Copyright © 2013 Wall and Glackin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these termsPeer reviewedFinal Published versio

Glackin, Cornelius

Wall, Julie

Frontiers in Computational Neuroscience

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Copyright © 2013 Wall and Glackin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these termsPeer reviewe

University of Hertfordshire Research Archive

EDITORIALpublished: 19 December 2013doi: 10.3389/fncom.2013.00182Spiking neural network connectivity and its potential fortemporal sensory processing and variable bindingJulie Wall1* and Cornelius Glackin21 Multimedia and Vision Research Group, School of Electronic Engineering and Computer Science, Queen Mary, University of London, London, UK2 Adaptive Systems Research Group, Department of Computer Science, University of Hertfordshire, Hatfield, Hertfordshire, UK*Correspondence: julie.wall@qmul.ac.ukEdited by:Misha Tsodyks, Weizmann Institute of Science, IsraelKeywords: cell assembly, spiking neural network, spike timing, biological neurons, learning, connectivity, sensory processingThe most biologically-inspired artificial neurons are those of thethird generation, and are termed spiking neurons, as individualpulses or spikes are the means by which stimuli are commu-nicated. In essence, a spike is a short-term change in electricalpotential and is the basis of communication between biologicalneurons. Unlike previous generations of artificial neurons, spik-ing neurons operate in the temporal domain, and exploit timeas a resource in their computation. In 1952, Alan Lloyd Hodgkinand Andrew Huxley produced the first model of a spiking neu-ron; their model describes the complex electro-chemical processthat enables spikes to propagate through, and hence be com-municated by, spiking neurons. Since this time, improvementsin experimental procedures in neurobiology, particularly within vivo experiments, have provided an increasingly more com-plex understanding of biological neurons. For example, it is nowwell-understood that the propagation of spikes between neuronsrequires neurotransmitter, which is typically of limited supply.When the supply is exhausted neurons become unresponsive. Themorphology of neurons, number of receptor sites, amongst manyother factors, means that neurons consume the supply of neu-rotransmitter at different rates. This in turn produces variationsover time in the responsiveness of neurons, yielding various com-putational capabilities. Such improvements in the understandingof the biological neuron have culminated in a wide range of dif-ferent neuron models, ranging from the computationally efficientto the biologically realistic. These models enable the modeling ofneural circuits found in the brain.In recent years, much of the focus in neuron modeling hasmoved to the study of the connectivity of spiking neural net-works. Spiking neural networks provide a vehicle to understandfrom a computational perspective, aspects of the brain’s neuralcircuitry. This understanding can then be used to tackle someof the historically intractable issues with artificial neurons, suchas scalability and lack of variable binding. Current knowledge offeed-forward, lateral, and recurrent connectivity of spiking neu-rons, and the interplay between excitatory and inhibitory neuronsis beginning to shed light on these issues, by improved under-standing of the temporal processing capabilities and synchronousbehavior of biological neurons. This research topic spans currentresearch on neuron models to spiking neural networks and theirapplication to interesting and current computational problems.The research papers submitted to this topic can be categorizedinto the following major areas of more efficient neuron model-ing; lateral and recurrent spiking neural network connectivity;exploitation of biological neural circuitry by means of spikingneural networks; optimization of spiking neural networks; andspiking neural networks for sensory processing.Moujahid and d’Anjou (2012) stimulate the giant squidaxon with simulated spikes to develop some new insights intothe development of more relevant models of biological neu-rons. They observed that temperature mediates the efficiency ofaction potentials by reducing the overlap between sodium andpotassium currents in the ion exchange and subsequent energyconsumption. The original research article by Dockendorf andSrinivasa (2013) falls into the area of lateral and recurrent spik-ing neural network connectivity. It presents a recurrent spikingmodel capable of learning episodes featuring missing and noisydata. The presented topology provides a means of recalling previ-ously encoded patterns where inhibition is of the high frequencyvariety aiming to promote stability of the network. Kaplan et al.(2013) also investigated the use of recurrent spiking connectiv-ity in their work on motion-based prediction and the issue ofmissing data. Here they address how anisotropic connectivity pat-terns that consider the tuning properties of neurons efficientlypredict the trajectory of a disappearing moving stimulus. Theydemonstrate and test this by simulating the network response ina moving-dot blanking experiment.Garrido et al. (2013) investigate how systematic modificationsof synaptic weights can exert close control over the timing of spiketransmissions. They demonstrate this using a network of leakyintegrate-and-fire spiking neurons to simulate cells of the cere-bellar granular layer. Börgers and Walker (2013) investigate sim-ulations of excitatory pyramidal cells and inhibitory interneuronswhich interact and exhibit gamma rhythms in the hippocam-pus and neocortex. They focus on how inhibitory interneuronsmaintain synchrony using gap junctions. Similarly, Ponulak andHopfield (2013) also take inspiration from the neural structureof the hippocampus to hypothesize about the problem of spa-tial navigation. Their topology encodes the spatial environmentthrough an exploratory phase which utilizes “place” cells to reflectall possible trajectory boundaries and environmental constraints.Subsequently, a wave propagation process maps the trajectorybetween the target or multiple targets and the current location byaltering the synaptic connectivity of the aforementioned “place”cells in a single pass. A novel viewpoint of the state-of-the-art forthe exploitation of biological neural circuitry by means of spik-ing neural networks is provided by Aimone and Weick (2013). Intheir paper, a thorough and comprehensive review of modelingFrontiers in Computational Neuroscience www.frontiersin.org December 2013 | Volume 7 | Article 182 | 1COMPUTATIONAL NEUROSCIENCEWall and Glackin Spiking neural connectivity, variable bindingcortical damage due to stroke is provided. They argue that a the-oretical understanding of the damaged cortical area post-diseaseis vital while taking into account current thinking of models foradult neurogenesis.One of the issues with modeling large-scale spiking neural net-works is the lack of tools to analyse such a large parameter space,as Buice and Chow (2013) discuss in their hypothesis and theoryarticle. They propose a possible approach which combines meanfield theory with information about spiking correlations; thusreducing the complexity to that of a more comprehensible rate-like description. Demonstrations of spiking neural networks forsensory processing include the work of Srinivasa and Jiang (2013).Their research consists of the development of spiking neuronmodels, initially assembled into an unstructured map topol-ogy. The authors show how the combination of self-organizedand STDP-based continuous learning can provide the initial for-mation and on-going maintenance of orientation and oculardominance maps of the kind commonly found in the visualcortex.It is clear that research on spiking neural networks hasexpanded beyond computational models of individual neuronsand now encompasses large-scale networks which aim to modelthe behavior of whole neural regions. This has resulted in adiverse and exciting field of research with many perspectives anda multitude of potential applications.REFERENCESAimone, J. B., and Weick, J. P. (2013). Perspectives for computational modeling ofcell replacement for neurological disorders. Front. Comput. Neurosci. 7:150. doi:10.3389/fncom.2013.00150Börgers, C., and Walker, B. (2013). Toggling between gamma-frequency activityand suppression of cell assemblies. Front. Comput. Neurosci. 7:33. doi: 10.3389/fncom.2013.00033Buice, M. A., and Chow, C. C. (2013). Generalized activity equations for spikingneural network dynamics. Front. Comput. Neurosci. 7:162. doi: 10.3389/fncom.2013.00162Dockendorf, K., and Srinivasa, N. (2013). Learning and prospective recall of noisyspike pattern episodes. Front. Comput. Neurosci. 7:80. doi: 10.3389/fncom.2013.00080Garrido, J. A., Ros, E., and D’Angelo, E. (2013). Spike timing regulation on the mil-lisecond scale by distributed synaptic plasticity at the cerebellum input stage: asimulation study. Front. Comput. Neurosci. 7:64. doi: 10.3389/fncom.2013.00064Kaplan, B. A., Lansner, A., Masson, G. S., and Perrinet, L. U. (2013). Anisotropicconnectivity implements motion-based prediction in a spiking neural network.Front. Comput. Neurosci. 7:112. doi: 10.3389/fncom.2013.00112Moujahid, A., and d’Anjou, A. (2012). Metabolic efficiency with fast spiking in thesquid axon. Front. Comput. Neurosci. 6:95. doi: 10.3389/fncom.2012.00095Ponulak, F. J., and Hopfield, J. J. (2013). Rapid, parallel path planning by propa-gating wavefronts of spiking neural activity. Front. Comput. Neurosci. 7:98. doi:10.3389/fncom.2013.00098Srinivasa, N., and Jiang, Q., (2013). Stable learning of functional maps in self-organizing spiking neural networks with continuous synaptic plasticity. Front.Comput. Neurosci. 7:10. doi: 10.3389/fncom.2013.00010Received: 14 November 2013; accepted: 02 December 2013; published online: 19December 2013.Citation: Wall J and Glackin C (2013) Spiking neural network connectivity andits potential for temporal sensory processing and variable binding. Front. Comput.Neurosci. 7:182. doi: 10.3389/fncom.2013.00182This article was submitted to the journal Frontiers in Computational Neuroscience.Copyright © 2013 Wall and Glackin. This is an open-access article distributed underthe terms of the Creative Commons Attribution License (CC BY). The use, distribu-tion or reproduction in other forums is permitted, provided the original author(s)or licensor are credited and that the original publication in this journal is cited, inaccordance with accepted academic practice. No use, distribution or reproduction ispermitted which does not comply with these terms.Frontiers in Computational Neuroscience www.frontiersin.org December 2013 | Volume 7 | Article 182 | 2

Metabolic efficiency with fast spiking in the squid axon.

Perspectives for computational modeling of cell replacement for neurological disorders.

Spike timing regulation on the millisecond scale by distributed synaptic plasticity at the cerebellum input stage: a simulation study.

Spiking neural network connectivity and its potential for temporal sensory processing and variable binding.

Spiking neural network connectivity and its potential for temporal sensory processing and variable binding

The most biologically-inspired artificial neurons are those of the third generation, and are termed spiking neurons, as individual pulses or spikes are the means by which stimuli are communicated. In essence, a spike is a short-term change in electrical potential and is the basis of communication between biological neurons. Unlike previous generations of artificial neurons, spiking neurons operate in the temporal domain, and exploit time as a resource in their computation. In 1952, Alan Lloyd Hodgkin and Andrew Huxley produced the first model of a spiking neuron; their model describes the complex electro-chemical process that enables spikes to propagate through, and hence be communicated by, spiking neurons. Since this time, improvements in experimental procedures in neurobiology, particularly with in vivo experiments, have provided an increasingly more complex understanding of biological neurons. For example, it is now well understood that the propagation of spikes between neurons requires neurotransmitter, which is typically of limited supply. When the supply is exhausted neurons become unresponsive. The morphology of neurons, number of receptor sites, amongst many other factors, means that neurons consume the supply of neurotransmitter at different rates. This in turn produces variations over time in the responsiveness of neurons, yielding various computational capabilities. Such improvements in the understanding of the biological neuron have culminated in a wide range of different neuron models, ranging from the computationally efficient to the biologically realistic. These models enable the modelling of neural circuits found in the brain. In recent years, much of the focus in neuron modelling has moved to the study of the connectivity of spiking neural networks. Spiking neural networks provide a vehicle to understand from a computational perspective, aspects of the brain's neural circuitry. This understanding can then be used to tackle some of the historically intractable issues with artificial neurons, such as scalability and lack of variable binding. Current knowledge of feed-forward, lateral, and recurrent connectivity of spiking neurons, and the interplay between excitatory and inhibitory neurons is beginning to shed light on these issues, by improved understanding of the temporal processing capabilities and synchronous behaviour of biological neurons. This research topic aims to amalgamate current research aimed at tackling these phenomena

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EDITORIAL
published: 19 December 2013
doi: 10.3389/fncom.2013.00182
Spiking neural network connectivity and its potential for
temporal sensory processing and variable binding
Julie Wall1* and Cornelius Glackin2
1 Multimedia and Vision Research Group, School of Electronic Engineering and Computer Science, Queen Mary, University of London, London, UK
2 Adaptive Systems Research Group, Department of Computer Science, University of Hertfordshire, Hatfield, Hertfordshire, UK
*Correspondence: julie.wall@qmul.ac.uk
Edited by:
Misha Tsodyks, Weizmann Institute of Science, Israel
Keywords: cell assembly, spiking neural network, spike timing, biological neurons, learning, connectivity, sensory processing
The most biologically-inspired artificial neurons are those of the
third generation, and are termed spiking neurons, as individual
pulses or spikes are the means by which stimuli are commu-
nicated. In essence, a spike is a short-term change in electrical
potential and is the basis of communication between biological
neurons. Unlike previous generations of artificial neurons, spik-
ing neurons operate in the temporal domain, and exploit time
as a resource in their computation. In 1952, Alan Lloyd Hodgkin
and Andrew Huxley produced the first model of a spiking neu-
ron; their model describes the complex electro-chemical process
that enables spikes to propagate through, and hence be com-
municated by, spiking neurons. Since this time, improvements
in experimental procedures in neurobiology, particularly with
in vivo experiments, have provided an increasingly more com-
plex understanding of biological neurons. For example, it is now
well-understood that the propagation of spikes between neurons
requires neurotransmitter, which is typically of limited supply.
When the supply is exhausted neurons become unresponsive. The
morphology of neurons, number of receptor sites, amongst many
other factors, means that neurons consume the supply of neu-
rotransmitter at different rates. This in turn produces variations
over time in the responsiveness of neurons, yielding various com-
putational capabilities. Such improvements in the understanding
of the biological neuron have culminated in a wide range of dif-
ferent neuron models, ranging from the computationally efficient
to the biologically realistic. These models enable the modeling of
neural circuits found in the brain.
In recent years, much of the focus in neuron modeling has
moved to the study of the connectivity of spiking neural net-
works. Spiking neural networks provide a vehicle to understand
from a computational perspective, aspects of the brain’s neural
circuitry. This understanding can then be used to tackle some
of the historically intractable issues with artificial neurons, such
as scalability and lack of variable binding. Current knowledge of
feed-forward, lateral, and recurrent connectivity of spiking neu-
rons, and the interplay between excitatory and inhibitory neurons
is beginning to shed light on these issues, by improved under-
standing of the temporal processing capabilities and synchronous
behavior of biological neurons. This research topic spans current
research on neuron models to spiking neural networks and their
application to interesting and current computational problems.
The research papers submitted to this topic can be categorized
into the following major areas of more efficient neuron model-
ing; lateral and recurrent spiking neural network connectivity;
exploitation of biological neural circuitry by means of spiking
neural networks; optimization of spiking neural networks; and
spiking neural networks for sensory processing.
Moujahid and d’Anjou (2012) stimulate the giant squid
axon with simulated spikes to develop some new insights into
the development of more relevant models of biological neu-
rons. They observed that temperature mediates the efficiency of
action potentials by reducing the overlap between sodium and
potassium currents in the ion exchange and subsequent energy
consumption. The original research article by Dockendorf and
Srinivasa (2013) falls into the area of lateral and recurrent spik-
ing neural network connectivity. It presents a recurrent spiking
model capable of learning episodes featuring missing and noisy
data. The presented topology provides a means of recalling previ-
ously encoded patterns where inhibition is of the high frequency
variety aiming to promote stability of the network. Kaplan et al.
(2013) also investigated the use of recurrent spiking connectiv-
ity in their work on motion-based prediction and the issue of
missing data. Here they address how anisotropic connectivity pat-
terns that consider the tuning properties of neurons efficiently
predict the trajectory of a disappearing moving stimulus. They
demonstrate and test this by simulating the network response in
a moving-dot blanking experiment.
Garrido et al. (2013) investigate how systematic modifications
of synaptic weights can exert close control over the timing of spike
transmissions. They demonstrate this using a network of leaky
integrate-and-fire spiking neurons to simulate cells of the cere-
bellar granular layer. Börgers and Walker (2013) investigate sim-
ulations of excitatory pyramidal cells and inhibitory interneurons
which interact and exhibit gamma rhythms in the hippocam-
pus and neocortex. They focus on how inhibitory interneurons
maintain synchrony using gap junctions. Similarly, Ponulak and
Hopfield (2013) also take inspiration from the neural structure
of the hippocampus to hypothesize about the problem of spa-
tial navigation. Their topology encodes the spatial environment
through an exploratory phase which utilizes “place” cells to reflect
all possible trajectory boundaries and environmental constraints.
Subsequently, a wave propagation process maps the trajectory
between the target or multiple targets and the current location by
altering the synaptic connectivity of the aforementioned “place”
cells in a single pass. A novel viewpoint of the state-of-the-art for
the exploitation of biological neural circuitry by means of spik-
ing neural networks is provided by Aimone and Weick (2013). In
their paper, a thorough and comprehensive review of modeling
Frontiers in Computational Neuroscience www.frontiersin.org December 2013 | Volume 7 | Article 182 | 1
COMPUTATIONAL NEUROSCIENCE
Wall and Glackin Spiking neural connectivity, variable binding
cortical damage due to stroke is provided. They argue that a the-
oretical understanding of the damaged cortical area post-disease
is vital while taking into account current thinking of models for
adult neurogenesis.
One of the issues with modeling large-scale spiking neural net-
works is the lack of tools to analyse such a large parameter space,
as Buice and Chow (2013) discuss in their hypothesis and theory
article. They propose a possible approach which combines mean
field theory with information about spiking correlations; thus
reducing the complexity to that of a more comprehensible rate-
like description. Demonstrations of spiking neural networks for
sensory processing include the work of Srinivasa and Jiang (2013).
Their research consists of the development of spiking neuron
models, initially assembled into an unstructured map topol-
ogy. The authors show how the combination of self-organized
and STDP-based continuous learning can provide the initial for-
mation and on-going maintenance of orientation and ocular
dominance maps of the kind commonly found in the visual
cortex.
It is clear that research on spiking neural networks has
expanded beyond computational models of individual neurons
and now encompasses large-scale networks which aim to model
the behavior of whole neural regions. This has resulted in a
diverse and exciting field of research with many perspectives and
a multitude of potential applications.
REFERENCES
Aimone, J. B., and Weick, J. P. (2013). Perspectives for computational modeling of
cell replacement for neurological disorders. Front. Comput. Neurosci. 7:150. doi:
10.3389/fncom.2013.00150
Börgers, C., and Walker, B. (2013). Toggling between gamma-frequency activity
and suppression of cell assemblies. Front. Comput. Neurosci. 7:33. doi: 10.3389/
fncom.2013.00033
Buice, M. A., and Chow, C. C. (2013). Generalized activity equations for spiking
neural network dynamics. Front. Comput. Neurosci. 7:162. doi: 10.3389/fncom.
2013.00162
Dockendorf, K., and Srinivasa, N. (2013). Learning and prospective recall of noisy
spike pattern episodes. Front. Comput. Neurosci. 7:80. doi: 10.3389/fncom.2013.
00080
Garrido, J. A., Ros, E., and D’Angelo, E. (2013). Spike timing regulation on the mil-
lisecond scale by distributed synaptic plasticity at the cerebellum input stage: a
simulation study. Front. Comput. Neurosci. 7:64. doi: 10.3389/fncom.2013.00064
Kaplan, B. A., Lansner, A., Masson, G. S., and Perrinet, L. U. (2013). Anisotropic
connectivity implements motion-based prediction in a spiking neural network.
Front. Comput. Neurosci. 7:112. doi: 10.3389/fncom.2013.00112
Moujahid, A., and d’Anjou, A. (2012). Metabolic efficiency with fast spiking in the
squid axon. Front. Comput. Neurosci. 6:95. doi: 10.3389/fncom.2012.00095
Ponulak, F. J., and Hopfield, J. J. (2013). Rapid, parallel path planning by propa-
gating wavefronts of spiking neural activity. Front. Comput. Neurosci. 7:98. doi:
10.3389/fncom.2013.00098
Srinivasa, N., and Jiang, Q., (2013). Stable learning of functional maps in self-
organizing spiking neural networks with continuous synaptic plasticity. Front.
Comput. Neurosci. 7:10. doi: 10.3389/fncom.2013.00010
Received: 14 November 2013; accepted: 02 December 2013; published online: 19
December 2013.
Citation: Wall J and Glackin C (2013) Spiking neural network connectivity and
its potential for temporal sensory processing and variable binding. Front. Comput.
Neurosci. 7:182. doi: 10.3389/fncom.2013.00182
This article was submitted to the journal Frontiers in Computational Neuroscience.
Copyright © 2013 Wall and Glackin. This is an open-access article distributed under
the terms of the Creative Commons Attribution License (CC BY). The use, distribu-
tion or reproduction in other forums is permitted, provided the original author(s)
or licensor are credited and that the original publication in this journal is cited, in
accordance with accepted academic practice. No use, distribution or reproduction is
permitted which does not comply with these terms.
Frontiers in Computational Neuroscience www.frontiersin.org December 2013 | Volume 7 | Article 182 | 2


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