Objective

The goal of this lab exercise is to study the synapse and understand its importance in transmission of a signal from one nerve cell to its neighboring cell. By varying the stimuli and membrane resistance, an action potential can be elicited. In the same way we are trying to explain synapse and synaptic conditions.

Here we are studying how an electrical synapse transmits signal from pre to post synaptic cell, by modeling an analog synapse, by modifying the synaptic weight and release probability modeled as synaptic gain (change in bias potential). This analog synapse is connected to a pre and post analog neuron, which we modeled in RC-experiment.

Theory

 Nerve cells have a special ability to communicate with another cell, rapidly, over a great distance with tremendous precision. We have studied how a signal is propagated in a single neuron and for a different input signal how an analog neuron will respond (see exp – 1). In this exercise we will be studying how a neuron transmits a signal form one cell to the  next through synapse. The process of transmission for a signal from one neuron to the next is called synaptic transmission. This property of nerve cells is the basic fundamental for many properties in the brain, such as perception, voluntary movement, and learning.

 A neuron forms an average of 1000 synaptic connections and receives even a higher number of connections. The Purkinje cell of the cerebellum receives up to 100,000 inputs. These large numbers of synapse and its transmission belongs to either chemical or electrical. Cellular activity can enhance or diminish in both forms of synaptic transmission; this property of brain is called plasticity. Plasticity is property of a synapse to adjust its weight according to the input signal in the presynaptic terminal. This plasticity in nerve cells is very crucial to memory and other higher functions in the brain. 

• Presynaptic terminal, which is the end of an axon (of the first neuron), contains tiny vesicles consisting of neurotransmitters - the small molecules which carry the nerve impulse from the sending neuron to the receiving neuron.
• Synaptic cleft, a gap between the two neurons across which the neurotransmitters migrate.
• Postsynaptic terminal, usually in the dendrites of receiving neurons (the second neuron). This contains receiving sites for the neurotransmitters.

Figure 1. Schematic representation of a presynaptic neuron(A) , synapse(D) and post synaptic neuron(B,C).

Electrical synapse

In electrical synapse pre- and postsynaptic cells communicate through special channels called gap-junction channels (see fig 2), through ions are passed to post synaptic cell at the time of synaptic transmission.

Gap-channels/Gap-junction connects pre and postsynaptic cells by providing low-resistance (high conductance) pathway for electrical current to flow between the two cells. The current injected in the presynaptic cell flows through these gap-junctions into post synaptic cell. This current deposits a positive charge on the inside of the membrane of the postsynaptic cell and depolarizes it. These current transmitted after the effect flows to extracellular medium through resting ion channels. If the depolarization exceeds threshold, voltage-gated ion channels in the postsynaptic cell will open and generate an action potential.

An action potential reaches to presynaptic terminal result for a depolarization in postsynaptic terminal that is often large enough to discharge an action potential. The conduction of current from pre to post synaptic terminal as ions, are in the speed of nano second (since there is no chemical reaction involved in this process), this remarkably short the latency—the time between the presynaptic spike and the postsynaptic potential. This unique property of electrical synapse makes the transmit signal faster.

Modeling biological synapse as analog synapse

We have modeled a synapse using basic electronic components; such as Resistors, Transistors, and Capacitors for imitating the electrical property of biological neuron. 

• Resistance represents the difficulty  a particle experiences while moving in a medium. It is measured in ohms. The inverse of resistance is conductance,  the ease at which a particle can move through a medium and it is measured in Siemens. Since they are inversely related, high conductance is correlated to low resistance, and vice versa. It is important to note that,  generally speaking, resistance and conduction in the neuron are dealing with the ability of ions to cross the membrane. Thus it often referred to as membrane resistance or membrane conductance. As such, when the majority of ion channels are closed, few ions cross the membrane, and membrane resistance is said to be high. 
• The capacitor is a passive electronic components consisting of pair of conductors separated by an insulator. The cell membrane has capacitance property. A capacitor consists of two conducting regions separated by an insulator. A capacitor works by accumulating a charge on one of the conducting surfaces. As this charge builds, it creates an electric field that pushes like charges on the other side of the insulator away. This causes an induced current known as a capacitive current. It is important to realize that there is no current between the conducting surfaces of the capacitor. Capacitance may be defined two ways: 1) an ability to store and separate charge, or 2) as the quantity of charge required to create a given potential difference between two conductors. Thus given a set number of charges on each side of the membrane, a higher capacitance results in a lower potential difference. In a cellular sense, increased capacitance requires a greater ion concentration difference across the membrane.
• Transistor is an active semiconductor device commonly used to amplify (strengthen) or switch electronic signal. Here we are using 3 transistors, two NPN and one PNP transistor. Transistor has mainly three terminals. Emitter (E), Base (B) and Collector(C). Transistor T1 and T3 are NPN transistor and T2 is a PNP transistor. For an NPN transistor collector voltage is more positive than emitter. So current flows from collector to emitter. For a PNP transistor emitter voltage is more positive than collector. So current flows from emitter to collector.

 Here we modeled an electronic circuit which mimics an electrical synapse. For studying information transmission, we connect two neurons using synapse circuit. Then we give square waveform as input stimuli to the first neuron.

 Before going through the synapse circuits, let us rewind the analog neuron circuit (Figure 3). Here we give an input excitation to the cell membrane as square wave form of amplitude 2Volt peak to peak (Vpp), since we want to obtain the output as pulse wave form. A square wave resembles to an impulse wave form in shape when pulse width is low. Here R1 represent a variable resistor which represent the membrane resistance and is inversely proportional to membrane conductance. By varying this R1 membrane, conductance can be changed considerabl,y i.e., when membrane resistance (R1) decreases the membrane conductance increases, making flow of signals easier.

Figure 3

Cm is the membrane capacitance. In all electrically excitable cells’ membrane causes a charge separation across the membrane. The separation of charge by an insulator causes a capacitive effect on the cell. This property is modeled as membrane capacitance. If there is only the resistor when the input voltage is applied, then voltage will be changed to steady state value, hence we are using a capacitor Cm along with it to resist this change.

When the applied input signal makes the membrane potential to cross a threshold value, then only neurons fired. The membrane potential is measured with respect to ground. When the input excitation is given the membrane capacitance Cm begins to charge, when the voltage across the capacitor reaches more than cut in voltage of transistor T1, the transistor turns on and the current flows from collector to emitter. Then the base voltage of transistor T2 becomes less and T2 also turns on and current flows from emitter to collector.

When the voltage across the capacitor C1 reaches more than cut in voltage of transistor T3, the transistor turns on and the potassium current flows from collector. By this time membrane capacitor Cm becomes fully charged and begins to discharge i.e., when the capacitor voltage drops transistor T1 turn off, consequently with T2. Then sodium current stops its flow (i.e., sodium channel closes). As a result capacitor C1 begins to discharge and transistor T3 turn off. Thus the repolarising  phase of an action potential.

Now we can connect two neurons using  a  synapse circuit and find out how information is transmitted across neurons. Here we modeled a circuit to study the different aspects of synapse. By this synaptic model we can study how neurons communicate each other.

 Figure 4

Figure 4 shows the excitatory synapse model, using a simple and minimal design, which is connected between two analog neuron circuits. The two neuron circuits are similar analog neuron model what we used in earlier experiments.  Such a synapse isolates the electrical properties of the post-synaptic site from the pre-synaptic one.    To this end, the synapse model is wired up between neurons via a high input resistance, Rin and a low output resistance, Rout, (variable according to the situation).   The rest synaptic circuit includes a serial connection, or so-called Darlington connection, of TA and TB which regulates the collector-emitter current of TC. 

Darlington pair is an amplifier which basically consists two bipolar junction transistor connected in series with their collectors connected together and emitter of one connected to the base of the other. So the circuit has an extremely high current gain and input impedance. To turn on two transistors TA and TB at the same time there must be 0.7V across base-emitter junctions of both the transistors. This is higher than that of a single transistor. 

The current injected in the presynaptic cell flows through the synapse circuit into post synaptic cell. This makes current to flow from Esyn to postsynaptic cell and depolarizes it. If the depolarization exceeds threshold, voltage-gated ion channels in the postsynaptic cell will open and generate an action potential.

 Now let us see what happens when more than one synapse is connected between two neurons. Circuit in Figure 5 shows two synapses between two neurons. Here we keep Esyn of synapse 1 constant and Esyn of second synapse dynamic.

We can analyze the circuit in two cases.

    Case 1: Esyn1=Esyn2=1V

Here with the same input signal conditions as above we get EPSP signal at the second neuron output. But the amplitude of EPSP signal is increased due to the effects of second synapse.

     Case 2: Esyn1=1V & Esyn2=3V

Here  also we apply the same input signal but with Esyn2=3V. This higher Esyn value makes more current  flow from synapse to second neuron which leads the second neuron to spike and generates action potential.

Figure 5

In this RT experiments, one can generates both EPSP and action potential by varying Esyn2.