SU696497A1 - Neuron simulator - Google Patents

Description

The invention relates to the field of modeling the properties of biological neurons and can be used in radio systems, telecontrol, telemetry and automation, as well as in robot sensory systems. A device for simulating a neuron is known, which contains a unit for initial conditions, a sugator and a learning memory unit 1. The closest in technical essence to the proposed invention is a device for simulating a neuron, which contains three channels, each of which contains an integrator, the output of which is connected to control conductivity unit, the output of the controllable conductivity unit of the third channel is connected to the second input of the conductivity control unit of the second channel, the output of which is connected to the second The input of the controllable conductivity unit of the first channel, the output of which is connected to the input of the action potential generator, the output of which is the output of device 2. A disadvantage of the known devices is the low accuracy of the simulation. The purpose of the invention is to improve the accuracy of modeling. This goal is achieved by introducing a learning memory block and controllable synaptic coefficient setting blocks into each channel of the device, the first inputs of which are the device inputs and connected to the inputs of the training memory section of its channel, the outputs of which are connected to the second inputs of the control setting blocks of the synaptic coefficients of its channel, the outputs of which are connected to the inputs of the integrator of its channel. FIG. 1 shows a block diagram of a device containing L blocks of controlled synaptic coefficients in the first channel (in excitation) 1, M blocks of controlled synaptic coefficients in the second channel (in the postsynaptic inhibition channel) 2, N blocks of controlled synaptic coefficients in the third channel (in the presynaptic inhibition channel) 3, integrators 4-6 in the excitation channels, postsynaptic and presynaptic inhibition, respectively, blocks 7t9 of the learning memory in the excitation channels, postsyn pticheskogo and presynaptic inhibition sootvets-venno blocks 10t12 controllable conductivity in channels excitation postsynaptic and presynaptic inhibition generator 13 action potentials. FIG. 2 shows a diagram of the formation of a short-term and long-term memory for one of the synaptic inputs in one of the channels of the device.

The device works as follows. Pulse sequences arriving at each input are integrated in time on integrators 4TB. The total excitation potential E (t) supplied to the input of the generator of the action potential 13, the braking potential I (t) and the disinhibition D (t) are described by the equations:

 E: t-H, I (A) Mrt-A ,.)

-00. |

1 (O-fg t-L,) (L) a) (, 1-L, P (L) xr., A) dA 2) D (t) f rf (t-A) (il), (3)

-00 X :( where L, M, N are the number of excitatory, inhibitory and disarming inputs of the neuron, respectively, ,, - (t), g (t), n (i) are the weighing functions of the excitatory, inhibitory and disinhibition inputs, (jj {t) , U34- (t), iJ | cCt) - controllable synaptic coefficients, XE1, Xij. , HDK signals arriving at - and exciting, j- and inhibitory, to the th splitting input. The control signals for the coefficients (U (t) are the output signals of the learning memory blocks 7, 8.9 Pa) -j th-pa-1 opx-), V –O p; (t) rK-i ( t) with P (t) ej, T. (t) np (tA) (OpX,. () dA TipvtP / tKQ ,, i -oo with P (t) 0T,) К.1Ш PB, (t) Up (tA) (Op) RLR (1) e at P () / in, P Ji K-iCt) where dp is the weighting function of the unit for teaching and learning, time constant

which () BO many times exceeds the time constants of the channels of excitation, braking, disinhibition, a) the synaptic coefficients of the learning memory blocks, EE iSj, Etz are the thresholds of the threshold blocks 14 (Fig. 2) learning memory blocks, Kl (t) - step function, which is the output signal of threshold block -14 (Fig. 2).

Pulse sequences arriving at each input are integrated in time on integrators in the excitation channel, postsynaptic and presynaptic inhibition, and, in addition, in parallel arrive at integrators 15 (Fig. 2) of learning memory blocks. Here they are integrated with a large time constant (Eqs. 446). The output of the integrator 15 P (t) is fed to the input of the threshold unit 14 and in parallel to the element OR 16, the output signal of which serves to control the synaptic coefficient CJ. When the signal P / t reaches the threshold value Q, the signal Kl (t) appears at the output of the threshold block 14 (Fig. 2), many times larger than the amplitude of the signal P (t), and the output element OR 16 is set to a potential equal to according to the value of Kl (t), the unit for setting the controlled synaptic coefficient 1 works in the following way. In the absence of an input signal () or in the case of single impulses, the synaptic coefficient bJ of block 1 is equal to O, since block 1 is an AND element, the output of which is greater, the greater the control signal from the output of the OR 16 element. the largest value in the presence of the output element OR 16 signal to -1 (t). The presence of the Kl (t) signal simulates a long-term memory. This signal appears when there are frequently repeated signals X (t) at the input. The short term memory determines the amplitude of the signal P (t), which is also generated in the learning memory unit. The introduction of new structural elements provides modeling of short-term and long-term memory, which increases the accuracy of modeling. Formula of the Invention A neuron simulation device comprising three channels, each of which contains an integrator, the output of which is connected to the first

the input of the controllable conductivity unit, the output of the controllable conductivity unit of the third channel is connected to the second input of the controllable conductivity unit of the second channel, the output of which is connected to the second input of the controlled conductivity unit of the first channel, the output of which is connected to the input of the action potential generator, the output of which is the output of the device, characterized in that, in order to improve the accuracy of the simulation, a learning memory block and blocks for specifying controlled synaptic coefficients are inserted into each channel of the device The patients whose first inputs are device inputs and are connected to the inputs of the learning memory of their channel, the outputs of which are connected to the second inputs of the tasks of controlled synaptic coefficients of their channel, the outputs of KOTopfcDC are connected to the inputs of the integrated channel of their channel.

Sources of information

0 taken into account in the examination

1. USSR author's certificate No. 563680, c. 06 G 7/60, 1975,

2. Authors certificate of the USSR W 453710, cl. G 06 G 7/60, 1972.