=Paper=
{{Paper
|id=Vol-1452/paper1
|storemode=property
|title=Mathematical Model of the Impulses Transformation Processes in Natural Neurons for Biologically Inspired Control Systems Development
|pdfUrl=https://ceur-ws.org/Vol-1452/paper1.pdf
|volume=Vol-1452
|dblpUrl=https://dblp.org/rec/conf/aist/BakhshievG15
}}
==Mathematical Model of the Impulses Transformation Processes in Natural Neurons for Biologically Inspired Control Systems Development==
https://ceur-ws.org/Vol-1452/paper1.pdf
Mathematical Model of the Impulses Transformation
Processes in Natural Neurons for Biologically Inspired
Control Systems Development
Bakhshiev A.V., Gundelakh F.V.
Russian State Scientific Center for Robotics and Technical Cybernetics (RTC) , Saint-
Petersburg, Russian Federation
{alexab, f.gundelakh}@rtc.ru
Abstract. One of the trends in the development of control systems for autono-
mous mobile robots is the approach of using neural networks with biologically
plausible architecture. Formal neurons do not take into account some important
properties of a biological neuron, which are necessary for this task. Namely - a
consideration of the dynamics of data changing in neural networks; difficulties
in describing the structure of the network, which cannot be reduced to the
known regular architectures; as well as difficulties in the implementation of bio-
logically plausible learning algorithms for such networks. Existing neurophys-
iological models of neurons describe chemical processes occurring in a cell,
which is too low level of abstraction.
The paper proposes a neuron’s model, which is devoid of disadvantages de-
scribed above. The feature of this model is description cell possibility with tree-
structured architecture dendrites. All functional changes are formed by modify-
ing structural organization of membrane and synapses instead of parametric
tuning. The paper also contains some examples of neural structures for motion
control based on this model of a neuron and similar to biological structures of
the peripheral nervous system.
Keywords: neural network, natural neuron model, control system, biologically
inspired neural network, motion control
1 Introduction
Nowadays, a lot of attention is paid to the study of the nervous system’s functioning
principles in the problems of motion control and data processing and the creation of
biologically inspired technical analogues for robotics [1,2,3].
At the same time borrowing just part of the data processing cycle inherent to natu-
ral neural structures, seems to be ineffective. In this case, we can't avoid the step of
converting the "inner world's picture" of our model, expressed in the structure and set
of the neural network's parameters, set up in the narrow context in the terms of current
problem. Such conversion can nullify the effectiveness of the approach. It is neces-
sary to start with a construction of simple self-contained systems that function in an
1
environment model, and then gradually complicate them. For example, it is possible
to synthesize the control system functionally similar to the reflex arc of human nerv-
ous system (Fig. 1).
Position control neural
network
Data from afferent neurons Control action on interneurons
Controller neural network
Data from sensors Control action
Actuator
Fig. 1. Control system similar to reflex arc of human nervous system
In this case, position control neural network has input and output layers of neurons,
as well as several hidden layers. Input and output layers have connections with neu-
rons of other neural networks, while neurons of the hidden layers are connected only
to the neurons of current neural network [4].
However, the most promising is the development of full-scale systems that imple-
ment all phases of the data transformation from sensors to effectors inherent to natural
prototypes.
There are many models of neuronal and neural networks. These models may be
quite clearly divided into two groups: for applied engineering problems (derived from
the formal neuron model) [5], and models, designed for the most complete quantita-
tive description of the processes occurring in biological neurons and neural networks
[6,7] .
Considering modeling of natural neuron, we investigate the transition from formal
neuron models to more complex models of neurons as a dynamic system for data
transformation [8] suitable for control tasks (Fig. 2).
2
Fig. 2. Evolution of neuron model
Where x1 – xm - neuron input signals;
w1 – wm - weights;
y - neuron output signal;
u - membrane potential value;
- threshold function;
F - activation function;
N - number of membrane segments at the dendrite branching node;
C , C - constants for expected level of membrane potential contribution;
u, u - contributions to the membrane potential from depolarizing and
hyperpolarizing ionic mechanisms;
Figure 2-1 represents a universal model of the formal neuron in general. Classic
formal neurons can be derived from this model, if we abandon the temporal summa-
tion of signals to establish a fixed threshold and choose, for example, a sigmoid acti-
vation function.
Further development of this model may be adding a description of the structural
organization of the neuron membrane (Fig. 2-2), with a separate calculation of the
contribution to the total potential (Fig. 2-3) to provide at each site the ability to inte-
grate information about the processes occurring with different speeds, as well as re-
jection of the an explicit threshold setting and move to the representation of the signal
in the neural network as a stream of pulses (Fig. 2-4). As a result, the potential value
of the neuron membrane segment is derived not only from the values of the inputs and
the weights of synapses, but also from the average value of the membrane potential of
other connected membrane segments. This will simulate the structure of the dendritic
and synaptic apparatus of neurons and carry out more complex calculations of the
spatial and temporal summation of signals on the membrane of the neuron. Thus,
membrane segment should be considered as the minimal functional element of the
neural network.
Given the existence of temporal summation of signals, the structural organization
allows to implement separate processing of signals with different functionality on a
3
single neuron. To do this, may be selected a single dendrite, which will provide, for
example, only the summation of signals on the current position of the control object
formed by afferent neurons, as well as to the signal of corrections to position, that
formed by the highest level of control. The individual dendrite will implement similar
behavior, for example, the speed of the object and the body of the neuron will provide
the integral combination of these control loops, which otherwise would require adding
an additional neuron.
2 Neuron model
It is assumed that the inputs of the model get pulsed streams, which are converted by
synapses into the analog values that describe the processes of releasing and metabo-
lizing of the neurotransmitter in the synaptic cleft. The model assumes that the input
and output signals of the neuron is zero for the absence of a pulse, and constant for
the duration of the pulse. The pulse duration is determined by the time parameters of
the neuron’s membrane. Membrane of soma and dendrites is represented by a set of
pairs of ionic mechanisms’ models that describe the function of depolarization and
hyperpolarization mechanisms, respectively. The outputs of the ionic mechanisms’
models represent the total contribution to the intracellular potential of depolarization
and hyperpolarization processes occurring in the cell. The signals from the synapses
modifies the ionic mechanisms’ activity in the direction of weakening their functions,
which simulates the change in the concentration of ions inside the cell under the in-
fluence of external influences. It is proposed to distinguish the type of ionic mecha-
nism in the sign of the output signal. A positive value of the output characterizes de-
polarizing influence, while negative characterizes hyperpolarization. Thus, the total
value of the output values will characterize the magnitude of the membrane segment
contribution to the total intracellular neuron potential [9].
The role of synaptic apparatus in the model is the primary processing of the input
signals. It should be noted that the pattern of excitatory and inhibitory synapses are
also identical to each other, and the difference in their effects on cell’s membranes is
determined by which of the ionic mechanisms each particular synapse is connected to.
Each synapse in this model describes a group of natural neuron synapses.
More detailed model of the membrane is shown in Fig. 3.
gsiΣ
waΣ
Тормозной
Inhibitoryионный
ionic
uaΣ ua u ai
механизм IIaaii
mechanism
vaiΣ
Участок
Membrane
мембраны
segment Mi Mi
нейрона
vsiΣ
Возбуждающий ионный
Excitatory ionic
usΣ us us i
механизм IIssi i
mechanism
wsΣ
gaiΣ
Fig. 3. Functional diagram of the i-th membrane segment model Mi
4
Each membrane segment M i , i 1, L consists of a pair of mechanisms - hyperpolar-
ization mechanism ( I a i ), and depolarization mechanism ( I s i ). Output of the mem-
brane’s segment is a pair of the contribution values of hyperpolarization ( u a ) and
depolarization ( us ), which determines the contribution to the total intracellular poten-
tial.
Each membrane’s segment M i can be connected to previous membrane’s segment
j j
M j taking its values { ua , u s } as inputs. When specified membrane’s segment is the
last in the chain (the end of the dendrite or the segment of soma), as signals { ua j , u s j }
stands pair of fixed values {-Em, Em} simulating some of the normal concentration of
ions in the cell in a fully unexcited state.
Excitatory {xs ik }, k 1, M i and inhibitory {xa ik }, k 1, Ni neuron’s inputs are in-
puts of many models of excitatory {S s k }, k 1, M i and inhibitory {Sa k }, k 1, Ni
i i
i
synapses, for each of the membrane’s segments M .
The resulting values of the effective influence on the mechanisms of synaptic hy-
perpolarization ( g s i ) and depolarization ( g a i ) are obtained by summation:
Mi Ni
gs gs k , ga ga k .
i i i i
(1)
k 1 k 1
Outputs of all membrane segment models are summed by following formula:
1 L i
u u
L i 1
The resulting signal is assumed as total intracellular potential of the neuron. Each
pair (depolarization and hyperpolarization mechanisms), depending on their internal
properties, can be regarded as model of dendrite segment or soma segment. Increasing
the number of pairs of such mechanisms automatically increases the size of the neu-
ron, and allows simulating a neuron with a complex organization of synaptic and
dendritic apparatus.
Similarly, the summation of signals at branching nodes of dendrites - the total con-
j j
tribution of the hyperpolarization and depolarization mechanisms { ua , u s } are
divided by their number.
Fig. 4 contains a general view of the neuron’s membrane structure [10].
5
Dendrites Soma Low-threshold area
Uf
gsnΣ Membrane
n - i - 1
{Em} M M M overcharge feedback
1
ganΣ U
Generator of the
gsmΣ Branching node UΣ action potential Y
{Em} Mm Mk of dendrite 2 G(UΣ,P)
U
gamΣ Neuron
{Em} ML-1 threshold
- UL P
gsLΣ
{Em} ML
-
gaLΣ
Fig. 4. Structural diagram of the neuron membrane
The body of the neuron (soma), we assume those parts of the membrane that are
covered by feedback from the generator of the action potential. It should also be noted
that the closer a membrane’s segment located to the generator, the more effective its
contribution to the overall picture of synapses in neuronal excitation.
Thus, in terms of the model:
1. carried out on the dendrites spatial and temporal summation of signals over long
periods of time (a small contribution to the excitation of the neuron from each syn-
apse), and accumulation of potential does not depend on the neuron discharges;
2. in the soma of a neuron produced summation of signals at short intervals of time (a
big contribution to the excitation of the neuron from each synapse) and accumulat-
ed potential is lost when the neuron discharges;
3. in low-threshold area is carried impulse formation on reaching the threshold of
generation and signal of membrane recharge.
The following discloses the mathematical description of the neuron model elements.
Synapse model. It is known that the processes of releasing and metabolizing of the
neurotransmitter are exponential, and besides the process of releasing neurotransmit-
ter, usually is much faster than the metabolizing process.
Another important factor is the effect presynaptic inhibition consists in that, when
the concentration of the neurotransmitter exceeds certain limit values, synaptic influ-
ence on ion channel starts to decrease rapidly - despite the fact that the ion channel is
fully open. Reaching the limit concentration is possible when synapse is stimulated by
the pulsed streams with high pulse frequency.
Model that implements all three main features of the synapse’s functioning can be
described by the following equations:
6
d
(t ) E y x, g* 4 ( )
1
TS
2
dt (2)
1
s , при x 0, g Rs g* , при g* 0,
,
TS ,
d , при x 0. 0, при g* 0.
Where s - time constant of releasing neurotransmitter,
d - time constant of metabolizing neurotransmitter,
[0.5, ) - limit value of neurotransmitter’s concentration needed to
presynaptic inhibition effect,
RS 0 - synapse’s resistance (“weight”), that characterizes the efficiency
of synapse’s influence on the ionic mechanism,
E y - the amplitude of the input signal.
Initial conditions: (0) 0 .
Model’s input is a discrete signal x(t), which is a sequence of pulses with a dura-
tion of 1 ms and an amplitude E. The releasing and metabolizing processes of the
neurotransmitter are proposed to simulate the first order inertial element with logic
control by time constant. Variable characterizes the concentration of neurotrans-
mitter released in response to a pulse. Usage of variable g * allows us to simulate
presynaptic inhibition effect.
Model's output g(t) is an efficiency of influence on ionic mechanism and it is pro-
portional to the synapse's conduction. Thus, in the absence of input actions synapse
conductance tends to zero, which corresponds to the open switch in the equivalent
circuit of the membrane.
Model of membrane's ionic mechanism. It is known that the ion channel can be
represented by an equivalent electrical circuit [11], which has three major characteris-
tics - the resistance R m , capacitance C m and ion concentration Em v maintained
within the cell membrane pump function. Product Tm Rm C m characterizes inertia of
the channel that defines the rate of recovery of the normal concentration of ions E m
in the cell. Synapse’s influence on the ionic mechanism consists in the loss of effi-
ciency of the channel’s pumping function and reducing the ions’ concentration in the
cell, with the time constant of the process:
T R I Cm . (3)
Resistance RI is determined from the relation:
1 1 1 .
g1 g 2 ... g n g (4)
RI Rm Rm
7
Where g1 , g 2 ,..., g n - conductions of active synapses’ models that have an influ-
ence on the current ionic channel. Reduction in ions’ concentration at the same time is
proportional to the product g Rm and the less, the lower the ions’ concentration in
the cell is.
Fig. 5a shows the dependence of the synapse’s contribution in changing the mem-
brane potential on the ratio of the synapse’s channel and postsynaptic membrane’s
resistance. It can be seen that the effective control range of the synapse's resistance is
in the range [0.1: 10] of membrane's resistance. Fig. 5b shows the change in the po-
tential contribution to the number of active synapses in the ratio Rs/Rm = 10 (dashed
line) and 1 (solid line).
The ordinate axis in both graphs - normalized postsynaptic membrane potential
change in proportion to its nominal value. Fig. 5a: the dependence of the efficiency on
the ratio of the synapse's channel and the membrane's resistance. Fig. 5b: the depend-
ence of the efficiency on the number of synapses.
Fig. 5. Current efficiency of the synapse's model
Inertial properties of the ionic mechanism’s model are proposed to describe as an
aperiodic element with logic control by time constant. For the ionic mechanism of
depolarization equations have the following form:
du
TI dt (1 g s Rm ) u v
(5)
Cm , ga 0
T
I
g s Rm 1
u Em , g a 0
Where g a - the total efficiency of synapses influence on the hyperpolarization
mechanism,
g s - the total efficiency of synapses influence on the depolarization mech-
anism,
Rm 0 - membrane's resistance,
Cm 0 - membrane's capacitance,
8
v - the expected contribution of the model in the value of the intracellular
potential in the absence of external excitation. This value is determined by the activity
of neighboring membrane segments,
u - a real model’s contribution to the value of the intracellular potential.
Initial conditions: u(0)=0.
For ionic mechanism of hyperpolarization equations are analogous up to relocation
of the effects of excitatory and inhibitory synapses and Em - on Em+.
Action’s potential generator’s model. Generator’s model performs the formation of
rectangular pulses of given amplitude Ey as a result of exceeding fixed threshold P by
the potential u . The model can be described by the following equations:
du*
TG u* u , (6)
dt
y FG (u* ).
Where P > 0 – neuron’s threshold,
TG - time constant, which determines the duration of the feedback over-
charging membrane and characterizing pulse durations,
FG (u* ) - Function describing the hysteresis. The output of the function is
Ey, if u* P and zero if u* 0 .
Initial conditions: u* (0) 0 .
Output signal y(t) goes to overcharge feedbacks of cell’s soma.
3 Research
Setting the model’s parameters was based on experimental data on the time parame-
ters of the processes occurring in the natural neuron [10].
Fig. 6 shows a typical response of a neuron model to the exciting pulse. In the
graph of intracellular potential (2) can be seen a typical region of the neuron mem-
brane depolarization is preceded by the formation of an action potential, the zone of
hyperpolarization after pulse generation and residual membrane depolarization at the
end of the generation's pattern.
9
Fig. 6. Neuron with synapse on its dendrite (1 - stimulating effect 2 - intracellular membrane
potential on the generator of the action potential, 3 - neuron responses combined with the graph
of the intracellular potential)
One of the main characteristics of the natural neuron qualitatively affects the trans-
formation of the pulsed streams is the size of the membrane. Unlike small neuron
large neuron is less sensitive to the effects of input and generates a pulse sequence
typically in a lower frequency range and generally corresponds to input effects with
single pulses.
The developed model allows to build neurons with different membrane structure
and location of synapses on it. Changing the number of the membrane segments neu-
rons of different sizes can be modeled, without changing the values of the parameters.
With the increasing size of the soma at the same stimulation of the neuron number
of pulses in the pattern of neuron response decreases and the interval between them
increases. Fig. 7a demonstrates dependence of the response's average frequency from
the number of pulses Np in it. Fig. 7b demonstrates dependence of response's average
frequency from the number of neuron's soma segments L.
Fig. 7. Discharge frequency, depending on the neuron's soma size
As a simple neural structures with feedback considered element, which is a widely
held in the nervous system connection excitatory inhibitory neurons, first studied in
neurophysiological experiments, the interaction of motoneuron and Renshaw's cells
(Fig. 8).
excitatory effect Motoneuron
excitatory effect
inhibitory effect
Renshaw cell
Fig. 8. The scheme of recurrent inhibition by the example of the regulation of motoneuron
discharges
10
There are two mechanisms for increasing the strength of muscle contraction. The
first is to increase the pulse repetition frequency at the output of motoneuron. Second
- increasing the number of active motoneurons, the axons of which are connected to
the muscle fibers of the muscle. Specialized inhibition neuron in the chain of recur-
rent inhibition - Renshaw cell - limits and stabilizes the frequency of motoneuron
discharges. Example of such a structure shows an analog model (Fig. 9), the behavior
of which corresponds to neurophysiological data [11].
Fig. 9. Recording pulsed streams in studying the interaction of motoneuron and Renshaw cells
motoneuron at the excitation frequency of 20Hz (a) and 50 Hz (b): 1 - excitatory motoneuron
input; 2 - Renshaw cell's discharges; 3 - motoneuron output pulses. Above - the time stamp 10
ms
The graphs show that the frequency of motoneuron stimulation enhances the inhib-
itory effect on Renshaw cells with motoneuron, causing, in turn, decrease the fre-
quency of motoneuron discharges. Thus, when the frequency of motoneuron stimula-
tion increases, the frequency of the pulses at the output of the first moments increases
and then stabilizes at a low level with a duration of interpulse intervals determined by
the duration of the Renshaw cell’s discharge. It is essential that this limit is dependent
on whether the motoneuron by recurrent inhibition "own" Renshaw cells or not.
Computer simulation has allowed a more detailed study of the interaction of neurons.
The results of the experiment are shown in Fig. 10, where the top-down plotted in-
put pulsed stream at the input of motoneurons and pulsed streams of motoneuron
Renshaw cell with recurrent inhibition and, accordingly, these neurons without feed-
back when motoneuron excites Renshaw cell, but it does not slow motoneuron.
Fig. 10. Reactions of structure “motoneuron-Renshaw cell” upon excitation of motoneurons
pulsed stream at 50 Hz: 1 - input pulsed stream; 2 – motoneuron’s reaction with enabled FB; 3 -
Renshaw cell responses with enabled FB; 4 – motoneuron’s reaction without FB; 5 - Renshaw
cell responses without FB
11
4 Conclusion
The paper presents a model of a neuron, which can serve as the basis for constructing
models of neural networks of living organisms and study their applicability in solving
the problems of motion control of robotic systems. The model allows to describe the
structure of the neuron’s membrane (dendritic and synaptic apparatus).
Plasticity model is also based primarily on changes in the structure of the mem-
brane, rather than adjusting the parameters of the model (synapse weights, neuron’s
threshold, etc.), which simplifies the construction of models of specific known biolog-
ical neural structures.
5 Sources
1. McKinstry, J. L., Edelman, G. M., Krichmar, J. L.: A cerebellar model for predictive mo-
tor control tested in a brain-based device. PNAS, February 28, 2006, vol. 103, No.9, pp.
3387–3392 (2006)
2. Hugo de Garis, Chen Shuo, Ben Goertzel, Lian Ruiting.: A world survey of artificial brain
projects, Part I: Large-scale brain simulations. Neurocomputing 74, pp. 3–29 (2010)
3. Bakhshiev, A.V., Klochkov, I.V., Kosareva, V.L., Stankevich, L.A.: Neuromorphic robot
control systems. Robotic and Technical Cybernetics No. 2(3)/2014, pp.40–44. Russia,
Saint-Petersburg, RTC (2014)
4. Bakhshiev, A.V., Gundelakh. F.V.: Investigation of biosimilar neural network model for
motion control of robotic systems. Robotics and Artificial Intelligence: Proceedings of the
VI Russian Scientific Conference with international participation, 13 december 2014,
Zheleznogorsk, Russia (2014)
5. McCulloch, W. S., Pitts W.: A logical calculus of the ideas immanent in nervous activity //
Bulletin of Mathematical Biophysics, vol. 5, pp. 115-133 (1943)
6. Hodgkin, A.L., Huxley, A.F.: A quantitative description of membrane current and its ap-
plication to conduction and excitation in nerve. J. Physiology, 117, pp. 500–544 (1952)
7. Izhikevich, E.M.: Simple model of spiking neurons. IEEE transactions on neural networks.
A publication of the IEEE Neural Networks Council, vol. 14, No. 6, pp. 1569–1572
(2003)
8. Romanov, S.P.: Neuron model. Some problems of the Biological Cybernetics. Russia, pp.
276-282 (1972)
9. Bakhshiev, A.V., Romanov, S.P.: Neuron with arbitrary structure of dendrite, mathemati-
cal models of biological prototypes. Neurocomputers: development, application, Russia,
No.3, pp. 71-80 (2009)
10. Bakhshiev, A.V., Romanov, S.P.: Reproduction of the reactions of biological neurons as a
result of modeling structural and functional properties membrane and synaptic structural
organization. Neurocomputers: development, application, Russia, No.7, pp. 25–35 (2012)
11. John Carew Eccles. The Physiology of Synapses. Springer-Verlag (1964)
12