had been noted before in continuous activity, but it turns out that oscillations greatly facilitate learning. A benchmark with more conventional rate-based codes demonstrates the superiority of oscillations and PoFC for both STDP-based learning and the speed of decoding: the oscillation partially formats the input spike times, so that they mainly depend on the current input currents, and can be efficiently learned by STDP and then recognized in just one oscillation cycle. This suggests a major functional role for oscillatory brain activity that has been widely reported experimentally.

show abstract

Section: Methodsmentioning

confidence: 99%

Oscillations, Phase-of-Firing Coding, and Spike Timing-Dependent Plasticity: An Efficient Learning Scheme

Masquelier¹,

Hugues²,

Deco³

et al. 2009

J. Neurosci.

149

153

View full text Add to dashboard Cite

show abstract

“…Experiments (equivalent to software simulation runs) can be defined, set-up, and carried out, using methods and commands analogous to those present in software modern neural simulators such as Brian [5] or PCSIM [6].…”

Section: The Pyncs Tool-setmentioning

confidence: 99%

Systematic configuration and automatic tuning of neuromorphic systems

Sheik

Neftci

Chicca

et al. 2011

2011 IEEE International Symposium of Circuits and Systems (ISCAS)

View full text Add to dashboard Cite

Abstract-In the past recent years several research groups have proposed neuromorphic Very Large Scale Integration (VLSI) devices that implement event-based sensors or biophysically realistic networks of spiking neurons. It has been argued that these devices can be used to build event-based systems, for solving real-world applications in real-time, with efficiencies and robustness that cannot be achieved with conventional computing technologies.In order to implement complex event-based neuromorphic systems it is necessary to interface the neuromorphic VLSI sensors and devices among each other, to robotic platforms, and to workstations (e.g. for data-logging and analysis). This apparently simple goal requires painstaking work that spans multiple levels of complexity and disciplines: from the custom layout of microelectronic circuits and asynchronous printed circuit boards, to the development of object oriented classes and methods in software; from electrical engineering and physics for analog/digital circuit design to neuroscience and computer science for neural computation and spike-based learning methods.Within this context, we present a framework we developed to simplify the configuration of multi-chip neuromorphic VLSI systems, and automate the mapping of neural network model parameters to neuromorphic circuit bias values. I. INTRODUCTIONWhile computational neuroscience models simulate neurons and synapses using parameters directly related to their biological characteristics (such as leak conductance, time constants, etc.), neuromorphic VLSI systems emulate them using circuits that can be configured by setting bias voltages and currents. The biases in these circuits are often only indirectly related to the parameters of computational neuroscience models. More generally, the relationship between parameters in theoretical models, software simulations, and hardware emulations of spiking neural networks is highly non-linear, and no systematic methodology exists for establishing it automatically.Current automated methods for mapping the VLSI circuits bias voltages to neural network type parameters are based on heuristics and result in ad-hoc custom made calibration routines. For example, in [1] the authors perform an exhaustive search of the parameter space to calibrate their hardware neural networks, using the simulator-independent description language "PyNN" [2]. This type of brute-force approach is possible because of the accelerated nature of the hardware used, but it becomes intractable for real-time hardware or for very large systems, due to the massive amount of data that must be measured and analyzed to carry out the calibration procedure. An alternative model-based approach is proposed in [3], where the authors fit data from experimental measurements with equations from transistors, circuit models,

show abstract

“…Brette et al [39] surveyed and discussed the existing work on SNN simulation in 2007. All the simulators discussed in this paper as well as the more recent Brian [23] simulator target the simulation of biological SNN. More recently, Bichler et al [12] proposed Xnet, a C++ event-driven simulator dedicated to the simulation of hardware SNNs.…”

Section: Requirements For a Spiking Neuromorphic Hardware Simulatormentioning

confidence: 99%

“…A lot of efforts have been put into developing appropriate simulation tools and techniques [12], [23]. In this paper, for implementation we use our simulator, N2S3 (Neural Network Scalable Spiking Simulator), an open source event-driven simulator, that is dedicated to the architecture exploration of hardware SNNs.…”

Section: Introductionmentioning

confidence: 99%

Parameter Exploration to Improve Performance of Memristor-Based Neuromorphic Architectures

Shahsavari

Boulet

2018

IEEE Trans. Multi-Scale Comp. Syst.

View full text Add to dashboard Cite

Abstract-The brain-inspired spiking neural network neuromorphic architecture offers a promising solution for a wide set of cognitive computation tasks at a very low power consumption. Due to the practical feasibility of hardware implementation, we present a memristor-based model of hardware spiking neural networks which we simulate with N2S3 (Neural Network Scalable Spiking Simulator), our open source neuromorphic architecture simulator. Although Spiking neural networks are widely used in the community of computational neuroscience and neuromorphic computation, there is still a need for research on the methods to choose the optimum parameters for better recognition efficiency. With the help of our simulator, we analyze and evaluate the impact of different parameters such as number of neurons, STDP window, neuron threshold, distribution of input spikes and memristor model parameters on the MNIST hand-written digit recognition problem. We show that a careful choice of a few parameters (number of neurons, kind of synapse, STDP window and neuron threshold) can significantly improve the recognition rate on this benchmark (around 15 points of improvement for the number of neurons, a few points for the others) with a variability of 4 to 5 points of recognition rate due to the random initialization of the synaptic weights.

show abstract

Brian: a simulator for spiking neural networks in Python

Cited by 154 publications

References 1 publication

Oscillations, Phase-of-Firing Coding, and Spike Timing-Dependent Plasticity: An Efficient Learning Scheme

Oscillations, Phase-of-Firing Coding, and Spike Timing-Dependent Plasticity: An Efficient Learning Scheme

Systematic configuration and automatic tuning of neuromorphic systems

Parameter Exploration to Improve Performance of Memristor-Based Neuromorphic Architectures

Contact Info

Product

Resources

About