LEARNING IN BIOLOGICAL NEUROPROCESSORS

USING A CENTER OF AREA METHOD

José M. Ferrández, Victor Lorente

Departamento de Electrónica, Tecnología de Computadores y Proyectos, Univ. Politécnica de Cartagena, Cartagena, Spain

Félix de la Paz, José Manuel Cuadra, José R. Álvarez-Sánchez

Departamento de Inteligencia Artiﬁcial, UNED, Madrid, Spain

Eduardo Fernández

Instituto de Bioingeniería, Univ. Miguel Hernández de Elche, CIBER-BBN, Elche, Spain

Keywords:

Cultured neural network, Induced plasticity, Multielectrode recordings, Robotic control.

Abstract:

Learning in a biological neuroprocessor is analyzed using human neuroblastoma cultures and a center of area

method in order to guide a robot to follow the light or the brightest area in a limited scenario. The main

setup consists in an inverted microscope where a multielectrode array is attached with the biological cultures.

This elements ampliﬁes and send the weak neural signals to a D/A card where analyzing process is achieved,

computing the movement of the robot, that is remotely linked to this computer. The robot also sends the a

picture of the scenario to the computer in order to stimulate the culture with a center of area scheme. In this

paper, it is shown that learning is possible in this culture, and guiding the robot to a desired goal is a feasible

task.

1 INTRODUCTION

Mammalian nervous systems exhibit complex com-

putational functions including sensory functions, mo-

tor function and in humans, abstract thought. In par-

ticular, pattern recognition exhibited in our olfactory,

visual and auditory functions are of particular interest

to the electronic and computing communities. Mean-

while several approaches attempt to mimic/substitute

sensory or neural elements (missing by congenital

state or due to pathological processes) in order to en-

able/restore function by establishing neuro-electronic

interfaces.

Classical computational paradigms consist in se-

rial and supervised processing computations with

high-frequency clocks silicon processors, with mod-

erate power consumption, and ﬁxed circuits structure.

In contrast, the brain uses millions of biological pro-

cessors, with dynamic structure, slow commutations

compared with silicon circuits, low power consump-

tion and unsupervised learning. There have been nu-

merous approaches to creating bioinspired parallel

processing. However, silicon provides a fundamen-

tally different technological platform to that of neu-

robiology. Neurons – the core technology component

has a huge number of interconnections compared to 3

in traditional transistors. This provides considerably

more computational power. Furthermore, this extraor-

dinary connectivity is coupled with natural unsuper-

vised learning based on varying connective efﬁciency.

Our learning experiments were performed in neu-

ral cultures containing 120.000 human neuroblastoma

SY-5Y, under the assumption that this kind of cells

are able to respond electrically to external stimuli and

modulate their neural ﬁring by changing the stimu-

lation parameters. Such cultured neuroblastoma net-

works have shown dynamical conﬁgurations, being

able to grow and adapt functionally in response to

external stimuli over different conﬁguration patterns.

We are especially interested in analyzing if popula-

tions of neuroblastoma cells are able to process and

store information, and if learning can be implemented

336

M. Ferrández J., Lorente V., de la Paz F., Manuel Cuadra J., R. Álvarez-Sánchez J. and Fernández E..

LEARNING IN BIOLOGICAL NEUROPROCESSORS USING A CENTER OF AREA METHOD.

DOI: 10.5220/0003084003360343

In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation (ICNC-2010), pages

336-343

ISBN: 978-989-8425-32-4

 2010 SCITEPRESS (Science and Technology Publications, Lda.)

over this biological structure. The main objective of

this work will be to control a robot using this biolog-

ical neuroprocessor and a simple center of area learn-

ing scheme. The ﬁnal system could be applied for

testing how chemicals affect the behavior of the robot

or to establish the basis for new hybrid optogenetic

neuroprostheses.

2 LEARNING IN HUMAN

NEUROBLASTOMA CULTURES

The physiological function of neural cells is modu-

lated by the underlying mechanisms of adaptation and

reconﬁguration in response to neural activity. Heb-

bian learning describes a basic mechanism for synap-

tic plasticity wherein an increase in synaptic efﬁcacy

arises from the presynaptic cell’s repeated and persis-

tent stimulation of the postsynaptic cell. The theory is

commonly evoked to explain some types of associa-

tive learning in which simultaneous activation of cells

leads to pronounced increases in synaptic strength.

The N-methyl-D-aspartate (NMDA) receptor, a sub-

type of the glutamate receptor, has been implicated as

playing a key role in synaptic plasticity in the CNS

(Bading and Greenberg, 1991), where as dopamine

receptors are involved in the regulation of motor and

cognitive behaviors. For most synaptic ion channels,

activation (opening) requires only the binding of neu-

rotransmitters. However, activation of the NMDA

channel requires two events: binding of glutamate (a

neurotransmitter) and relief of Mg

block. NMDA

channels are located at the postsynaptic membrane.

When the membrane potential is at rest, the NMDA

channels are blocked by the Mg

ions. If the mem-

brane potential is depolarized due to excitation of the

postsynaptic neuron, the outward depolarizing ﬁeld

may repel Mg

out of the channel pore. On the

other hand, binding of glutamate may open the gate

of NMDA channels (the gating mechanisms of most

ion channelsare not known). In the normal physiolog-

ical process, glutamate is released from the presynap-

tic terminal when the presynaptic neuron is excited.

Relief of Mg

block is due to excitation of the post-

synaptic neuron. Therefore, excitation of both presy-

naptic and postsynaptic neurons may open the NMDA

channels, this is closely related with Hebbian learn-

ing.

Another important feature of the NMDA channel

is that it conducts mainly the Ca

ion which may

activate various enzymes for synaptic modiﬁcation,

even nitric oxide has been identiﬁed as a relevant el-

ement in synaptic regulation. The enhancement of

synaptic transmission is called the long-term poten-

Figure 1: Human neuroblastoma cells, showing different

growing stages and neuritic development.

tiation (LTP), which involves two parts: the induction

and the maintenance. The induction refers to the pro-

cess, which opens NMDA channels for the entry of

ions into the postsynaptic neuron. The subse-

quent synaptic modiﬁcation by Ca

ions is referred

to as the maintenance of LTP.

A human neuroblastoma SY5Y cell line, that ex-

press clonal speciﬁc human dopamine receptors, and

also NMDA receptors, will be the biological platform

for studying learning in cultured cells.

Neuroblastoma SH-SY5Y cells are known to be

dopaminergic, acetylcholinergic, glutamatergic and

adenosinergic, so in this line they respond to differ-

ent neurotransmitters. The cells have very different

growth phases, as it can be seen in Figure 1. The

cells both propagate via mitosis and differentiate by

extending neurites to the surrounding area. The di-

viding cells can form clusters of cells which are re-

minders of their cancerous nature, but chemicals can

force the cells to dendrify and differentiate, in some

kind of neuritic growth.

As conclusion, neuroblastoma culture cells show

electrophysiological responses similar to standard

neurons, as potential actions generation sensible to

tetrodotoxin (TTX) and acetylcholyn. They have neu-

rotransmitters synthesis process and are able to neu-

ritic growth in culture medium.

3 EXPERIMENTAL SETUP

The neuro-physiology setup provides a complete so-

lution for stimulation, heating, recording, and data

acquisition from 64 channels. The MEA (microelec-

trode array) system, (Rolston et al., 2009; Hales et al.,

2010), is intended for extracellular electrophysiolog-

ical recordings in vitro of different applications that

include acute brain, heart, and retina slices; cultured

slices; and dissociated neuronal cell cultures, see Fig-

ure 3.

The basic components of the proposed system are

LEARNING IN BIOLOGICAL NEUROPROCESSORS USING A CENTER OF AREA METHOD

337

Cell Culture

Computer

Robot

Sensors

Stimulator

Figure 2: Experimental setup, see text for detailed explana-

tion.

Figure 3: Neuroblastoma cells over multielectrode array,

the picture shows a rectangle with sides about 1 mm. long.

shown in Figure 2. These components are:

1. A microelectrode array is an arrangement of 60

electrodes that allows the simultaneous targeting

of several sites for extracellular stimulation and

recording. Cell lines or tissue slices are placed

directly on the MEA, see ﬁgure , and can be cul-

tivated for up to several months. Almost all ex-

citable or spontaneously active cells and tissues

can be used.

2. Raw data from the MEA electrodes are ampliﬁed

by MCS ﬁlter ampliﬁers with custom bandwidth

and gain, which are built very small and compact

using SMD (Surface Mounted Devices) technol-

ogy. The small-sized ampliﬁer combines the in-

terface to the MEA probe with the signal ﬁltering

and the ampliﬁcation of the signal. The compact

design reduces line pick up and keeps the noise

level down. The ampliﬁers are mounted over an

inverted microscopes.

3. The analog input signals are then acquired and

digitized by the MC-Card that is preinstalled on

the data acquisition computer, that supplies the

power for the ampliﬁers, and the pattern stimuli

to the stimulators.

4. The robot sends information about the environ-

ment to the computer using a bluetooth link. The

sensor consists in infrared sensors for detecting

obstacles.

It has been developed a system that provides a com-

plete robotic control platform over remote neural

cultures. The system includes free, open-source,

console-based programs written in C/C++ for real-

time robotic applications with embodied cultures. All

of this software has been developed for the Linux

Operating System and MCS hardware (MultiChan-

nel Systems, Reutlingen, Germany). Using this soft-

ware in conjunction with MEABENCH is specially

intended for close-loop experiments.

The software developed consists of the following

programs:

1. Cult2Robot: The main program. It has been de-

veloped as a MEABENCH module, so it can read

spikes information from MEABENCH spike de-

tector and compute a direction vector based on

MEA neural activity. The direction vector is cal-

culated based on the number of spikes per elec-

trode in t seconds and it can be weighted by the

height and width of the spikes. This direction vec-

tor can be sent to a robot to control its movement.

2. Stg_control: This program controls a general-

purpose two channel stimulus generator.

3. BT_server: Non-blocking Bluetooth server that

uses RFCOMM protocol to receive characters

from a speciﬁc MAC, process the information and

do some action, this protocol is used for remote

controlling the robot.

4. Remote environment: the computer that is com-

municating directly with the robot include TCP-

IP facilities for reading/sending information to the

bio-hybrid platform, wherever it is located.

Bluetooth client and server will be used with a hu-

manoid robot (Robonova, Hitec Robotics) and a two-

wheeled robot (e-puck, www.epuck.org) to send and

receive information about obstacles.

The system comprises:

1. A bio-hybrid robot control: It includes the devel-

opment of a whole bio-hybrid hardware/software

platform with for robotic guidance.

2. A remote communication system with the neural

culture: It has been developed a remote environ-

ment for communicating the robot with any bio-

hybrid control platform through TCP-IP links.

3. Creation of a bio-hybrid robotic control: Design

the neuromorphic processes, e.g. obstacle avoid-

ance tasks, implemented over the bio-hybrid sys-

tem.

4 METHODS

Human neuroblastoma cultures were produced us-

ing the commercial line SH/SY5Y. Neural cells were

ICFC 2010 - International Conference on Fuzzy Computation

338

then plated on Micro-Electrode Arrays -MEAs (Mul-

tiChannel Systems, Reutlingen, Germany). Initially

the nitrogen frozen cells, was immersed in a 37 de-

gree bath, and centrifuged at 1000 rpm during 5 min-

utes. When cells have grown in a uniform mono-

layer process, they are washed three time with buffer

Phosphate-buffered saline (PBS) for keeping the pH

approximately constant. 0,5 per cent trypsin was

added to the solution in order to re-suspend cells ad-

herent to the cell culture dish wall during the process

of harvesting cells. The cells were kept in the in-

cubator for 5 minutes and passed through a 40 µm.

cell strainer (Falcon, Bedford, MA) to remove large

debris. Finally the cells are transferred to a speciﬁc

medium in order to inactivate trypsin, and centrifuged

again during 5 minutes at 1000 rpm.

For seeding the plate cells are stained with try-

pan blue, (because cells that loose their permeabil-

ity get colored with this solution) and counted with a

Neubauer chamber. Finally, 80.000 or 120.000 total

neuroblastoma cells have been placed over the MEA

substrate.

Maintaining cells in culture is essential for study-

ing their physiological properties. Cell culturing is

dependent on the growth surfaces and cells must ad-

here to the electrode substrate in order to establish the

best connection with the electrodes material. For most

cultures coated tissue culture plates are prerequisite

for seeding. The most commonly used coatings are

positively charged polymers. In this work, the insu-

lation layer (silicon nitride) of some of the plates was

pre-treated with polyethyleneimine (PEI), showing no

advantages compared with no covered plates.

The neuroblastoma cultures are maintained in a 37

degree humidiﬁed incubator with 5 per cent CO

and

95 per cent O

with serum-free Neurobasal medium.

Under the aforementioned conditions we were able to

record stable electrophysiological signals over differ-

ent days in vitro (Div). The medium was replaced

one-half of the medium every 5 days.

5 RESULTS

The cultured neuroblastoma cells establish synap-

tic connections. It can be seen differentiated and

non-differentiated neuroblastoma cell bodies grow-

ing around the whole electrode population. The den-

dritic arborescence is more evident in the magniﬁca-

tion Figure 1 b) where differentiated neural cells sur-

round the four electrodes while the rest of the cells are

in their growing process. This Figure corresponds to

80.000 neuroblastoma cells seeded in a no-PEI MEA

at 2nd day in vitro (div).

-20

-40

-20

-40

-20

-40

-20

-40

-20

-40

-20

-40

-20

-40

-20

-40

µV

-1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1

sec

thresholds

-3 std. dev.

spikes

population activity

200

150

100

60000

40000

20000

total spike number [%;spikes]

day 1 day 5 day 16

pre stimulus post stimulus

80000

Figure 4: a) Spontaneous neural activity detected by the

multielectrode array. b) Induced neural activity by tetaniza-

tion stimuli.

The electrophysiological properties of the neu-

roblastoma cultures were analyzed by recording the

spontaneous activity of the network. Time course of

experiments was over 15 days; recordings were done

using two MCS-Meas with two neuroblastoma cell

cultures (but only in one the cells survivedtill day 15).

In vitro neuroblastoma networks show spontaneously

ﬁring. This ﬁring rates change during the culture de-

velopment with marked day differencesand the global

rate is closely related to the age of the network.

The physiological recordings correspond to neu-

roblastoma cultures in the range of 1-7 div. They

show bursting and spiking activity, with usually neg-

ative depolarizations. Figure 4 a) show the spiking

activity of the neural population with an automatic

detection level for each electrode. This is very con-

venient if one has multiple channels for extracting

spikes.

The standard deviation of each data trace is used

to estimate its spike threshold. A time interval of 500

ms is used to calculate the standard deviation. By ﬁx-

ing the factor, by which the standard deviation is mul-

tiplied, the sign of the factor determines whether the

spike detection level is positive or negative, only val-

ues above this will be extracted as spiking activity.

A value between −1 and −4 is appropriate for most

applications the threshold was ﬁxed at standard devi-

ation equal to −3 with respect to the electrode activity

LEARNING IN BIOLOGICAL NEUROPROCESSORS USING A CENTER OF AREA METHOD

339

in order to identify spikes embedded in the noisy sig-

nals.

During the neuroblastoma development, a wide

range of population bursting or synchronized activity

has been observed, according to some studies in neu-

ral cultures preparations (Wagenaar et al., 2006; Rol-

ston et al., 2007; Madhavan et al., 2007; Esposti et al.,

2009). The burst usually contains a large number of

spikes at many channels, with variable duration, from

milliseconds to seconds.

5.1 Tetanic Stimulation

Spontaneous activity was recorded for intervals of 3

minutes before stimulation (PRE-data), and the total

number of spikes extracted was counted. The biphasic

stimulus consists in a 10 trains of a 100 anodic-ﬁrst

waveform with 1 Volt amplitude delivered to all 60

electrodes in order to propagate a tetanization stimu-

lus to the neuroblastoma culture.

In neurobiology, a tetanic stimulation consists of

a high-frequency sequence of individual stimulations

of a neuron. It is associated with long-term poten-

tiation, the objective of this work. High-frequency

stimulation causes an increase in transmitter release

called post-tetanic potentiation (Antonov et al., 2003).

This presynaptic event is caused by calcium inﬂux.

Calcium-protein interactions then produce a change

in vesicle exocytosis. Some studies (Jimbo et al.,

1998) use repetitive stimulation for training neural

cultures, achieving activity potentiation or depres-

sion.

Once the tetanization stimulus was applied to the

whole population 5 minutes after the stimulation a

3 minutes interval was recorded (POST-data). Only

neuronal signals which had at least a 2:1 signal:noise

ration were valued as "spikes". Again, the total num-

ber of spikes extracted was counted. This process was

made for cultures at 1 day in vitro (div), 5 div and 16

div. Figure 4 b) represents the counted spikes with

bar charts for the different recordings. The conclu-

sion from this Figure is:

1. While the neuroblastoma culture is growing new

connections are created, and the number of spikes

increases as the culture expands over the MEA.

2. After a tetanic stimulation the cells continue with

their increased spiking rate, providing a persistent

change in the culture behavior. When this change

in the network response lasts, these changes can

be called learning.

In all the experimentation performed, tetanic stimula-

tion was applied as training method, and the electro-

physiological properties of the neuroblastoma culture

Figure 5: Obstacles avoidance using area center. These pic-

tures form a sequence showing how a robot overcomes an

obstacle using the center of area method. The current ad-

vance sector is the dark blue area, discarded sectors are in

light blue. a) Area center (magenta/dark circle) is accessi-

ble: follow it (using full advance sector). b) Area center

is still accessible: follow it (using full advance sector). c)

Area center became inaccessible (red/dark circle): shrink

and split the advance sector, choose one side and turn to its

area center (yellow/lighter or brown/darker circles), remem-

ber (local coordinates) the split point (marked with arrow).

d) Left side has been chosen: as robot turns the sector is

expanded to the right to cover the split point and increase

restricted advance sector radius as function of sector ampli-

tude increment, follow sector area center (yellow/light cir-

cle). e) Robot is going past the split point: continue expand-

ing the sector, continue following sector area center (using

restricted advance sector). f) Split point has been gone past,

the advance sector has been expanded to its initial radius

and amplitude: forget split point, continue following sector

area center (magenta/dark), the full advance sector has been

recovered. g) Area center is accessible: follow it (using full

advance sector). h) Robot has overcome the obstacle, area

center is accessible: follow it.

change, getting a potentiation effect on the sponta-

neous ﬁring, modulating in this way the culture neural

activity.

5.2 Robotic Control

For controlling the direction of the robot we propose

to compute the winner neurons (that is the ones that

increase more its ﬁring characteristics) resulting from

neural activity recorded in the human neuroblastoma

culture stimulated using a center of area method (Ál-

varez Sánchez et al., 2010; Álvarez Sánchez et al.,

2009).

This method is a new brand of navigation meth-

ICFC 2010 - International Conference on Fuzzy Computation

340

Figure 6: Image acquired by a robot over MEA.

ods, at this time a reactive version has been devel-

oped. The method computes the center of area of a

frontal sector, the advance sector, of the robot free

perceived area and use its position for robot driving.

The robot approximately follows the center of area

path, see Figure 5. The idea is that the center of area

is normally a safe place to go, but when an obstacle

lies near robot path, the center of area could became

inaccessible inside the obstacle, so there is no way

to follow it. In this case the advance sector is split

in two shorter sectors using a point of the obstacle

as a reference. If the centers of area of both sectors

are accessible one of them is chosen, randomly or by

some external preference. If only one center is acces-

sible it is selected. If both centers are inaccessible, no

way, then the method determines the center of area of

the robot shrunk rear area and robot turns to it as an

escape maneuver. Successive splits may be done, if

needed, so robot can drive through complex conﬁgu-

ration of obstacles in a safe way, even passing through

narrow places and following smooth paths.

A modiﬁcation of the method makes it capable of

goal reaching avoiding obstacles, still being, at least

at these preliminary stages of its development, a reac-

tive method.

Note that this center of area concept is a construc-

tion that emerges from the visual or ranging percep-

tion of the robot. In Figure 6, it can be seen a simu-

lation of a robot walking through a cave. This image

will be digitized in three grey levels, black, white and

grey, in order to provide three differentstimulations to

the neural culture, no stimulation, high tetanization,

and medium stimulation respectively.

The resulting stimulation conﬁguration is shown

in Figure 7 a). White boxes correspond to

no-stimulation, red boxes correspond to medium

tetanization, while blue electrodes will deliver high

tetanization according with the acquired cave im-

age. Medium tetanization will consist in ﬁve trains

of a hundred anodic ﬁrst pulses with 1 V amplitude,

while high tetanization will provide 1,5 V anodic ﬁrst

1 2

1 3

1 4

1 5

1 6

1 7

2 2

2 1

2 3

2 4

2 5

2 6

... 100 pulses

high

medium

mV.

1000

500

-500

-1000

1500

-1500

1000

500

-500

-1000

Figure 7: Selective electrode tetanization corresponding to

data acquired from image shown in Figure 6. a) MEA

representation showing three groups of electrodes, white

ones are not stimulated, light blue ones are stimulated with

high tetanization and red (darker) ones are stimulated with

medium tetanization. b) High and medium tetanization

pulses train, anodic ﬁrst waveform.

pulses, Figure 7 b). From this example, it is expected

that electrodes that cover the center of area of the grey

and white image, that is the electrodes centered at the

bottom of the image, electrodes 48 and 58, will in-

crease more their activity. This winner neuron coordi-

nates will be provided to the robot in order to guide its

movement. In the new robot position the camera will

send a new image, and the information will be passed

to the computer in order to induce a selective tetaniza-

tion of the biological neural network for changing the

resulting direction vector.

Again, human neuroblastoma cultures were pro-

duced using the commercial line SH/SY5Y. Cell cul-

ture of SH SY5Y was grown in DMEM (Gibco) com-

pleted with 10% of fetal bovine serum at 37

C in 5%

and humidify atmosphere.

The electrophysiological properties of the neu-

roblastoma cultures were analyzed by recording the

spontaneous activity of the network. In vitro neurob-

lastoma networks showed spontaneously ﬁring. This

ﬁring rates changed during the culture development

with marked day differences and the global rate was

closely related to the age of the network.

Recordings of Neuroblastoma SH-SY5Y has the

disadvantage of having a very low signal to noise ra-

tio. As we have shown in previous papers, the elec-

trophysiological properties of the culture change with

the age of the culture, getting a potentiation effect

in the spontaneous ﬁring. A young neuroblastoma

culture (1-5 DIV) has a low spontaneous ﬁring ac-

tivity, with a signal to noise ratio barely higher than

1:1. A mature neuroblastoma culture (1-15 DIV)

have a higher spontaneous ﬁring activity and its snr

may be higher than 2:1, but still is lower than snr

of other cells. The physiological recordings corre-

spond to neuroblastoma cultures in the range of 1-7

div. They showed bursting and spiking activity, with

usually negative depolarizations. It was used also an

automatic detection level for each electrode. This is

LEARNING IN BIOLOGICAL NEUROPROCESSORS USING A CENTER OF AREA METHOD

341

2 3

4 5

6 7 8 9

E58

E19

E22

E26

E29

E30

It. E58 E19 E26 E29 E30

1 7 11 4 7 17 4

2 7 7 3 10 12 9

3 4 8 5 9 11 5

4 6 12 5 9 11 5

5 15 9 10 6 13 4

6 8 6 4 7 8 1

7 8 2 3 14 15 1

8 12 2 10 9 12 4

9 9 6 6 8 10 4

E22

2360 25 28 31 34 36 61

20 21 24 29 35 38 39

18 19 27 32 37 40 41

15 16 17 26 33 42 43 44

14 13 12 3 56 47 46 45

11 10 7 2 57 52 49 78

9 8 5 0 59 54 51 50

62 6 4 1 58 55 53 63

Figure 8: Spontaneous neural activity detected previous to

tetanization. Spikes number (y-axis) from six electrodes

were recorded 9 times (x-axis). Left table shows data in nu-

meric format, right table shows a MEA representation with

selected electrodes marked out. Spikes number is always

below 20.

very convenient if one has multiple channels for ex-

tracting spikes.

The standard deviation of each data trace was

used to estimate its spike threshold and computing the

spikes per channel. A time interval of 500 ms was

used to calculate the standard deviation. The thresh-

old was ﬁxed at standard deviation equal to −4 with

respect to the electrode activity in order to identify

spikes embedded in the noisy signals.

Spontaneous activity was recorded for intervals of

5 seconds before stimulation, and the total number of

spikes extracted was counted for each channel. Figure

8 shows the total number of spikes per channel, and

a graphic visualization of this data for six different

electrodes located at different positions at the neural

culture.

When the tetanization conﬁguration shown in Fig-

ure 7 was applied, the spiking characteristics of the

neuroblastoma culture changed. The computed spikes

per channel is shown in Figure 9 during the tetaniza-

tion process. It can be seen that the most signiﬁcant

increment registered is at electrode 58, that matches

the center of area of the provided image, guiding in

this way the robot to the light. When the image of

the cave was presented once again, that is the same

selective stimulation was provided, the registered ac-

tivity was again modiﬁed. In Figure 10 it can be seen

a clearly potentiation effect in electrode 58 about 4

times, while the rest of the electrodes did not show

any signiﬁcant increase. In this way, selecting the ori-

entation of the robot as the neuron or group of neu-

rons that increase in a more quantitative aspect its ﬁr-

ing characteristics, it is possible to guide the robot to

the light or the brightest area of the discretized scene.

This changes last for seconds, in this case a biped

robot will be the perfect candidate due to its limited

2 3

4 5

E58

E19

E22

E26

E29

E30

It. E58 E19 E22 E26 E29 E30

1 5 7 6 16 6 4

2 5 1 6 12 18 4

3 3 9 6 10 6 3

4 46 13 7 12 6 4

5 9 7 3 4 14 3

6 7 4 7 8 7 4

2360 25 28 31 34 36 61

20 21 24 29 35 38 39

18 19 27 32 37 40 41

15 16 17 26 33 42 43 44

14 13 12 3 56 47 46 45

11 10 7 2 57 52 49 78

9 8 5 0 59 54 51 50

62 6 4 1 58 55 53 63

Figure 9: Spontaneous neural activity detected during

tetanization. It shows a considerable increase in activation

at electrode 58. See ﬁgure 8 caption for more explanations.

250

200

150

100

2 3

4 5

E58

E19

E22

E26

E29

E30

It . E58 E19 E22 E26 E29 E30

1 23 10 8 8 5 5

2 148 10 11 11 20 8

3 198 15 7 15 15 3

4 33 15 5 9 16 11

5 24 9 4 8 14 6

6 3 14 9 8 13 4

2360 25 28 31 34 36 61

20 21 24 29 35 38 39

18 19 27 32 37 40 41

15 16 17 26 33 42 43 44

14 13 12 3 56 47 46 45

11 10 7 2 57 52 49 78

9 8 5 0 59 54 51 50

62 6 4 1 58 55 53 63

Figure 10: Spontaneous neural activity detected after se-

lective tetanization. Note the different y-axis scale in this

ﬁgure respect to ﬁgures 8 and 9. Activation at electrode 58

is bigger than before.

movement. The robotic control will be reﬁned in fu-

ture works, while in this paper the plasticity of the

center of area stimulation is presented.

6 CONCLUSIONS

Learning in cultured neuroblastoma networks by a

stimulation process requires identifying the correct

stimuli to provide to the neurons in culture. These

neuroblastoma networks form a large culture covering

the whole electrode array and generating a rich den-

dritic conﬁguration. The connectivity can be modu-

lated by external stimulation as has been described in

many studies, (Bakkum et al., 2007; Bakkum et al.,

2008b; Bakkum et al., 2008a; Chao et al., 2008), but

also the activity of the network can be modulated with

the appropriate stimulation scheme. Tetanization con-

sists in high-frequency stimulation to the culture, in

order to cause an increase in transmitter release called

post-tetanic potentiation. The results illustrate the ex-

istence of qualitatively different responses to stimu-

ICFC 2010 - International Conference on Fuzzy Computation

342

lation. Our results indicate the existence of a clear

facilitation mechanism in response to the tetaniza-

tion stimuli at different stages of cell development.

By selective tetanizing some parts of the culture, the

network changes its ﬁring characteristics in seconds,

modifying in this way its electrical behavior, and it

has been shown that if the brightest area of the sce-

nario, induces more stimulation in its corresponding

part of the culture, then the increase in the ﬁring prop-

erties of the neurons that represent the area where

light is detected is observed. Future work consists in

determining the optimal stimulation to apply for in-

ducing permanent ﬁring changes in the culture. These

aspects will then constitute the basis for analyzing the

behavior change by adding chemicals to the culture,

and for designing new optogenetic hybrid learning

schemes. A more detailed robotic control will be also

studied analyzing the culture time responses.

ACKNOWLEDGEMENTS

This work was supported by the Spanish Govern-

ment through grants TIN2008-06893-C03,TEC2006-

14186-C02-02 and SAF2008-03694, Cátedra Bidons

Egara, Fundación Séneca 08788/PI/08, CIBER-BBN

and by the European Comission through the project

"‘NEUROPROBES"’ IST-027017.

REFERENCES

Álvarez Sánchez, J. R., de la Paz López, F., Cuadra

Troncoso, J. M., and de Santos Sierra, D.

(2010). Reactive Navigation in Real Environ-

ments Using Partial Center of Area Method.

Robotics and Autonomous Systems. In press,

http://dx.doi.org/10.1016/j.robot.2010.05.009.

Álvarez Sánchez, J. R., de la Paz López, F., Cuadra Tron-

coso, J. M., and Rosado Sánchez, J. I. (2009). Par-

tial Center of Area Method Used for Reactive Au-

tonomous Robot Navigation. In Mira, J., Ferrández,

J. M., Álvarez, J. R., de la Paz, F., and Toledo, F. J.,

editors, Bioinspired Applications in Artiﬁcial and Nat-

ural Computation, volume 5602 of LNCS, pages 408–

418. Springer Verlag.

Antonov, I., Antonova, I., and Kandel, E. (2003). Activity-

dependent presynaptic facilitation and hebbian LTP

are both required and interact during classical condi-

tioning in aplysia. Neuron, 37(1):135–147.

Bading, H. and Greenberg, M. (1991). Stimulation of pro-

tein tyrosine phosphorylation by NMDA receptor ac-

tivation. Science, 253(5022):912–914.

Bakkum, D. J., Chao, Z. C., and Potter, S. M. (2008a).

Long-term activity-dependent plasticity of action po-

tential propagation delay and amplitude in cortical

networks. PLoS One, 3(5):e2088. Online Open-

Access paper.

Bakkum, D. J., Chao, Z. C., and Potter, S. M. (2008b).

Spatio-temporal electrical stimuli shape behavior of

an embodied cortical network in a goal-directed learn-

ing task. Journal of Neural Engineering, 5:310–323.

Bakkum, D. J., Gamblen, P. M., Ben-Ary, G., Chao, Z. C.,

and Potter, S. M. (2007). MEART: the semi-living

artist. Frontiers in NeuroRobotics, 1(5):1–10. Online

Open-Access paper.

Chao, Z. C., Bakkum, D. J., and Potter, S. M. (2008).

Shaping embodied neural networks for adaptive goal-

directed behavior. PLoS Computational Biology,

4(3):e1000042. Online Open-Access paper, supple-

ment, and movie.

Esposti, F., Signorini, M. G., Potter, S. M., and Cerutti,

S. (2009). Statistical long-term correlations in dis-

sociated cortical neuron recordings. IEEE Transac-

tions on Neural Systems & Rehabilitation Engineer-

ing, 17(4):364–9.

Hales, C. M., Rolston, J. D., and Potter, S. M. (2010).

How to culture, record and stimulate neuronal net-

works on micro-electrode arrays (MEAs). JoVE,

39. doi: 10.3791/2056. Online video tutorial:

http://www.jove.com/index/Details.stp?ID=2056.

Jimbo, Y., Robinson, H., and Kawana, A. (1998). Strength-

ening of synchronized activity by tetanic stimulation

in cortical cultures: application of planar electrode ar-

rays. IEEE transactions on Biomedical Engineering,

45(11):1297–1304.

Madhavan, R., Chao, Z. C., and Potter, S. M. (2007). Plas-

ticity of recurring spatiotemporal activity patterns in

cortical networks. Physical Biology, pages 181–193.

Rolston, J. D., Gross, R. E., and Potter, S. M. (2009). A

low-cost multielectrode system for data acquisition

and real-time processing with rapid recovery from

stimulation artifacts. Frontiers in Neuroengineering,

2(12):1–17. Online Open-Access paper.

Rolston, J. D., Wagenaar, D. A., and Potter, S. M. (2007).

Precisely timed spatiotemporal patterns of neural ac-

tivity in dissociated cortical cultures. Neuroscience,

148:294–303.

Wagenaar, D. A., Pine, J., and Potter, S. M. (2006). An ex-

tremely rich repertoire of bursting patterns during the

development of cortical cultures. BMC Neuro-science,

7:11.

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