NEURAL ADAPTATION IN LOCAL REFLEX CONTROL OF LIMB

MOVEMENTS

Oliver P. Dewhirst

, Natalia Angarita-Jaimes

, David M. Simpson

, Robert Allen

Carlos D. Maciel

and Philip L. Newland

Institute of Sound and Vibration Research, University of Southampton, SO17 1BJ, Southampton, U.K.

Dep. de Eng. El´etrica - Escola de Eng. de S˜ao Carlos, Universidade de S˜ao Paulo

Av. Trabalhador S˜ao-Carlense, 400, Centro, CEP 13566-590, S˜ao Carlos-SP, Brazil

Centre for Biological Sciences, Building 85, University of Southampton, Highﬁeld Campus, Southampton, SO17 1BJ, U.K.

Keywords:

Neural Adaptation, Local Reﬂex Limb Control, Locust, Motor Neuron.

Abstract:

Neural adaptation, a change in the response of a neuron to repetitive stimulation, is a widespread property of

neurons in many networks, including those controlling local reﬂex limb movements. The majority of previous

studies have investigated the steady state properties of neurons rather than considering those of their adapting

(transient) response. Bandlimited Gaussian White Noise, sinusoidal and walking stimulation signals have

therefore, for the ﬁrst time, been used to investigate neural adaptation in ﬂexor and extensor motor neurons in

the locusts local hind limb control system. Our results show that the adaptation rate of the response of two

extensor and one ﬂexor motor neuron are the same. We also show that the adaptation rate of the Fast Extensor

Tibia motor neuron is affected by the properties of the stimulation signal.

1 INTRODUCTION

The adaptation of the response of individual or

networks of neurons to constant stimulation is a

widespread property of vertebrate and invertebrate

nervous systems (Prescott and Sejnowski, 2008). It

can be caused by a number of processes such as in-

trinsic cell mechanisms or extrinsic factors such as the

mechanical properties of sensory receptors (French,

1986) and has many functions including gain control

(Brenner et al., 2000).

Invertebrates provide the opportunity to gain

physiological insight into a system which is simpler

and more accessible than many vertebrate counter-

parts (Bassler, 1993). The current study concerns the

resistance reﬂexes in the network of neurons which

help to maintain postural stability in the hind leg of

the locust. The sensory, inter and motor neurons in

this local network are known to adapt their output

amplitude or spike ﬁring rate to repetitive stimula-

tion (Field and Burrows, 1982; DiCaprio et al., 2002;

Gamble and DiCaprio, 2003). An example of adapta-

tion can be seen in the reﬂex response of the locusts

Fast Extensor Tibia (FETi) motor neuron to GWN

stimulation of its stretch receptor, the Femoro-tibial

Chordotonal Organ (FeCO) (Figure 1). The FeCO

monitors the position of the tibia about the femur in

the hind leg of a locust (Burrows, 1996).

Many previous studies (Newland and Kondoh,

1997; Field and Burrows, 1982; Gamble and Di-

Caprio, 2003; DiCaprio et al., 2002) focus on the

steady state (SS)(Figure 1C) response of reﬂex limb

control systems. These studies have used either si-

nusoidal type stimulation or Gaussian White Noise

(GWN) with system identiﬁcation modelling meth-

ods to characterise neuronal responses. As the adapt-

ing or transient (TR) response may be more important

for reﬂex control we investigate the TR responses of

the locusts motor neurons to GWN and functionally

more relevant natural stimulation. The TR and SS re-

sponses are shown in Figure 1C between 4-8 s and

15-40 s respectively. Our aim is to determine if adap-

tation rate differs between motor neurons and if it is

affected by the properties of the stimulation signal.

The motivation for this study is to gain deeper un-

derstanding of insect neurophysiology but this work

may be of practical relevance for optimising the treat-

ment of patients with neuromuscular dysfunction. It

may also allow the features of such biological sys-

tems to be exploited to improve the design of engi-

neering control systems used in robotic applications

(bio-inspired design)(Bar-Cohen, 2006).

398

P. Dewhirst O., Angarita-Jaimes N., M. Simpson D., Allen R., D. Maciel C. and L. Newland P..

NEURAL ADAPTATION IN LOCAL REFLEX CONTROL OF LIMB MOVEMENTS.

DOI: 10.5220/0003783203980401

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2012), pages 398-401

ISBN: 978-989-8425-89-8

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

2 METHODS

Preparation of the adult male and female locusts,

Schistocerca gregaria (Forsk˚al) follows that used pre-

viously (Newland and Kondoh, 1997). To summarise,

locusts were mounted ventral side uppermost in mod-

elling clay, a hind leg was rotated through 90

◦

and

ﬁxed anterior face uppermost. The angle between the

femur and the abdomen, and the tibia and the femur,

was set at 60

◦

. The apodeme of the FeCO (Figure

1A) was exposed and clamped by forceps. The for-

ceps were mounted on a shaker (Ling Altec 101, LDS

Test and Measurement) and were moved by differ-

ent stimulus signals. Two bandlimited GWN signals

(27Hz and 50Hz cut off frequencies), sinusoidal (2,5

and 10Hz) and two “walking” signals were used in the

current study. The 27Hz GWN signal was generated

using a random binary generator (CG-742, NF Circuit

Design Block) and bandlimited using a low pass ﬁl-

ter (SR-4BL, NF Circuit Design Block). The 50Hz

GWN signal and the sinusoidal and walking signals

were generated in MATLAB. The “walking” signals

were estimated from ﬁlmed recordings of the locusts

obtained from a high speed camera (Redlake Imag-

ing, Tring, UK) during walking. Apodeme position

was converted to femoro-tibial angle using previous

results (Field and Burrows, 1982). The GWN input

was scaled so that ∼99.7% of its values fell between

5 and 115

◦

. Sinusoidal stimulus signals had ampli-

tude values corresponding to a joint range between 16

and 102

◦

and walking signals between 30 and 90

◦

A small window was cut in the ventral thorax

to gain access to the meso- and meta- thoracic gan-

glia. The ganglia were supported by a wax coated sil-

ver platform which also served as the reference elec-

trode. Glass micro-electrodes, ﬁlled with potassium

acetate, were driven into the soma of the motor neu-

rons (Figure 1A). Intracellular recordings were made

from the posterior intermediate ﬂexor tibiae (PIFlTi)

and the slow and fast extensor tibia (SETi and FETi)

motor neurons with the use of an Axoclamp 2A am-

pliﬁer (Axon Instruments). Signals were digitised

=10KHz) using the USB 2527 data acquisition

board (Measurement Computing, Norton, MA, USA)

and stored on a computer hard-drive for further anal-

ysis. An example of the response of the FETi mo-

tor neuron to 50Hz GWN stimulation of the FeCO

is shown in Figure 1C. As only synaptic inputs were

recorded in the motor neurons we quantify adapta-

tion by calculating the power in 1 s long non overlap-

ping blocks of data. The responses recorded from the

PIFlTi, SETi and FETi motor neurons to 27Hz ban-

dlimited GWN stimulation were used to investigate

whether adaptation rate varied between motor neu-

120

Angle(degs.)

-1

Amp.(mV)

0 10 20 30 40

Tibia

Femur

FeCO

(stretchsensor)

Apodeme

Electrode

Forceps

Systeminput(position)-Voltage

inputtoshakerdrivesforceps

whichmoveapodeme

Non-spiking

interneurons

Sensory

neurons

Metathoracicganglia

Motor

neurons

Brain

Abdominal

ganglia

Thoracic

ganglia

Zoomintohindleg

reflexcontrolsystem

Systemoutput(electrical)-Intracellularpost

synapticpotentialsrecordedinthesomaof

themotorneurons

Extensor

muscle

Pivot

point

Loop

structure

Time(s)

TR SS

Figure 1: The locusts nervous system (A) and an example

intracellular recording from the FETimotor neuron showing

how it adapts its response to 50Hz bandlimited GWN stim-

ulation of the FeCO (B,C). The GWN input signal applied

to the FeCO is shown in (B). An example of the intracellular

post synaptic response of a FETi motor neuron to this stim-

ulus is shown in (C). Its transient (TR), adapting response

can be seen in (C) between ∼ 4 and 8 s. Its steady state

response (SS) can be seen between ∼ 15 and 40 s. Signals

were preprocessed to remove slow time varying drifts using

a high pass ﬁlter with a cut off frequency of 0.5Hz.

rons. The effect on adaptation rate was investigated

when the FETi was stimulated with 50Hz bandlim-

ited GWN, sinusoidal and walking stimulation sig-

nals. The change in neural power over time was mod-

elled using the exponential equation y(t) = A+Be

(

−t

)

where t represents time and y(t) represents the neu-

rons neural power normalised by its base line power

(BLP). BLP was calculated from a 1 s window taken

from each recording before stimulation was applied.

The ﬁnal SS power is represented by A, and A + B is

the peak power amplitude. Adaptation rate was quan-

tiﬁed by the time constant τ which represents the time

NEURAL ADAPTATION IN LOCAL REFLEX CONTROL OF LIMB MOVEMENTS

399

0 10 20

0.5

1.5

Time (s)

Normalised power

FETi (µ)

FETi (± 2 )σ

PIFlTi (µ)

PIFlTi (± 2 )σ

SETi (µ)

SETi (± 2 )σ

0 10 20

Time (s)

GWN (µ)

GWN (± 2 )σ

S2 (µ)

S2 (± 2 )σ

S5 (µ)

S5 (± 2 )σ

S10 (µ)

S10 (± 2 )σ

0 10 20

Time (s)

GWN (µ)

GWN (± 2 )σ

W1 (µ)

W1 (± 2 )σ

W2 (µ)

W2 (± 2 )σ

Figure 2: Adaptation of the different motor neurons to the same 27Hz bandlimited GWN stimulation signal (A) and adaptation

of the response of the FETi motor neuron to different stimulus signals (B,C). (A) shows the mean and ± 2 std of power in the

response of PIFlTi (n=6), SETi (n=6) and FETi (n=14) motor neurons. (B,C) show how the power (mean and ± 2 std) in the

response recorded from the FETi motor neuron varies when different stimulation signal types are applied. GWN, S2, S5 and

S10 represent the 50Hz bandlimited GWN, 2Hz, 5Hz and 10Hz sinusoidal stimulation signals respectively. W1 and W2 are

the two walking stimulation signals. Five recordings for each stimulus signal type were made from FETi in different animals.

taken for the BLP normalised neural power to fall by

63.2% of B. The Nelder Mead (simplex) (Nelder and

Mead, 1965) iterative search method was used to es-

timate the time constant τ.

3 RESULTS

To investigate whether adaptation rate varies in differ-

ent motor neurons we calculated and plotted the mean

power and ± 2 standard deviations in the response of

one ﬂexor motor neuron (PIFlTi)(n=6) and two dis-

tinct physiological types of extensor motor neuron,

fast (FETi), and slow (SETi)(n=14and 6) (Figure 2A).

Each recording was made from a different animal. To

aid comparison, power values were normalised by the

value calculated from the ﬁrst window. TR and SS

sections of responses were deﬁned using mean power

levels (Figure 2A) and visual analysis of the signals.

Thus the TR response was deﬁned as the response

which occurred within the ﬁrst 3 s after stimulus on-

set (Figure 2A). The SS response was deﬁned as the

response which occurred after 10 s of stimulus on-

set (Figure 2A). The power of all three motor neuron

types falls to approximately 40% of their initial val-

ues within the TR section (Figure 2A). The median

difference of the mean of the power in the TR and SS

sections is signiﬁcantly different from zero (Wilcoxon

signed-rank test, p < 0.05) for each motor neuron

type. Signal power remains relatively constant in the

SS section (Figure 2A, B and C). The rate of adap-

tation of the response of the PIFlTi, SETi and FETi

motor neurons to 27Hz bandlimited GWN appears to

be similar (Figure 2A, B). There was no signiﬁcant

difference between the time constant values for the

different motor neurons (p=0.48, Kruskal Wallice).

To investigate if stimulus signal properties affect

adaptation rate we calculated the mean power and ±

2 standard deviationsof the responses of the FETi mo-

tor neuron to bandlimited 50Hz GWN, sinusoidal (2,

5 and 10Hz) and two walking input signals (Figure 2B

and C). For each stimulus type results were obtained

from recordings made in 5 different animals. Visual

analysis of the results suggests that adaptation rate is

similar for the differentstimulation signals (Figure 2B

and C). However, we found a signiﬁcant difference

between the time constant values (p=0.01, Kruskal

Wallice); a post hoc (Dunn-Sidak) test revealed a dif-

ference between the walk 2 and the 5Hz sinusoidal

stimulation signal.

The effect that stimulation signal type had on

adaptation was probed further by investigating how

adaptation rate varied with transient response power

(TRP). Both TRP and the time constant of the expo-

nential function ﬁtted to the power values were nor-

malised by the base line power. In general the scat-

ter plots of time constant against TRP normalised by

BLP (Figure 3) show no correlation between vari-

ables (Spearman rank correlation). The result when

10Hz sinusoidal stimulation is applied suggests that

the variables are correlated but not at the 95% signiﬁ-

cance level (r=0.9, p=0.08)(Figure 3 S10).

4 DISCUSSION

The aim was to investigate whether adaptation rate

differs between motor neuron types and if it is af-

fected by the properties of the stimulation signal.

Adaptation rate was quantiﬁed by the time constant of

BIOSIGNALS 2012 - International Conference on Bio-inspired Systems and Signal Processing

400

τ (normalisedtimeconstant)

GWN

(r=0.30,p=0.68)

(r=0.00,p=1.00)

(r=0.50,p=0.45)

0 50

S10

(r=0.90,p=0.08)

0 50

TRP/BLP

(r=−0.30,p=0.68)

0 50

(r=0.60,p=0.35)

Figure 3: Relationship between the rate of adaptation, mea-

sured by the time constant of an exponential function ﬁt-

ted to the power values, and the transient response power

(TRP). Both values are normalised by the base line power

(BLP) which is calculated from a 1s window taken from the

recording before stimulation is applied. GWN, S2, S5 and

S10 represent the 50Hz bandlimited GWN, 2Hz, 5Hz and

10Hz sinusoidal stimulation signals respectively. W1 and

W2 are the walking signals. Five recordings for each stim-

ulus signal type were made from FETi in different animals.

Correlation is tested using Spearman rank correlation. We

found no correlation between TRP and the time constant.

an exponential function ﬁtted to the response power of

the motor neurons. We haveshown that the adaptation

rate of the response of two extensor (SETi and FETi)

and one ﬂexor (PlFITi) motor neuron to 27Hz ban-

dlimited GWN stimulation of the FeCO is the same.

Our results are contrary to those found in the study by

Field and Burrows (1982). They found that the PlFITi

motor neuron showed little difference in its response

after 10 s of stimulation, whereas we found a drop in

power of approximately 40% after 3 s. Field and Bur-

rows (1982) also found that the fast and slow ﬂexor

motor neurons exhibited different rates of adaptation

whilst we found that the fast and slow extensor mo-

tor neurons exhibited the same rate of adaptation. It

should be noted that they stimulated the FeCO using

repetitive triangular movements with a frequency of

5Hz and femoral tibial joint angles between 40

◦

and

◦

. This is very different from the 27Hz bandlimited

GWN used in the current study and their excursion of

± 20

◦

does not cover the full range of movement that

occurs during walking or kicking. Our results could

also occur because the extensor and ﬂexor neurons

may share common inputs, a property well known to

occur in vertebrate studies (Luca and Erim, 2002).

We have also shown that the adaptation rate of the

FETi motor neuron can be affected by the properties

of the stimulation signal, as has been found in other

work (Fraser et al., 2006). We have used correlation

analysis to show that FETi response power decreases

to its steady state level within the same amount of

time regardless of its transient response power (both

BLP normalised). Further experiments and analysis

are required to understand how this adaptation affects

the function of the reﬂex response.

ACKNOWLEDGEMENTS

This work was supported by the BBSRC, EPSRC and

the Gerald Kerkut Charitable Trust.

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