Conductance-Based Synapses

Spikes open synaptic channels, which change the voltage of an attached neuron.

Abstract

An arriving spike opens synaptic channels. Their conductance then decays, while current through them changes the receiving neuron's voltage. We use a simple animation to explore how synaptic weight, decay time and reversal potential shape this response, then combine excitatory and inhibitory inputs.

Introduction

A spike causes neurotransmitter release. The transmitter crosses the synaptic cleft and opens channels embedded in the receiving neuron’s membrane. We represent their combined conductance by gsyng_{\mathrm{syn}}, measured in nanosiemens (nS). Each arrival at time tjt_j adds the synaptic weight weventw_{\mathrm{event}}; between arrivals, conductance decays with time constant τsyn\tau_{\mathrm{syn}}. Time tt and τsyn\tau_{\mathrm{syn}} are in milliseconds (ms), and jj labels spikes. The Dirac impulse δ(t−tj)\delta(t-t_j) has unit area and units of inverse milliseconds; each impulse adds weventw_{\mathrm{event}} nS at arrival.[1]

dgsyndt=−gsynτsyn+wevent∑jδ(t−tj).(1)\frac{dg_{\mathrm{syn}}}{dt}=-\frac{g_{\mathrm{syn}}}{\tau_{\mathrm{syn}}}+w_{\mathrm{event}}\sum_j\delta(t-t_j). \tag{1}

Open channels carry current according to the difference between their reversal potential EsynE_{\mathrm{syn}} and the neuron’s voltage VmV_m, both in millivolts (mV):

Isyn=gsyn(Esyn−Vm).(2)I_{\mathrm{syn}}=g_{\mathrm{syn}}(E_{\mathrm{syn}}-V_m). \tag{2}

Here IsynI_{\mathrm{syn}} is in picoamperes (pA), positive when it raises voltage. Above VmV_m, reversal potential makes the current depolarising; below it, hyperpolarising. At equality, net current is zero even with open channels. This sign convention is opposite to the outward-positive convention in reference [1].

Between output spikes, the receiving neuron also leaks towards its resting voltage ELE_L:

CmdVmdt=gL(EL−Vm)+gsyn(Esyn−Vm).(3)C_m\frac{dV_m}{dt}=g_L(E_L-V_m)+g_{\mathrm{syn}}(E_{\mathrm{syn}}-V_m). \tag{3}

CmC_m is membrane capacitance, set to 100 picofarads (pF); gLg_L is leak conductance, set to 10 nS; and EL=−65E_L=-65 mV. A leaky integrate-and-fire rule adds output spikes: reaching the threshold Vth=−50V_{\mathrm{th}}=-50 mV emits a spike and resets voltage to Vreset=−65V_{\mathrm{reset}}=-65 mV. There is no refractory interval.[2]

Vm≥Vth  ⇒  spike,Vm←Vreset.(4)V_m\geq V_{\mathrm{th}}\;\Rightarrow\;\text{spike},\quad V_m\leftarrow V_{\mathrm{reset}}. \tag{4}

Explore the synapse

Increase Weight weventw_{\mathrm{event}} to open more channels, or Decay τsyn\tau_{\mathrm{syn}} to keep them open longer. Move Reversal EsynE_{\mathrm{syn}} above or below −65 mV to raise or lower the neuron’s voltage. At −65 mV, channels still open but the neuron stays at rest. Reversal potential changes the voltage response, not the conductance trace. The default weight of 8 nS produces output spikes; the voltage trace reaches threshold and resets.

Figure 1. Spikes open the channel in A. B shows its conductance; C shows the neuron's voltage. A wider opening means greater conductance. At threshold, the neuron fires and resets. The blue dashed line marks reversal potential; an arrow indicates a value outside the graph.

Excitatory and inhibitory inputs

Two presynaptic neurons now feed the receiving neuron: E supplies excitation and I supplies inhibition. Each has its own conductance, gEg_E or gIg_I, and nonnegative synaptic weight, wEw_E or wIw_I, all in nS. Their conductances obey the same decay-and-arrival rule:

dgPdt=−gPτP+wP∑jδ(t−tjP).(5)\frac{dg_P}{dt}=-\frac{g_P}{\tau_P}+w_P\sum_j\delta(t-t_j^P). \tag{5}

P∈{E,I}P\in\{E,I\} labels the input, tjPt_j^P its spike arrival times, and τP\tau_P its decay time in ms. Here τE=12\tau_E=12 ms and τI=20\tau_I=20 ms. Both currents act on the same membrane voltage:

CmdVmdt=gL(EL−Vm)+gE(EE−Vm)+gI(EI−Vm).(6)C_m\frac{dV_m}{dt}=g_L(E_L-V_m)+g_E(E_E-V_m)+g_I(E_I-V_m). \tag{6}

EEE_E and EIE_I are the two reversal potentials, adjustable in Figure 2 and initially set to 0 and −75 mV; capacitance, leak and rest retain their values from equation (3). These are illustrative settings. The two channel symbols represent two distinct conductance components, each combining the physical channels activated by its input.[1]

Increase Weight wEw_E to strengthen excitation, or Weight wIw_I to strengthen inhibition. Set either to zero to remove its current. At its default reversal, the inhibitory conductance pulls voltage towards −75 mV and can suppress output spikes. Inhibition therefore uses a positive conductance with a different reversal potential, rather than a negative weight. The default weights are wE=8w_E=8 nS and wI=4w_I=4 nS, which allow the receiving neuron to fire. Its threshold and reset follow equation (4). Changing EIE_I can change the polarity of the I-pathway current: it becomes depolarising when EI>VmE_I>V_m.

Figure 2. Excitatory (blue) and inhibitory (rose) spikes feed one neuron. A shows the inputs and channels, B their conductances, and C the neuron's voltage. Rose pulses show output spikes. Dashed lines mark reversal potentials; arrows indicate values outside the graph.

Parameters

ParameterIllustrative setting
Neurons
Membrane capacitance100 pF
Leak conductance10 nS
Rest / reset / threshold-65 / -65 / -50 mV
Passive time constant (derived)10 ms
Refractory intervalNone
Synapses and weights
Single-input initial weight / decay / reversal8 nS / 12 ms / 0 mV
Dual E / I initial weights8 / 4 nS
Dual E / I decay times12 / 20 ms
Dual E / I initial reversals0 / −75 mV
Input and simulation
Timestep / schematic delay0.25 / 8 ms
Prescribed single-input spike interval30 ms
Membrane updateExact passive update with conductances held constant during the step
Conductance updateExponential decay and event increments
Table A1.

These are browser-model choices, not fitted values or run evidence. Membrane and timing constants are imported from the same script as the animation; slider defaults are the authored initial settings. Sliders change the live state. The passive time constant is capacitance divided by leak conductance.

References

  1. Wulfram Gerstner, Werner M. Kistler, Richard Naud and Liam Paninski. Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition. Cambridge University Press, 2014. Chapter 3.1, “Synapses”.

  2. Wulfram Gerstner, Werner M. Kistler, Richard Naud and Liam Paninski. Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition. Cambridge University Press, 2014. Chapter 1.3, “Integrate-And-Fire Models”.