The Space Between Two Neurons Is Called The

So You Need to Know About the Space Between Two Neurons

The gap is called a synapse. It is not empty space, it is a highly regulated biochemical junction that determines whether a signal passes from one neuron to the next or gets blocked entirely. People learn the definition early and then never really understand what happens there because textbooks flatten it into a diagram. The reality is messier. A synapse has three main parts. The presynaptic terminal on the sending neuron holds vesicles packed with neurotransmitters. There is the synaptic cleft, which is roughly twenty nanometers wide in a chemical synapse. And on the receiving side you have the postsynaptic membrane studded with receptors. An action potential arrives at the terminal, voltage-gated calcium channels open, vesicles fuse with the membrane, and neurotransmitter spills into the cleft. It binds to receptors on the other side and either excites or inhibits the next cell. That is the basic loop. But the depth of what goes on in that gap is where most people get lost.

The Space Between Two Neurons Is Called The Synapse

I used to work in a lab studying synaptic transmission, and one of the first things you learn is that the size of that cleft matters more than anyone outside the field realizes. If you are doing electrophysiology recordings or running simulations, twenty nanometers is not just a number. It controls how fast neurotransmitter concentrations build up, how much back-diffusion happens, and whether you are looking at a sharp, transient signal or something smeared out over time. Here is an edge case that almost cost us a publication. We were measuring miniature excitatory postsynaptic currents at particular hippocampal synapses, and the amplitudes looked inconsistent across trials. The first instinct was that the patch pipette was failing or the seals were bad. They were fine. What actually happened was that we had picked a synapse where the cleft geometry was slightly distorted, likely from tissue processing, and that was altering the neurotransmitter concentration profile enough to shift the apparent quantal size. The workaround was straightforward once we knew what to look for: we switched from analyzing individual mEPSCs to looking at paired-pulse ratios across multiple trials, and we also ran a simple diffusion simulation using known cleft dimensions to confirm the effect was structural rather than biological. It saved us from writing off what was actually a legitimate finding. There are two things beginners routinely get wrong about synapses that I want to flag here. First, the idea that a synapse is purely excitatory or purely inhibitory. That is mostly true at the level of a single synapse type, but a single neuron can release more than one neurotransmitter, and even a GABAergic synapse can be excitatory in early development when chloride gradients are different. Second, people think the synapse is just a relay point, a passive wire between cells. It is not. It is a computational unit. Short-term facilitation, short-term depression, presynaptic inhibition, neuromodulation — all of these shape the output in ways that a simple "fire or don't fire" model completely misses.

Electrical synapses exist too. They use gap junctions instead of neurotransmitter, and the signal passes directly through connexin channels from one cell to the next. They are faster, nearly instantaneous, and bidirectional. You find them in circuits where timing is critical, like in the retina or certain oscillator networks in the brainstem. Chemical synapses dominate the cortex, but electrical ones are not a curiosity, they are a specialized tool for specific jobs. If you are trying to study or model synapses practically, start by picking a preparation and sticking with it long enough to understand its quirks. Hippocampal slices are standard for a reason, but they introduce artifacts from slicing. Cultured neurons are cleaner but less physiologically relevant. In vivo recordings are the real thing but introduce noise and variability that can overwhelm subtle effects. There is no universal best choice. The tradeoffs are real. One technical note that tends to get glossed over: the recovery time between stimuli matters enormously for synaptic strength. If you stimulate too rapidly, vesicle pools deplete and you get depression. If you wait too long, you lose the facilitation that builds response strength. A common mistake in both experimental and computational work is using a single inter-stimulus interval and generalizing from it. Synapses adapt. Their behavior changes depending on recent history. Design your protocol around that fact or your results will be misleading.

Neurotransmitter reuptake is another area where the synapse is not just a gap but an active participant. Transporters on nearby astrocytes and on the presynaptic terminal itself clear neurotransmitter from the cleft. Without that, signals would persist far longer than they should. Blocking reuptake with drugs like SSRIs is one of the most common pharmacological interventions in neuroscience, and its effects are entirely rooted in what happens in that small space between cells. The bottom line is that the synapse is where the actual computation happens. The neuron is just the wiring. If you want to understand neural function, stop treating the synapse as a connector and start treating it as the engine.