Question

In modeling synaptic weights using the discrete Hebb's update rule, what limitation does this rule present regarding synaptic weights?

A) The synaptic weights are unbounded and can grow infinitely.
B) Synaptic weights reach a threshold and plateau, causing network disruption.
C) Synaptic weights decay rapidly with neuronal inactivity.
D) Synaptic weights fluctuate chaotically, hindering network stability.

Answer 1

Using the discrete Hebb's update rule presents a limitation where synaptic weights can grow infinitely, not reflecting the biological reality of synaptic plasticity such as LTP and LTD. Hence, the correct answer is option (A).

Step-by-step explanation:

In modeling synaptic weights using the discrete Hebb's update rule, the limitation that this rule presents regarding synaptic weights is that the synaptic weights are unbounded and can grow infinitely. This unbounded growth leads to an unrealistic model of synaptic activity, as real biological systems have mechanisms such as homeostatic plasticity to prevent runaway effects like infinite strengthening of synaptic connections.

Such infinity in synaptic strength does not reflect the true adaptability and modulation of synaptic efficacy seen in processes like long-term potentiation (LTP) and long-term depression (LTD), which are crucial for learning and memory. Therefore, option (A) would be the correct answer.