Reinforcement Learning and Synaptic Weights
Synaptic efficacy describes how strongly neurotransmitter released at a synapse influences the postsynaptic neuron.
Why Release Is Not Enough
When a synapse releases neurotransmitter, an important question remains: how strongly does that release influence the receiving, or postsynaptic, neuron? Synaptic efficacy describes this strength of influence. A synapse can therefore be discussed not only in terms of whether neurotransmitter is released, but also in terms of how effectively that release affects the postsynaptic neuron.
Synaptic efficacy is the current functional strength of a synapse: the strength with which released neurotransmitter influences the postsynaptic neuron.
The diagram uses a qualitative comparison. The released neurotransmitter is held constant in the illustration, while the efficacy differs. The point is that synaptic efficacy describes the effectiveness of the influence, not merely the occurrence of neurotransmitter release.
Activity and Chemical Context
Synaptic plasticity is the ability of synaptic efficacy to change. Those changes can depend on activity in the presynaptic neuron, activity in the postsynaptic neuron, and sometimes a neuromodulator. Presynaptic and postsynaptic activity can therefore contribute to changing the synapse's efficacy, while efficacy names the resulting strength of influence.
Three Levels of Description
| Term | What it describes | Role in the explanation |
|---|---|---|
| Synaptic efficacy | How strongly released neurotransmitter influences the postsynaptic neuron | The current strength of synaptic influence |
| Synaptic plasticity | The ability of synaptic efficacy to change | The changeable property or biological process |
| Neuromodulation | The participation of a neuromodulator in synaptic influence or changes to it | A chemical influence that can affect how efficacy changes |
Naming the Three Parts
A synapse currently has a particular strength of influence. Activity and a neuromodulator contribute to a process that changes that strength. Identify the role of each concept.
Current influence: The strength with which released neurotransmitter influences the postsynaptic neuron is synaptic efficacy.
Ability to change: The ability of that efficacy to change is synaptic plasticity.
Chemical participation: The neuromodulator is an influence that can sometimes participate in the synaptic influence or in changes to efficacy.
Efficacy names the current strength, plasticity names the ability to change, and neuromodulation names the possible chemical influence on that system.
From Efficacy to Algorithmic Weight
Reinforcement-learning algorithms adjust parameters or weights. The source material relates those adjustable algorithmic weights to synaptic efficacies. In this analogy, a changing weight corresponds to a changing synaptic efficacy, while synaptic plasticity corresponds to the biological process that permits the change.
This is a relationship between a biological process and an algorithmic idea, not a claim that a synapse is literally a software variable. The useful correspondence is structural: an adjustable algorithmic weight represents how strongly a connection contributes, and a changing synaptic efficacy represents a biological change in synaptic influence.
A Synapse Updating Its Influence
Two Connections, Two Efficacies
Imagine two synapses releasing neurotransmitter toward postsynaptic neurons. Use the idea of synaptic efficacy to compare them.
Start with release: Both synapses release neurotransmitter. Release alone does not tell us how strongly the receiving neuron will be influenced.
Compare efficacy: Suppose the first synapse has lower efficacy and the second has higher efficacy. The second synapse has the stronger described influence on its postsynaptic neuron.
Introduce change: If activity and, sometimes, a neuromodulator contribute to a change in efficacy, the functional strength of a synapse can change. That change is an instance of synaptic plasticity.
Translate to learning language: In the reinforcement-learning analogy, the changed efficacy corresponds to an adjusted algorithmic weight.
The important distinction is between neurotransmitter release and the efficacy of that release. Plasticity permits efficacy to change, and the changed efficacy corresponds to a changed learning-algorithm weight.
This example does not assign numerical values to the synapses. The source supports a qualitative comparison: two synapses can release neurotransmitter, yet the effectiveness of that release is the relevant measure when describing synaptic efficacy.
Mistakes in Translating the Concepts
Treating neurotransmitter release and synaptic efficacy as the same thing.
Synaptic efficacy describes how strongly the released neurotransmitter influences the postsynaptic neuron. Release is not the complete description of influence.
Fix:
Ask how effectively the release influences the postsynaptic neuron, not only whether release occurs.Using synaptic plasticity as if it meant the current synaptic strength.
Plasticity describes the ability of efficacy to change, while efficacy describes the strength of influence itself.
Fix:
Use efficacy for the current influence and plasticity for the ability or process of changing it.Treating neuromodulation as identical to the weight.
A neuromodulator can sometimes participate in synaptic influence or in changes to efficacy. It is not the same concept as the efficacy or its algorithmic counterpart.
Fix:
Describe the neuromodulator as a possible chemical influence on efficacy or plasticity.Claiming that the biological and algorithmic descriptions are literally identical.
The source presents a correspondence between biological synaptic efficacies and adjustable algorithmic weights.
Fix:
State the relationship as an analogy or correspondence: changing efficacy maps to changing an adjustable weight.
When explaining a learning connection, name the level you mean. Say whether you are describing the current strength of synaptic influence, the ability of that strength to change, the possible chemical influence on the change, or the algorithmic weight that corresponds to the synaptic efficacy.
Check Your Understanding
A learning model contains an adjustable weight corresponding to a synaptic efficacy. Explain, in four parts, what each phrase refers to: the current weight, a change to the weight, the biological ability that permits the change, and a neuromodulator that may influence the change.
Hints
- Match the current influence with synaptic efficacy.
- Match the ability of efficacy to change with synaptic plasticity.
- Treat the neuromodulator as a possible influence on synaptic influence or on plasticity.
- Describe the weight as the algorithmic counterpart of synaptic efficacy.
What do you think happens?
If a synapse's efficacy changes while the released neurotransmitter is still being discussed as the same kind of event, what has changed: the occurrence of release, or the strength of its influence on the postsynaptic neuron?
Reveal answer
Answer: The strength of influence on the postsynaptic neuron
Synaptic efficacy describes how strongly released neurotransmitter influences the postsynaptic neuron. A change in efficacy is therefore a change in described synaptic influence.
Key Takeaways
- Synaptic efficacy describes how strongly released neurotransmitter influences a postsynaptic neuron.
- Synaptic plasticity describes the ability of synaptic efficacy to change.
- Presynaptic activity, postsynaptic activity, and sometimes a neuromodulator can contribute to changes in efficacy.
- In reinforcement-learning terms, synaptic efficacies correspond to adjustable algorithmic weights.
- A changing weight corresponds to changing synaptic efficacy, while plasticity describes the biological ability or process that permits the change.
Key Takeaways
- Synaptic efficacy is the strength of neurotransmitter's influence on a postsynaptic neuron.
- Synaptic plasticity is the ability of that efficacy to change.
- Presynaptic activity, postsynaptic activity, and neuromodulators can contribute to changes in efficacy.
- Adjustable algorithmic weights correspond to synaptic efficacies in the reinforcement-learning analogy.
- Neuromodulation, plasticity, efficacy, and algorithmic weight describe related but distinct ideas.