7,859 research outputs found
Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines
Recent studies have shown that synaptic unreliability is a robust and
sufficient mechanism for inducing the stochasticity observed in cortex. Here,
we introduce Synaptic Sampling Machines, a class of neural network models that
uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised
learning. Similar to the original formulation of Boltzmann machines, these
models can be viewed as a stochastic counterpart of Hopfield networks, but
where stochasticity is induced by a random mask over the connections. Synaptic
stochasticity plays the dual role of an efficient mechanism for sampling, and a
regularizer during learning akin to DropConnect. A local synaptic plasticity
rule implementing an event-driven form of contrastive divergence enables the
learning of generative models in an on-line fashion. Synaptic sampling machines
perform equally well using discrete-timed artificial units (as in Hopfield
networks) or continuous-timed leaky integrate & fire neurons. The learned
representations are remarkably sparse and robust to reductions in bit precision
and synapse pruning: removal of more than 75% of the weakest connections
followed by cursory re-learning causes a negligible performance loss on
benchmark classification tasks. The spiking neuron-based synaptic sampling
machines outperform existing spike-based unsupervised learners, while
potentially offering substantial advantages in terms of power and complexity,
and are thus promising models for on-line learning in brain-inspired hardware
GeNN: a code generation framework for accelerated brain simulations
Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ.
GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials,
Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/
On-chip Few-shot Learning with Surrogate Gradient Descent on a Neuromorphic Processor
Recent work suggests that synaptic plasticity dynamics in biological models
of neurons and neuromorphic hardware are compatible with gradient-based
learning (Neftci et al., 2019). Gradient-based learning requires iterating
several times over a dataset, which is both time-consuming and constrains the
training samples to be independently and identically distributed. This is
incompatible with learning systems that do not have boundaries between training
and inference, such as in neuromorphic hardware. One approach to overcome these
constraints is transfer learning, where a portion of the network is pre-trained
and mapped into hardware and the remaining portion is trained online. Transfer
learning has the advantage that pre-training can be accelerated offline if the
task domain is known, and few samples of each class are sufficient for learning
the target task at reasonable accuracies. Here, we demonstrate on-line
surrogate gradient few-shot learning on Intel's Loihi neuromorphic research
processor using features pre-trained with spike-based gradient
backpropagation-through-time. Our experimental results show that the Loihi chip
can learn gestures online using a small number of shots and achieve results
that are comparable to the models simulated on a conventional processor
Artificial Neural Network Pruning to Extract Knowledge
Artificial Neural Networks (NN) are widely used for solving complex problems
from medical diagnostics to face recognition. Despite notable successes, the
main disadvantages of NN are also well known: the risk of overfitting, lack of
explainability (inability to extract algorithms from trained NN), and high
consumption of computing resources. Determining the appropriate specific NN
structure for each problem can help overcome these difficulties: Too poor NN
cannot be successfully trained, but too rich NN gives unexplainable results and
may have a high chance of overfitting. Reducing precision of NN parameters
simplifies the implementation of these NN, saves computing resources, and makes
the NN skills more transparent. This paper lists the basic NN simplification
problems and controlled pruning procedures to solve these problems. All the
described pruning procedures can be implemented in one framework. The developed
procedures, in particular, find the optimal structure of NN for each task,
measure the influence of each input signal and NN parameter, and provide a
detailed verbal description of the algorithms and skills of NN. The described
methods are illustrated by a simple example: the generation of explicit
algorithms for predicting the results of the US presidential election.Comment: IJCNN 202
On the role of synaptic stochasticity in training low-precision neural networks
Stochasticity and limited precision of synaptic weights in neural network
models are key aspects of both biological and hardware modeling of learning
processes. Here we show that a neural network model with stochastic binary
weights naturally gives prominence to exponentially rare dense regions of
solutions with a number of desirable properties such as robustness and good
generalization performance, while typical solutions are isolated and hard to
find. Binary solutions of the standard perceptron problem are obtained from a
simple gradient descent procedure on a set of real values parametrizing a
probability distribution over the binary synapses. Both analytical and
numerical results are presented. An algorithmic extension aimed at training
discrete deep neural networks is also investigated.Comment: 7 pages + 14 pages of supplementary materia
Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective
On metrics of density and power efficiency, neuromorphic technologies have
the potential to surpass mainstream computing technologies in tasks where
real-time functionality, adaptability, and autonomy are essential. While
algorithmic advances in neuromorphic computing are proceeding successfully, the
potential of memristors to improve neuromorphic computing have not yet born
fruit, primarily because they are often used as a drop-in replacement to
conventional memory. However, interdisciplinary approaches anchored in machine
learning theory suggest that multifactor plasticity rules matching neural and
synaptic dynamics to the device capabilities can take better advantage of
memristor dynamics and its stochasticity. Furthermore, such plasticity rules
generally show much higher performance than that of classical Spike Time
Dependent Plasticity (STDP) rules. This chapter reviews the recent development
in learning with spiking neural network models and their possible
implementation with memristor-based hardware
Simulation of networks of spiking neurons: A review of tools and strategies
We review different aspects of the simulation of spiking neural networks. We
start by reviewing the different types of simulation strategies and algorithms
that are currently implemented. We next review the precision of those
simulation strategies, in particular in cases where plasticity depends on the
exact timing of the spikes. We overview different simulators and simulation
environments presently available (restricted to those freely available, open
source and documented). For each simulation tool, its advantages and pitfalls
are reviewed, with an aim to allow the reader to identify which simulator is
appropriate for a given task. Finally, we provide a series of benchmark
simulations of different types of networks of spiking neurons, including
Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based
or conductance-based synapses, using clock-driven or event-driven integration
strategies. The same set of models are implemented on the different simulators,
and the codes are made available. The ultimate goal of this review is to
provide a resource to facilitate identifying the appropriate integration
strategy and simulation tool to use for a given modeling problem related to
spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of
Computational Neuroscience, in press (2007
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