151 research outputs found
Demonstrating Advantages of Neuromorphic Computation: A Pilot Study
Neuromorphic devices represent an attempt to mimic aspects of the brain's
architecture and dynamics with the aim of replicating its hallmark functional
capabilities in terms of computational power, robust learning and energy
efficiency. We employ a single-chip prototype of the BrainScaleS 2 neuromorphic
system to implement a proof-of-concept demonstration of reward-modulated
spike-timing-dependent plasticity in a spiking network that learns to play the
Pong video game by smooth pursuit. This system combines an electronic
mixed-signal substrate for emulating neuron and synapse dynamics with an
embedded digital processor for on-chip learning, which in this work also serves
to simulate the virtual environment and learning agent. The analog emulation of
neuronal membrane dynamics enables a 1000-fold acceleration with respect to
biological real-time, with the entire chip operating on a power budget of 57mW.
Compared to an equivalent simulation using state-of-the-art software, the
on-chip emulation is at least one order of magnitude faster and three orders of
magnitude more energy-efficient. We demonstrate how on-chip learning can
mitigate the effects of fixed-pattern noise, which is unavoidable in analog
substrates, while making use of temporal variability for action exploration.
Learning compensates imperfections of the physical substrate, as manifested in
neuronal parameter variability, by adapting synaptic weights to match
respective excitability of individual neurons.Comment: Added measurements with noise in NEST simulation, add notice about
journal publication. Frontiers in Neuromorphic Engineering (2019
Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate
We present first experimental results on the novel BrainScaleS-2 neuromorphic
architecture based on an analog neuro-synaptic core and augmented by embedded
microprocessors for complex plasticity and experiment control. The high
acceleration factor of 1000 compared to biological dynamics enables the
execution of computationally expensive tasks, by allowing the fast emulation of
long-duration experiments or rapid iteration over many consecutive trials. The
flexibility of our architecture is demonstrated in a suite of five distinct
experiments, which emphasize different aspects of the BrainScaleS-2 system
Emulating insect brains for neuromorphic navigation
Bees display the remarkable ability to return home in a straight line after
meandering excursions to their environment. Neurobiological imaging studies
have revealed that this capability emerges from a path integration mechanism
implemented within the insect's brain. In the present work, we emulate this
neural network on the neuromorphic mixed-signal processor BrainScaleS-2 to
guide bees, virtually embodied on a digital co-processor, back to their home
location after randomly exploring their environment. To realize the underlying
neural integrators, we introduce single-neuron spike-based short-term memory
cells with axo-axonic synapses. All entities, including environment, sensory
organs, brain, actuators, and the virtual body, run autonomously on a single
BrainScaleS-2 microchip. The functioning network is fine-tuned for better
precision and reliability through an evolution strategy. As BrainScaleS-2
emulates neural processes 1000 times faster than biology, 4800 consecutive bee
journeys distributed over 320 generations occur within only half an hour on a
single neuromorphic core
Six networks on a universal neuromorphic computing substrate
In this study, we present a highly configurable neuromorphic computing substrate and use it for emulating several types of neural networks. At the heart of this system lies a mixed-signal chip, with analog implementations of neurons and synapses and digital transmission of action potentials. Major advantages of this emulation device, which has been explicitly designed as a universal neural network emulator, are its inherent parallelism and high acceleration factor compared to conventional computers. Its configurability allows the realization of almost arbitrary network topologies and the use of widely varied neuronal and synaptic parameters. Fixed-pattern noise inherent to analog circuitry is reduced by calibration routines. An integrated development environment allows neuroscientists to operate the device without any prior knowledge of neuromorphic circuit design. As a showcase for the capabilities of the system, we describe the successful emulation of six different neural networks which cover a broad spectrum of both structure and functionality
Structural plasticity on an accelerated analog neuromorphic hardware system
In computational neuroscience, as well as in machine learning, neuromorphic
devices promise an accelerated and scalable alternative to neural network
simulations. Their neural connectivity and synaptic capacity depends on their
specific design choices, but is always intrinsically limited. Here, we present
a strategy to achieve structural plasticity that optimizes resource allocation
under these constraints by constantly rewiring the pre- and gpostsynaptic
partners while keeping the neuronal fan-in constant and the connectome sparse.
In particular, we implemented this algorithm on the analog neuromorphic system
BrainScaleS-2. It was executed on a custom embedded digital processor located
on chip, accompanying the mixed-signal substrate of spiking neurons and synapse
circuits. We evaluated our implementation in a simple supervised learning
scenario, showing its ability to optimize the network topology with respect to
the nature of its training data, as well as its overall computational
efficiency
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