ParkYuntae1,†
SunaryaUnang1,2,†
YangGeunbo1
LeeChoongseop1
BaekJaewoo1
BaekSuwhan1
ParkCheolsoo1*
-
(Department of Computer Engineering, KwangWoon University, Korea {parkjow15, unangsunarya2040,
rmsqhwkd2, cndtjq97, jw03070, lunit, parkcheolsoo}@kw.ac.kr
)
-
( School of Applied Science, Telkom University, Bandung, Indonesia unangsunarya@telkomuniversity.ac.id)
Copyright © The Institute of Electronics and Information Engineers(IEIE)
Keywords
Spiking neural network, Machine learning, Neuromorphic, Artificial intelligence
1. Introduction
The spiking neural network (SNN) is a third-generation artificial neural network (ANN)
[1] in which every neuron gets trigger signals from other neuron fire spikes at a certain
threshold. Unlike other neural networks, such as the ANN, that use more than one-bit
communication between cells, the SNN uses spikes with a precision of one bit [2]. In an SNN, it is not the signal form that matters but the timing and number of spikes.
The patterns of the signals generated from each neuron will be in the same form. However,
every neuron can generate different numbers of spikes at different times, which is
the key feature of the SNN. There are a lot of issues when implementing conventional
neural network algorithms in hardware devices due to energy consumption, especially
with a large network. The SNN can consume less energy, compared to conventional networks
[3].
Spiking neural networks can be used in a variety of ways. Harvest yield estimation,
for example, attempts to forecast a wheat crop yield [1]. They are also used for pattern digit recognition [2,4] and EEG signal classification [5,6]. In addition, SNN researchers try to keep the network biologically plausible [2]; that is, they make all or most of the neuron parameters the same as the actual neuronal
biology observations, which can reduce accuracy [7,8]. Others have tried to implement an SNN using rate-based learning in an ANN. In this
approach, they use backpropagation training, followed by conversion to a spiking neural
network [9-11]. The basic principle of the SNN is to transform input signals (temporal information)
into spike trains representing event-sensitive time [1]. In this paper, several SNN algorithms with variations in the model are explained
in Section 2. There are several encoding methods, and the encoding utilized by the
SNN is discussed in Section 3. Section 4 addresses learning methods for SNNs, and
Section 5 concludes this paper.
2. The Spiking Neural Network
The SNN is a model based on biology, mimicking the behavior and activities of neurons
[2]. In an SNN, spike trains encode temporal input information. The neurons' firing rates
change in response to different input strengths. Spike trains from one neuron flow
and enter other neurons.
There are three main parts of the neuronal system: dendrite, axon, and soma. Dendrites
receive spike trains from an input neuron through a synapse. Then, the accumulated
spikes form potential membranes in neurons. The potential membrane could increase
and decrease exponentially based on the number of spike trains in a certain time frame.
If the accumulated potential in a membrane crosses a certain threshold, neurons fire
and generate a spike. The threshold can be defined by each model. The more often a
neuron receives spike trains, the faster the neuron will fire spikes. The generated
spikes are transmitted to other neurons via the axon, on which the end is called the
synapse. Fig. 1 illustrates the mechanism in the neuronal system [5,12].
Fig. 1. The neuronal system.
2.1 Classes of Spiking Neural Networks
There are various classes of SNN, first of which is the evolving spiking neural network
(eSNN) including a neural network’s ability to learn, its close reasoning, and linguistically
meaningful features [13-15]. Second is the dynamic evolving spiking neural network (deSNN) utilizing a dynamic
synapse and a learning process of rank-order to train spike-driven synaptic plasticity
in a fast, online mode [16-18]. Lastly, the NeuCube SNN learns from spectro temporal brain data, which makes connections
between clusters of neurons to build a track of neuronal activity [1,19]. The details of the above-mentioned algorithms are described below.
▪ Evolving Spiking Neural Network
An eSNN integrates neural network adaptive learning with fuzzy-rule-based logical
reasoning. To discover new shapes from incoming data, the eSNN applies strategy and
a one-pass rank order to develop a new spiking neuron and neural connections [16]. This architecture is a class of an Evolving Connections System (ECS) and an SNN,
which creates spiking neurons and then merges incrementally to find groups and similarities
from incoming data. The eSNN is claimed to have an adaptive system and a fast training
process [13]. One implementation of the eSNN is utilized to control animals for a certain task
requiring pattern recognition [20]. The paper proposed a mechanism to find target sources of specific signals (temporal
patterns of sound) in the presence of distractors.
▪ Dynamic Evolving Spiking Neural Network
The deSNN is an improved eSNN model that utilizes temporal spike coding and rank order
spike coding [18]. A deSNN also utilizes dynamic synapses and rank order learning from spatio/spectro
temporal data (SSTD) in a fast, online mode. The deSNN models have better performance
in terms of speed and accuracy, compared to other SNN models that employ STDP or rank-order
learning [16].
▪ NeuCube
The NeuCube is an SNN model that was made to handle streams of spatiotemporal data
at first. This model is widely used to process spatiotemporal data for remote sensing
applications to estimate crop yields and early predictions of events [1]. A NeuCube is also used for understanding, mapping, and learning spatiotemporal brain
data (STBD) [19]. Fig. 2 illustrates the NeuCube structure [19]. General principles of the NeuCube architecture consist of four functional modules
[19]:
Fig. 2. The NeuCube architecture for spatiotemporal brain data.
1. input data encoding
2. 3D SNN reservoir (SNNr)
3. classification output function
4. an optional Gene Regulatory Network (GRN)
Several softwires implement a NeuCube for an SNN in Python with PyNN, Matlab, C++,
and Java. SpiNNaker, based in the cloud, is a computer platform to support the implementation
of NeuCube models [21].
2.2 Neuron Model
There are a variety of neuron models: Izhikevich, Hodgkin-Huxley, the Leaky Integrate
and Fire method, etc. [22]. This paper illustrates various spiking neuron models.
▪ Izhikevich Neuron Model
Izhikevich's neuron model can handle nonlinear and linear pattern recognition problems
[24]:
where a is a recovery time constant, b can be an integrator if b < 0 or a resonator
if b > 0, k is a constant value that would be gained after input resistance is known,
while c and d sum different currents activated during spike firing. Both a and b consider
the action of currents with high thresholds that are engaged during firing. The membrane
potential is represented by v, the recovery value by u, the membrane capacitor by
C, and the resting membrane potential by v$_{r}$; $v_{t}$ is a simultaneous potential-threshold,
$\dot{v}$ is the first voltage derivate over time, and $v_{peak}$ is the spike cutoff
value.
▪ Hodgkin-Huxley Neuron Model
In the Hodgkin-Huxley neuron model, the current flowing through membrane resistance
is determined by three ion channels: the sodium channel with conductance $g_{Na},$
a potassium channel with conductance $g_{k}$, and a leak channel with conductance
$g_{CL}~ $[25]:
where $V_{i}$ is the membrane potential of the i-th neuron, and dt is time in ms.
Parameters m, h, and n are gating variables for sodium activation, sodium inactivation,
and potassium activation, respectively.
▪ Leaky Integrate and Fire Model
Another model that explains the interaction between neurons is the Leaky Integrate
and Fire (LIF) neuron model. The relationship models the neuron connections using
electric ingredients such as circuit elements, voltage source, and current source:
where $\tau _{m}$ is a membrane time constant, $u\left(t\right)$ is the membrane potential
at time t, R is membrane resistance, and $I\left(t\right)$ is input current at time
t. This model encodes input value intensity into spikes [2,26]. Fig. 3 shows the structure of the LIF model [9].
Fig. 3. Structure of the Leaky Integrate and Fire model.
3. Encoding
Neurons are cells in the body that generate signals and rapidly propagate them. Neurons
accomplish this by producing characteristic electrical pulses known as action potentials.
A sensory neuron changes its activity by firing a series of action potentials according
to a variety of time patterns in response to an external stimulus.~As the action potential
patterns are transmitted throughout the brain, information about stimuli is encoded
[27]. Information is converted to a different form or format for standardization, security,
processing speed improvement, and storage space savings [27]. The most common encoding method is the rate-temporal coding method. The two approaches
can be distinguished depending on how important the precise timing and order of the
information conveyed are.
3.1 Rate Coding
In 1926, Adrian and Zotterman proposed rate coding [29], also known as frequency coding since rate refers to the average over time [28]. This is the standard coding scheme, which assumes that the neurons contain the majority
of the information about the stimuli [30]. The kind of stimulus that generates action potentials is different every time, so
our neural response system analyzes them statistically or probabilistically [30]. Thus, it is possible to characterize the firing rate, rather than a specific spike
sequence [31]. The rate coding technique became the standard method for characterization of sensory
or cortical neurons in the following decades owing to the relatively easy measurement
of rates experimentally [32]. However, this approach can ignore information that might be included in the precise
timing of the spike. Increasingly, experimental evidence suggests that the direct
rate of fire based on time averages may not be a sufficient representation of brain
activity [32].
3.2 Temporal Coding
Neural code is described as temporal code because it transmits information based on
precise spike timing. Firing rates of neurons fluctuate at high frequencies, and they
can transmit information or be noise [31]. As an alternative explanation for noise, temporal coding suggests that information
is encoded and transmitted through it. It also influences neural activity. In some
cases, temporal codes are referred to as spike codes. Essential information can be
lost because it is not possible to capture all information about spike trains from
rate code. Additionally, in response to similar stimuli, there are enough differences
in responses, suggesting that distinct patterns of spikes contain more information
than can be encoded by rate code [34].
4. Types of Learning
Learning is a process where a network builds a model by training it from input data.
After the training process, the network will have a model to be used to resolve or
recognize new data. In general, types of learning are classified as seen in Fig. 4 [35,36].
Fig. 4. Types of Learning.
▪ Supervised Learning
In supervised learning, the algorithm can use label information to apply to the new
test data what it has learned from the training data. Fig. 5 shows a supervised neural network process [37].
The output of this learning process is a model that can be applied to new test data,
and the model can estimate the labels of test data using the weights of networks that
were calculated during the training process. A general SVM pseudocode format for supervised
learning is seen below.
One of the implementations of supervised learning in spiking neural networks was presented
in [37]. That study implemented supervised learning in an SNN to test complex nonlinear classification
problems for an iris dataset. It demonstrated that the SNN could perform nonlinear,
separable classification tasks with 97.33\% accuracy after 1200 iterations. A supervised
learning rule for classification of spatiotemporal spike patterns is another supervised
learning SNN solution [7]. This implementation takes into account axonal and synaptic delays caused by weights
and STDP, as in a remote supervised method (e.g., ReSuMe). It was compared with other
methods using the tempotron learning rule and the spike pattern association neuron
(SPAN). The proposed method outperformed others with the highest result at 100\% accuracy
from training and testing.
Fig. 5. A supervised neural network.
▪ Unsupervised Learning
An unsupervised learning algorithm updates the weights of networks without involving
label information during the training process [37]. Fig. 6 diagrams an unsupervised neural network [37].
Although learning is conducted without ground truth, this algorithm explores the input
data and draws inferences from datasets to describe the hidden structure of the network
[28]. General pseudocode for the K-means format of unsupervised learning is seen below.
Several studies implementing the SNN in an unsupervised way have been suggested. In
[8], the authors implemented an unsupervised clustering SNN to classify an iris dataset
from efficient utilization of neurons. That study demonstrated how Hebbian learning
induces and exploits synchronous neurons with enhanced unsupervised clustering capabilities.
It built an SNN architecture for efficient use of neurons, but still yielded reliable
clustering of high-dimensional multi-modal data. Another unsupervised SNN study proposed
an approach comprising hybridization of self-organized maps (SOM) [21]. This method combined STDP rules with a SOM algorithm to train an MNIST dataset of
handwritten digits [41,42]. Fig. 7 is the architecture of an unsupervised spiking neural network [41,42].
In this network, weights are trained on the connection between input and excitatory
layers using the STDP learning rule (see Fig. 7). An SNN using unsupervised learning was implemented in [2] where biologically plausible mechanisms for learning new data were designed. This
SNN model was tested using an MNIST dataset and achieved 95\% accuracy.
Fig. 6. An unsupervised neural network.
Fig. 7. An unsupervised spiking neural network architecture.
▪ Semi-supervised Learning
The semi-supervised learning algorithm has properties of both supervised learning
and unsupervised learning using a small amount of labeled data and a large amount
of unlabeled data during the training process. Varone et al. demonstrated that this
model outperformed other conventional SNN models [28]. Fig. 8 is a diagram of a semi-supervised neural network [37].
The semi-supervised learning approach was also applied in the pre-training phase [43] where STDP was utilized to perform the unsupervised pre-training process to train
the weights of the network. Then, labeled data were trained for a classification problem.
General pseudocode for the self-training format of semi-supervised learning is below.
Fig. 8. A semi-supervised neural network.
▪ Reinforcement Learning
The reinforcement learning algorithm interacts with the environment by producing actions
and receiving rewards/errors from the environment [36,37]. Fig. 9 shows the reinforcement learning diagram [37].
This algorithm is commonly used when optimal interaction is required, and the form
of the pseudocode is presented below.
A reinforcement learning SNN was designed in [44] in which the model implements the action selection process biologically and updates
the weights of the network during the next iteration of the process. That study presented
a PFC function model based on the Leaky Integrate and Fire neuron model. In this way,
it could perform operand and spatial goal-directed tasks. The model reuses associations
from representations in PFC learned before Hebbian STDP. Another implementation of
a reinforcement learning SNN [45] presented a spiking neuron model that learns how to carry out a motor control task.
Here, reinforcement learning is applied to train the model, and global reward and
punishment signals are controlled using STDP.
Fig. 9. Reinforcement learning diagram.
5. Conclusion
The SNN models reviewed in this paper have their own advantages and disadvantages
depending on the types of input data—the higher the data complexity, the higher the
complexity of the network to solve the problem. Performance, individual plausibility,
simplicity, and computing time are often trade-offs. The Hodgkin-Huxley neuron model
could be more biologically plausible, yielding highly accurate performance. Also,
the Leaky Integrated and Fire neuron model displays less computing time during the
process. In terms of learning methods, clear and simple supervised learning would
be the best choice to model the network. Semi-supervised learning could be an option
for improving accuracy problems between supervised and unsupervised methods. The reinforcement
learning method evaluates the network based on previous action to see if the previous
action works well based on rewards, which is why the trial-and-error process is crucial.
ACKNOWLEDGMENTS
This research was supported by the Ministry of Science and ICT (MSIT), under the
National Program for Excellence in SW (2017-0-00096), supervised by the Institute
of Information & Communications Technology Planning & Evaluation (IITP) and the Ministry
of Science and ICT (MSIT), Korea, under the Information Technology Research Center
(ITRC) support program (IITP-2022-RS-2022-00156225) supervised by the Institute for
Information & Communications Technology Planning & Evaluation (IITP).
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Author
YunTae Park is a senior in the Computer Engineering Department at Kwangwoon University,
Seoul, South Korea.
Unang Sunarya is a PhD student in the Computer Engineering Department at Kwangwoon
University, South Korea. He received a diploma from Bandung State Polytechnic (POLBAN),
and bachelor’s and master’s degrees from Telkom University, Indonesia. His research
interests include machine learning, robotics, and signal processing.
Geunbo Yang received his B.S. from the Department of Computer Engineering at Kwangwoon
University, Seoul, South Korea. He is currently pursuing an M.S. in computer engineering
at the same university. His research interests are signal processing, machine learning
algorithms, and computational neuroscience
Choongseop Lee received his B.S. in computer science and engineering from Kwangwoon
University in Seoul, South Korea. His research interests include machine learning
and computational neuroscience.
Jaewoo Baek received a B.S. in computer engineering from Kwang-woon University,
Seoul, South Korea, where he is currently pursuing a master’s degree in computer engineering.
His research interests include biological signal processing, machine learning, deep
learning, and reinforcement learning.
Suwhan Baek is a junior in computer engineering at Kwangwoon University in Seoul,
South Korea. His research interests include overall medical AI and auto ML. He is
also attracted to reinforcement learning, generative models, and the SNN.
Cheolsoo Park is an assistant professor in the Computer Engineering Department
at Kwangwoon University, Seoul, South Korea. He received a
B.Eng. in electrical engineering from Sogang University, Seoul, and received his
M.Sc. in biomedical engineering from Seoul National University, South Korea. In 2012,
he received his Ph.D. in adaptive nonlinear signal processing from the Imperial College
London, London, U.K., and worked as a postdoctoral researcher in the bioengineering
department at the University California, San Diego, U.S.A. His research interests
are mainly in the areas of machine learning and adaptive and statistical signal processing,
with applications in brain-computer interfaces, computational neuroscience, and wearable
technology.