Detection of Internal and External Events in Spiking Neural

Networks

Sergey Lobov

, Victor Kazantsev

and Valeri A. Makarov

1,2

Lobachevsky State University of Nizhny Novgorod, Gagarin Ave. 23, 603950 Nizhny Novgorod, Russia

Instituto de Matemática Interdisciplinar, Applied Mathematics Dept., Universidad Complutense de Madrid,

Avda Complutense s/n, 28040 Madrid, Spain

1 OBJECTIVES

Neurons as a main building block of the brain have

enormous computational capacity. Therefore, the

development of mathematical models of spiking

neurons and neural networks on their basis is a

promising approach for applied computations

(Paugam-Moisy and Bohte, 2009). However, the

number of successful attempts of technical

implementations remains very limited. Recent

studies have shown that networks of spiking neurons

can be used for recognition of patterns of different

origin (Bichler et al., 2011; Loiselle et al., 2005;

Kasabov et al., 2012).

In this work we report two successful studies of

spiking neural networks. In the first case we use a

toy robot, a crocodile, driven by a neural network in-

silico. We show that this so-called neuroanimat is

capable of detecting internal events of

synchronization of network responses to stimuli. In

the second example we employ a spiking neural

network for building a human-robot interface. Using

a bracelet with eight electromyographic sensors we

classify hand gestures in real time and use them to

control a mobile robot.

2 METHODS

2.1 Neuroanimat

We developed a neuro-simulator, called NeuroNet,

which models a network of 400 excitatory and 100

inhibitory Izhikevich-type neurons (Izhikevich,

2004). Topologically the neurons are distributed

over nodes in a 2D graph whose edges correspond to

couplings between cells. Then, the time delay in

spike transmission between neurons is proportional

to the distance between the corresponding nodes.

Each neuron receives about 30 afferent couplings.

The coupling probability decreased with the distance

between neurons.

The model simulates two types of synaptic

plasticity. The short-term plasticity (facilitation and

depression) is implemented by varying the

transmitter release according to the frequency of

presynaptic spikes (Tsodyks et al., 1998). The long-

term potentiation is based on spike-timing dependent

plasticity (STDP) (Morrison et al., 2008). If a

postsynaptic spike follows a presynaptic spike then

the coupling strength increases. In the case of

inverse spike timings the coupling strength reduces.

An ultrasonic distance sensor placed on the robot

head provides sensory information to the neural

network. The sensor modulates the frequency of

square pulses produced by a virtual generator. The

output of this generator is fed to an arbitrary part of

the network. Finally, the network output controls the

robot movements.

2.2 Human-robot Interface

We developed a hardware-software complex, called

MyoClass, for real time recording of EMG signals

and recognition of hand gestures for controlling a

mobile robot. The recording is accomplished by a

bracelet MYO™ Thalmic providing simultaneously

eight sEMG signals from the sensors (embedded

MYO Thalmic gesture recognition was off). We

used nine static hand gestures as motor patterns.

During an experiment users performed four series of

nine gestures each, selected in random order.

For extraction of the discriminating features

from sEMG signals we employed the same neuronal

model as in the neuroanimat approach. The network

output was connected to a multilayer artificial neural

network for the feature classification. The standard

error backpropagation algorithm was used for

learning.

2.3 Robot Platforms

Both robot platforms for the animat (a crocodile)

Lobov, S., Kazantsev, V. and Makarov, V..

Detection of Internal and External Events in Spiking Neural Networks.

and for testing the human-robot interface (a car)

were built from a LEGO kit NXT Mindstorms ®.

Communication between all parts has been

implemented through a Bluetooth® interface.

3 RESULTS

3.1 Neuroanimat: Basic Behaviours

We first checked that the neural network in-silico

could exhibit all basic properties of an in-vitro

neuronal culture such as bursting activity (Wagenaar

et al., 2006) and plasticity provoked by external

stimuli (Pimashkin et al., 2013). Adaptive structural

changes in the network are related to long-term

potentiation of the coupling weights. We found that

such changes can lead to new emerging functional

properties, i.e. to synchronization of the network

firing with external stimuli.

We revealed two criteria working at the low

neuron level that allow distinguishing between

synchronous and asynchronous network activities:

a. High frequency (> 8-11 Hz) spiking of neurons;

b. Stable phase lag (about 60-70 ms) of fired spikes

related to the stimulus onset.

To combine both criteria we proposed a neural

circuit that includes phase and frequency neuronal

filters coupled in series. Then, the filter output

passes through a neuron-detector, which fires spikes

in case of synchronization of the network activity

with the stimulus.

The phase filter employs axonal delays in two

inhibitory neurons included between the stimulated

part of the network and the neuron-detector. The

first neuron receives input through the geometrically

shortest path and thus suppresses excitatory spikes in

the time range [20-60] ms after the stimulus onset.

The second neuron placed at a distance from the

detector suppresses the excitation in the range [70-

120] ms. Thus, these neurons strongly inhibit all

spikes at the detector except those falling into the

range [60-70] ms.

The frequency filter relies on the effect of

presynaptic facilitation in the framework of short-

term synaptic plasticity. The filter parameters have

been tuned in such a way that the amount of

neurotransmitter release increased for series of

presynaptic spikes coming at rates higher than 8 Hz.

Thus, the output spikes are generated for high

frequency activity of the presynaptic neuron only.

Spontaneous activity in the neural network

eventually leads to an arbitrary movement of the

robot. Then, in case of the presence of an object in

the sensory field of the robot, its sensory system

generates an output that innervates the neural

network. This in turn may lead to a strong increase

of the motor activity. The combination of

spontaneous and evoked activities in the neural

network may lead to the behaviour of searching for a

target. Even in the absence of any object in the

immediate neighbourhood, the animat from time to

time begins moving and “looking” for objects or

walls in the room.

In case of event synchronization we observed

“eating” behaviour (Fig. 1). At high frequency

synchronization neuronal spikes pass the phase and

frequency filters, which leads to activation of moto-

neurons driving quick opening and closing of the

jaws.

Figure 1: “Eating” behaviour based on synchronization

phenomenon in the animat. Left and right columns

correspond to before and after learning, respectively. The

output from the ultrasonic sensor (s) provokes

synchronization (red circled) of neurons in the main

network (n is a representative neuron). This in turn leads

to activation of the phase filter neuron (d1) and later of the

frequency filter neuron (d2) and the animat opens the

jaws.

3.2 Spiking Neurons in Human-robot

Interface

The myographic bracelet provides simultaneously

eight sEMG signals. Then, the purpose of the neural

network is to extract the most discriminative features

from these signals in such a way that the artificial

neural network could easily classify them according

to the gestures made by hand.

Spiking neurons, acting as sensory neurons,

receive myographic signals from the bracelet and

produce some output spikes. We consider the output

synaptic signal evaluated in the framework of the

Tsodycs-Markram model as continuously changing

feature. Then, we can sample this variable at discrete

time instants.

Figure 2 shows a representative example of an

sEMG signal (top), the transmembrane potential of

the spiking sensory neuron (middle) and its output

(bottom). During experiments we tuned the

parameters of spiking neurons to ensure high

accuracy of the classifier, comparable with the use

of classic sEMG feature as the root mean square

value. For ten subjects (25-56 years old) the

classifier accuracy was 92.3±4.2%.

Figure 2: A representative example of processing of an

EMG signal by a spiking sensory neuron.

We then tested the human-robot interface in real

time. The user controlled the mobile robot using

hand gestures. Every recognized gesture (except

“rest”) was associated with an instruction of

movement of the robot: “drive”, “reverse”, “forward

right”, “forward left”, “reverse right”, “reverse left”,

“stop”, and “fire”. Our results show that all users

after 3-10 trials managed to control fluently the

robot.

4 DISCUSSION

In this work we reported two successful cases of

developing neural networks of spiking neurons for

controlling mobile robots. In the first case the neural

network works autonomously as a “brain” of an

animat. We have shown that it is able to learn from

the environment and to reproduce basic behaviour of

advancing towards an object and “eating”. In the

second case the neural network has been used as a

processor for human-robot interface. We have

shown that the interface can faithfully detect

myographic signals, classify them according to hand

gestures, and send the corresponding commands to

the robot.

Although the two applications belong to

different areas of the Control Theory and applied

Neuroscience, they are based on a common

approach of neural computations. We note that in

both cases besides neural networks there are no

additional external algorithms for the decision-

making.

ACKNOWLEDGEMENTS

This work was supported by the Russian Science

Foundation project 15-12-10018 (Sections 1, 2.1, 3.1

and 4) and project 14-19-01381 (Sections 2.2, 2.3,

and 3.2).

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