683 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
Inherent Weight Normalization in Stochastic Neural Networks
Multiplicative stochasticity such as Dropout improves the robustness and
generalizability of deep neural networks. Here, we further demonstrate that
always-on multiplicative stochasticity combined with simple threshold neurons
are sufficient operations for deep neural networks. We call such models Neural
Sampling Machines (NSM). We find that the probability of activation of the NSM
exhibits a self-normalizing property that mirrors Weight Normalization, a
previously studied mechanism that fulfills many of the features of Batch
Normalization in an online fashion. The normalization of activities during
training speeds up convergence by preventing internal covariate shift caused by
changes in the input distribution. The always-on stochasticity of the NSM
confers the following advantages: the network is identical in the inference and
learning phases, making the NSM suitable for online learning, it can exploit
stochasticity inherent to a physical substrate such as analog non-volatile
memories for in-memory computing, and it is suitable for Monte Carlo sampling,
while requiring almost exclusively addition and comparison operations. We
demonstrate NSMs on standard classification benchmarks (MNIST and CIFAR) and
event-based classification benchmarks (N-MNIST and DVS Gestures). Our results
show that NSMs perform comparably or better than conventional artificial neural
networks with the same architecture
Efficient Computation in Adaptive Artificial Spiking Neural Networks
Artificial Neural Networks (ANNs) are bio-inspired models of neural
computation that have proven highly effective. Still, ANNs lack a natural
notion of time, and neural units in ANNs exchange analog values in a
frame-based manner, a computationally and energetically inefficient form of
communication. This contrasts sharply with biological neurons that communicate
sparingly and efficiently using binary spikes. While artificial Spiking Neural
Networks (SNNs) can be constructed by replacing the units of an ANN with
spiking neurons, the current performance is far from that of deep ANNs on hard
benchmarks and these SNNs use much higher firing rates compared to their
biological counterparts, limiting their efficiency. Here we show how spiking
neurons that employ an efficient form of neural coding can be used to construct
SNNs that match high-performance ANNs and exceed state-of-the-art in SNNs on
important benchmarks, while requiring much lower average firing rates. For
this, we use spike-time coding based on the firing rate limiting adaptation
phenomenon observed in biological spiking neurons. This phenomenon can be
captured in adapting spiking neuron models, for which we derive the effective
transfer function. Neural units in ANNs trained with this transfer function can
be substituted directly with adaptive spiking neurons, and the resulting
Adaptive SNNs (AdSNNs) can carry out inference in deep neural networks using up
to an order of magnitude fewer spikes compared to previous SNNs. Adaptive
spike-time coding additionally allows for the dynamic control of neural coding
precision: we show how a simple model of arousal in AdSNNs further halves the
average required firing rate and this notion naturally extends to other forms
of attention. AdSNNs thus hold promise as a novel and efficient model for
neural computation that naturally fits to temporally continuous and
asynchronous applications
Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks
Biological neurons communicate with a sparing exchange of pulses - spikes. It
is an open question how real spiking neurons produce the kind of powerful
neural computation that is possible with deep artificial neural networks, using
only so very few spikes to communicate. Building on recent insights in
neuroscience, we present an Adapting Spiking Neural Network (ASNN) based on
adaptive spiking neurons. These spiking neurons efficiently encode information
in spike-trains using a form of Asynchronous Pulsed Sigma-Delta coding while
homeostatically optimizing their firing rate. In the proposed paradigm of
spiking neuron computation, neural adaptation is tightly coupled to synaptic
plasticity, to ensure that downstream neurons can correctly decode upstream
spiking neurons. We show that this type of network is inherently able to carry
out asynchronous and event-driven neural computation, while performing
identical to corresponding artificial neural networks (ANNs). In particular, we
show that these adaptive spiking neurons can be drop in replacements for ReLU
neurons in standard feedforward ANNs comprised of such units. We demonstrate
that this can also be successfully applied to a ReLU based deep convolutional
neural network for classifying the MNIST dataset. The ASNN thus outperforms
current Spiking Neural Networks (SNNs) implementations, while responding (up
to) an order of magnitude faster and using an order of magnitude fewer spikes.
Additionally, in a streaming setting where frames are continuously classified,
we show that the ASNN requires substantially fewer network updates as compared
to the corresponding ANN
Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks
Biological neurons communicate with a sparing exchange of pulses - spikes. It
is an open question how real spiking neurons produce the kind of powerful
neural computation that is possible with deep artificial neural networks, using
only so very few spikes to communicate. Building on recent insights in
neuroscience, we present an Adapting Spiking Neural Network (ASNN) based on
adaptive spiking neurons. These spiking neurons efficiently encode information
in spike-trains using a form of Asynchronous Pulsed Sigma-Delta coding while
homeostatically optimizing their firing rate. In the proposed paradigm of
spiking neuron computation, neural adaptation is tightly coupled to synaptic
plasticity, to ensure that downstream neurons can correctly decode upstream
spiking neurons. We show that this type of network is inherently able to carry
out asynchronous and event-driven neural computation, while performing
identical to corresponding artificial neural networks (ANNs). In particular, we
show that these adaptive spiking neurons can be drop in replacements for ReLU
neurons in standard feedforward ANNs comprised of such units. We demonstrate
that this can also be successfully applied to a ReLU based deep convolutional
neural network for classifying the MNIST dataset. The ASNN thus outperforms
current Spiking Neural Networks (SNNs) implementations, while responding (up
to) an order of magnitude faster and using an order of magnitude fewer spikes.
Additionally, in a streaming setting where frames are continuously classified,
we show that the ASNN requires substantially fewer network updates as compared
to the corresponding ANN
Gibbs Sampling with Low-Power Spiking Digital Neurons
Restricted Boltzmann Machines and Deep Belief Networks have been successfully
used in a wide variety of applications including image classification and
speech recognition. Inference and learning in these algorithms uses a Markov
Chain Monte Carlo procedure called Gibbs sampling. A sigmoidal function forms
the kernel of this sampler which can be realized from the firing statistics of
noisy integrate-and-fire neurons on a neuromorphic VLSI substrate. This paper
demonstrates such an implementation on an array of digital spiking neurons with
stochastic leak and threshold properties for inference tasks and presents some
key performance metrics for such a hardware-based sampler in both the
generative and discriminative contexts.Comment: Accepted at ISCAS 201
Constructive spiking neural networks for simulations of neuroplasticity
Artificial neural networks are important tools in machine learning and neuroscience;
however, a difficult step in their implementation is the selection of the neural network size and
structure. This thesis develops fundamental theory on algorithms for constructing neurons in
spiking neural networks and simulations of neuroplasticity. This theory is applied in the
development of a constructive algorithm based on spike-timing- dependent plasticity (STDP) that
achieves continual one-shot learning of hidden spike patterns through neuron construction.
The theoretical developments in this thesis begin with the proposal of a set of definitions of
the fundamental components of constructive neural networks. Disagreement in terminology across the
literature and a lack of clear definitions and requirements for constructive neural networks is a
factor in the poor visibility and fragmentation of research. The proposed definitions are used as
the basis for a generalised methodology for decomposing constructive neural networks into
components to perform comparisons, design and analysis.
Spiking neuron models are uncommon in constructive neural network literature; however, spiking
neurons are common in simulated studies in neuroscience. Spike- timing-dependent construction is
proposed as a distinct class of constructive algorithm for spiking neural networks. Past algorithms
that perform spike-timing-dependent construction are decomposed into defined components for a
detailed critical comparison and found to have limited applicability in simulations of biological
neural networks.
This thesis develops concepts and principles for designing constructive algorithms that are
compatible with simulations of biological neural networks. Simulations often have orders of
magnitude fewer neurons than related biological neural systems; there- fore, the neurons in a
simulation may be assumed to be a selection or subset of a larger neural system with many neurons
not simulated. Neuron construction and pruning may therefore be reinterpreted as the transfer of
neurons between sets of simulated neurons and hypothetical neurons in the neural system.
Constructive algorithms with a functional equivalence to transferring neurons between sets allow
simulated neural networks to maintain biological plausibility while changing size.
The components of a novel constructive algorithm are incrementally developed from the principles
for biological plausibility. First, processes for calculating new synapse weights from observed
simulation activity and estimates of past STDP are developed and analysed. Second, a method for
predicting postsynaptic spike times for synapse weight calculations through the simulation of a proxy for hypothetical neurons is developed. Finally, spike-dependent conditions for neuron construction and pruning are developed and
the processes are combined in a constructive algorithm for simulations of STDP.
Repeating hidden spike patterns can be detected by neurons tuned through STDP; this result is
reproduced in STDP simulations with neuron construction. Tuned neurons become unresponsive to other
activity, preventing detuning but also preventing neurons from learning new spike patterns.
Continual learning is demonstrated through neuron construction with immediate detection of new
spike patterns from one-shot predictions of STDP convergence.
Future research may investigate applications of the developed constructive algorithm in
neuroscience and machine learning. The developed theory on constructive neural networks and
concepts of selective simulation of neurons also provide new directions for future research.Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 201
Seeing into Darkness: Scotopic Visual Recognition
Images are formed by counting how many photons traveling from a given set of
directions hit an image sensor during a given time interval. When photons are
few and far in between, the concept of `image' breaks down and it is best to
consider directly the flow of photons. Computer vision in this regime, which we
call `scotopic', is radically different from the classical image-based paradigm
in that visual computations (classification, control, search) have to take
place while the stream of photons is captured and decisions may be taken as
soon as enough information is available. The scotopic regime is important for
biomedical imaging, security, astronomy and many other fields. Here we develop
a framework that allows a machine to classify objects with as few photons as
possible, while maintaining the error rate below an acceptable threshold. A
dynamic and asymptotically optimal speed-accuracy tradeoff is a key feature of
this framework. We propose and study an algorithm to optimize the tradeoff of a
convolutional network directly from lowlight images and evaluate on simulated
images from standard datasets. Surprisingly, scotopic systems can achieve
comparable classification performance as traditional vision systems while using
less than 0.1% of the photons in a conventional image. In addition, we
demonstrate that our algorithms work even when the illuminance of the
environment is unknown and varying. Last, we outline a spiking neural network
coupled with photon-counting sensors as a power-efficient hardware realization
of scotopic algorithms.Comment: 23 pages, 6 figure
SuperSpike: Supervised learning in multi-layer spiking neural networks
A vast majority of computation in the brain is performed by spiking neural
networks. Despite the ubiquity of such spiking, we currently lack an
understanding of how biological spiking neural circuits learn and compute
in-vivo, as well as how we can instantiate such capabilities in artificial
spiking circuits in-silico. Here we revisit the problem of supervised learning
in temporally coding multi-layer spiking neural networks. First, by using a
surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based
three factor learning rule capable of training multi-layer networks of
deterministic integrate-and-fire neurons to perform nonlinear computations on
spatiotemporal spike patterns. Second, inspired by recent results on feedback
alignment, we compare the performance of our learning rule under different
credit assignment strategies for propagating output errors to hidden units.
Specifically, we test uniform, symmetric and random feedback, finding that
simpler tasks can be solved with any type of feedback, while more complex tasks
require symmetric feedback. In summary, our results open the door to obtaining
a better scientific understanding of learning and computation in spiking neural
networks by advancing our ability to train them to solve nonlinear problems
involving transformations between different spatiotemporal spike-time patterns
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