167 research outputs found
Synaptic Sampling of Neural Networks
Probabilistic artificial neural networks offer intriguing prospects for
enabling the uncertainty of artificial intelligence methods to be described
explicitly in their function; however, the development of techniques that
quantify uncertainty by well-understood methods such as Monte Carlo sampling
has been limited by the high costs of stochastic sampling on deterministic
computing hardware. Emerging computing systems that are amenable to
hardware-level probabilistic computing, such as those that leverage stochastic
devices, may make probabilistic neural networks more feasible in the
not-too-distant future. This paper describes the scANN technique --
\textit{sampling (by coinflips) artificial neural networks} -- which enables
neural networks to be sampled directly by treating the weights as Bernoulli
coin flips. This method is natively well suited for probabilistic computing
techniques that focus on tunable stochastic devices, nearly matches fully
deterministic performance while also describing the uncertainty of correct and
incorrect neural network outputs.Comment: 9 pages, accepted to 2023 IEEE International Conference on Rebooting
Computin
Dynamic Analysis of Executables to Detect and Characterize Malware
It is needed to ensure the integrity of systems that process sensitive
information and control many aspects of everyday life. We examine the use of
machine learning algorithms to detect malware using the system calls generated
by executables-alleviating attempts at obfuscation as the behavior is monitored
rather than the bytes of an executable. We examine several machine learning
techniques for detecting malware including random forests, deep learning
techniques, and liquid state machines. The experiments examine the effects of
concept drift on each algorithm to understand how well the algorithms
generalize to novel malware samples by testing them on data that was collected
after the training data. The results suggest that each of the examined machine
learning algorithms is a viable solution to detect malware-achieving between
90% and 95% class-averaged accuracy (CAA). In real-world scenarios, the
performance evaluation on an operational network may not match the performance
achieved in training. Namely, the CAA may be about the same, but the values for
precision and recall over the malware can change significantly. We structure
experiments to highlight these caveats and offer insights into expected
performance in operational environments. In addition, we use the induced models
to gain a better understanding about what differentiates the malware samples
from the goodware, which can further be used as a forensics tool to understand
what the malware (or goodware) was doing to provide directions for
investigation and remediation.Comment: 9 pages, 6 Tables, 4 Figure
Neurogenesis Deep Learning
Neural machine learning methods, such as deep neural networks (DNN), have
achieved remarkable success in a number of complex data processing tasks. These
methods have arguably had their strongest impact on tasks such as image and
audio processing - data processing domains in which humans have long held clear
advantages over conventional algorithms. In contrast to biological neural
systems, which are capable of learning continuously, deep artificial networks
have a limited ability for incorporating new information in an already trained
network. As a result, methods for continuous learning are potentially highly
impactful in enabling the application of deep networks to dynamic data sets.
Here, inspired by the process of adult neurogenesis in the hippocampus, we
explore the potential for adding new neurons to deep layers of artificial
neural networks in order to facilitate their acquisition of novel information
while preserving previously trained data representations. Our results on the
MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes
lower and upper case letters and digits, demonstrate that neurogenesis is well
suited for addressing the stability-plasticity dilemma that has long challenged
adaptive machine learning algorithms.Comment: 8 pages, 8 figures, Accepted to 2017 International Joint Conference
on Neural Networks (IJCNN 2017
A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines
Information in neural networks is represented as weighted connections, or
synapses, between neurons. This poses a problem as the primary computational
bottleneck for neural networks is the vector-matrix multiply when inputs are
multiplied by the neural network weights. Conventional processing architectures
are not well suited for simulating neural networks, often requiring large
amounts of energy and time. Additionally, synapses in biological neural
networks are not binary connections, but exhibit a nonlinear response function
as neurotransmitters are emitted and diffuse between neurons. Inspired by
neuroscience principles, we present a digital neuromorphic architecture, the
Spiking Temporal Processing Unit (STPU), capable of modeling arbitrary complex
synaptic response functions without requiring additional hardware components.
We consider the paradigm of spiking neurons with temporally coded information
as opposed to non-spiking rate coded neurons used in most neural networks. In
this paradigm we examine liquid state machines applied to speech recognition
and show how a liquid state machine with temporal dynamics maps onto the
STPU-demonstrating the flexibility and efficiency of the STPU for instantiating
neural algorithms.Comment: 8 pages, 4 Figures, Preprint of 2017 IJCN
Magnetic Tunnel Junction Random Number Generators Applied to Dynamically Tuned Probability Trees Driven by Spin Orbit Torque
Perpendicular magnetic tunnel junction (pMTJ)-based true-random number
generators (RNG) can consume orders of magnitude less energy per bit than CMOS
pseudo-RNG. Here, we numerically investigate with a macrospin
Landau-Lifshitz-Gilbert equation solver the use of pMTJs driven by spin-orbit
torque to directly sample numbers from arbitrary probability distributions with
the help of a tunable probability tree. The tree operates by dynamically
biasing sequences of pMTJ relaxation events, called 'coinflips', via an
additional applied spin-transfer-torque current. Specifically, using a single,
ideal pMTJ device we successfully draw integer samples on the interval 0,255
from an exponential distribution based on p-value distribution analysis. In
order to investigate device-to-device variations, the thermal stability of the
pMTJs are varied based on manufactured device data. It is found that while
repeatedly using a varied device inhibits ability to recover the probability
distribution, the device variations average out when considering the entire set
of devices as a 'bucket' to agnostically draw random numbers from. Further, it
is noted that the device variations most significantly impact the highest level
of the probability tree, iwth diminishing errors at lower levels. The devices
are then used to draw both uniformly and exponentially distributed numbers for
the Monte Carlo computation of a problem from particle transport, showing
excellent data fit with the analytical solution. Finally, the devices are
benchmarked against CMOS and memristor RNG, showing faster bit generation and
significantly lower energy use.Comment: 10 pages, 8 figures, 2 table
The Role of Additive Neurogenesis and Synaptic Plasticity in a Hippocampal Memory Model with Grid-Cell Like Input
Recently, we presented a study of adult neurogenesis in a simplified hippocampal memory model. The network was required to encode and decode memory patterns despite changing input statistics. We showed that additive neurogenesis was a more effective adaptation strategy compared to neuronal turnover and conventional synaptic plasticity as it allowed the network to respond to changes in the input statistics while preserving representations of earlier environments. Here we extend our model to include realistic, spatially driven input firing patterns in the form of grid cells in the entorhinal cortex. We compare network performance across a sequence of spatial environments using three distinct adaptation strategies: conventional synaptic plasticity, where the network is of fixed size but the connectivity is plastic; neuronal turnover, where the network is of fixed size but units in the network may die and be replaced; and additive neurogenesis, where the network starts out with fewer initial units but grows over time. We confirm that additive neurogenesis is a superior adaptation strategy when using realistic, spatially structured input patterns. We then show that a more biologically plausible neurogenesis rule that incorporates cell death and enhanced plasticity of new granule cells has an overall performance significantly better than any one of the three individual strategies operating alone. This adaptation rule can be tailored to maximise performance of the network when operating as either a short- or long-term memory store. We also examine the time course of adult neurogenesis over the lifetime of an animal raised under different hypothetical rearing conditions. These growth profiles have several distinct features that form a theoretical prediction that could be tested experimentally. Finally, we show that place cells can emerge and refine in a realistic manner in our model as a direct result of the sparsification performed by the dentate gyrus layer
Learning to Learn: Theta Oscillations Predict New Learning, which Enhances Related Learning and Neurogenesis
Animals in the natural world continuously encounter learning experiences of varying degrees of novelty. New neurons in the hippocampus are especially responsive to learning associations between novel events and more cells survive if a novel and challenging task is learned. One might wonder whether new neurons would be rescued from death upon each new learning experience or whether there is an internal control system that limits the number of cells that are retained as a function of learning. In this experiment, it was hypothesized that learning a task that was similar in content to one already learned previously would not increase cell survival. We further hypothesized that in situations in which the cells are rescued hippocampal theta oscillations (3β12 Hz) would be involved and perhaps necessary for increasing cell survival. Both hypotheses were disproved. Adult male Sprague-Dawley rats were trained on two similar hippocampus-dependent tasks, trace and very-long delay eyeblink conditioning, while recording hippocampal local-field potentials. Cells that were generated after training on the first task were labeled with bromodeoxyuridine and quantified after training on both tasks had ceased. Spontaneous theta activity predicted performance on the first task and the conditioned stimulus induced a theta-band response early in learning the first task. As expected, performance on the first task correlated with performance on the second task. However, theta activity did not increase during training on the second task, even though more cells were present in animals that had learned. Therefore, as long as learning occurs, relatively small changes in the environment are sufficient to increase the number of surviving neurons in the adult hippocampus and they can do so in the absence of an increase in theta activity. In conclusion, these data argue against an upper limit on the number of neurons that can be rescued from death by learning
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