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Spiking Neural Network Connectivity and its Potential for Temporal Sensory Processing and Variable Binding

DOI:10.3389/fncom.2013.00182
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Julie Wall at University of East London
Julie Wall
Cornelius Glackin at Intelligent Voice Ltd
Cornelius Glackin
  • Intelligent Voice Ltd
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Abstract

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.
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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
1Multimedia and Vision Research Group, School of Electronic Engineering and Computer Science, Queen Mary, University of London, London, UK
2Adaptive 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 NEUROSCIENC
E
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.
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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.
Neuro sci . 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
... A Liquid State Machines (LSM) is a computational model which consists essentially of recurrent and random spiking neural network and multiple read-out neurons (Zhang et al., 2015). Spiking Neural Network (SNN) according to Wall and Glackin (2013) is a third generation artificial neuron that is most biologically-inspired. They are preferred above the previous generations of artificial neurons because the spiking neurons operate in temporal domain and their computation is based on time resource. ...
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Liquid State Machine (LSM) is a neural model with real time computations which transforms the time varying inputs stream to a higher dimensional space. The concept of LSM is a novel field of research in biological inspired computation with most research effort on training the model as well as finding the optimum learning method. In this review, the performance of LSM model was investigated using two learning method, online learning and offline (batch) learning methods. The review revealed that optimal performance of LSM was recorded through online method as computational space and other complexities associated with batch learning is eliminated.
... Spiking neural networks (SNNs) (Maass, 1996(Maass, , 1997Kistler and Gerstner, 2002) are sometimes referred to as the "third generation" of neural networks because of their potential to supersede deep learning methods in the fields of computational neuroscience (Wall and Glackin, 2013) and biologically plausible machine learning (ML) . SNNs are also thought to be more practical for data-processing tasks in which the data has a temporal component since the neurons which comprise SNNs naturally integrate their inputs over time. ...
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