7,104 research outputs found
Principles of Neuromorphic Photonics
In an age overrun with information, the ability to process reams of data has
become crucial. The demand for data will continue to grow as smart gadgets
multiply and become increasingly integrated into our daily lives.
Next-generation industries in artificial intelligence services and
high-performance computing are so far supported by microelectronic platforms.
These data-intensive enterprises rely on continual improvements in hardware.
Their prospects are running up against a stark reality: conventional
one-size-fits-all solutions offered by digital electronics can no longer
satisfy this need, as Moore's law (exponential hardware scaling),
interconnection density, and the von Neumann architecture reach their limits.
With its superior speed and reconfigurability, analog photonics can provide
some relief to these problems; however, complex applications of analog
photonics have remained largely unexplored due to the absence of a robust
photonic integration industry. Recently, the landscape for
commercially-manufacturable photonic chips has been changing rapidly and now
promises to achieve economies of scale previously enjoyed solely by
microelectronics.
The scientific community has set out to build bridges between the domains of
photonic device physics and neural networks, giving rise to the field of
\emph{neuromorphic photonics}. This article reviews the recent progress in
integrated neuromorphic photonics. We provide an overview of neuromorphic
computing, discuss the associated technology (microelectronic and photonic)
platforms and compare their metric performance. We discuss photonic neural
network approaches and challenges for integrated neuromorphic photonic
processors while providing an in-depth description of photonic neurons and a
candidate interconnection architecture. We conclude with a future outlook of
neuro-inspired photonic processing.Comment: 28 pages, 19 figure
An Efficient Method for online Detection of Polychronous Patterns in Spiking Neural Network
Polychronous neural groups are effective structures for the recognition of
precise spike-timing patterns but the detection method is an inefficient
multi-stage brute force process that works off-line on pre-recorded simulation
data. This work presents a new model of polychronous patterns that can capture
precise sequences of spikes directly in the neural simulation. In this scheme,
each neuron is assigned a randomized code that is used to tag the post-synaptic
neurons whenever a spike is transmitted. This creates a polychronous code that
preserves the order of pre-synaptic activity and can be registered in a hash
table when the post-synaptic neuron spikes. A polychronous code is a
sub-component of a polychronous group that will occur, along with others, when
the group is active. We demonstrate the representational and pattern
recognition ability of polychronous codes on a direction selective visual task
involving moving bars that is typical of a computation performed by simple
cells in the cortex. The computational efficiency of the proposed algorithm far
exceeds existing polychronous group detection methods and is well suited for
online detection.Comment: 17 pages, 8 figure
Graded, Dynamically Routable Information Processing with Synfire-Gated Synfire Chains
Coherent neural spiking and local field potentials are believed to be
signatures of the binding and transfer of information in the brain. Coherent
activity has now been measured experimentally in many regions of mammalian
cortex. Synfire chains are one of the main theoretical constructs that have
been appealed to to describe coherent spiking phenomena. However, for some
time, it has been known that synchronous activity in feedforward networks
asymptotically either approaches an attractor with fixed waveform and
amplitude, or fails to propagate. This has limited their ability to explain
graded neuronal responses. Recently, we have shown that pulse-gated synfire
chains are capable of propagating graded information coded in mean population
current or firing rate amplitudes. In particular, we showed that it is possible
to use one synfire chain to provide gating pulses and a second, pulse-gated
synfire chain to propagate graded information. We called these circuits
synfire-gated synfire chains (SGSCs). Here, we present SGSCs in which graded
information can rapidly cascade through a neural circuit, and show a
correspondence between this type of transfer and a mean-field model in which
gating pulses overlap in time. We show that SGSCs are robust in the presence of
variability in population size, pulse timing and synaptic strength. Finally, we
demonstrate the computational capabilities of SGSC-based information coding by
implementing a self-contained, spike-based, modular neural circuit that is
triggered by, then reads in streaming input, processes the input, then makes a
decision based on the processed information and shuts itself down
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