Question 1

What is neuromorphic computing in one sentence?

Accepted Answer

It is the design of chips whose circuits imitate how biological neurons and synapses work — communicating with brief electrical spikes and storing memory right where computation happens — so that intelligent processing can run on a tiny energy budget.

Question 2

How is a spiking neural network different from a normal neural network?

Accepted Answer

A conventional artificial network passes continuous numbers between layers on every forward pass. A spiking network passes discrete events in time: a neuron accumulates incoming charge and only sends a pulse when it crosses a threshold. Information is carried by which neurons fire and exactly when, so most of the network is silent — and silence costs almost no energy.

Question 3

Will neuromorphic chips replace CPUs and GPUs?

Accepted Answer

No. They are complementary. Exact arithmetic, databases and large language model training remain far better suited to classical silicon. Neuromorphic hardware targets sparse, event-driven, always-on sensory workloads where power is the binding constraint.

Question 4

What exactly is a memristor?

Accepted Answer

A memristor is a two-terminal device whose resistance depends on the history of charge that has flowed through it. That makes it a natural analog synapse: the resistance is the weight, it persists without power, and applying a voltage across a crossbar of them performs a whole matrix multiplication in one physical step using Ohm's and Kirchhoff's laws.

Question 5

What is STDP?

Accepted Answer

Spike-timing-dependent plasticity is a local learning rule. If a presynaptic neuron fires just before the postsynaptic one, the connection is strengthened; if it fires just after, the connection is weakened. Because the rule only needs information available at that synapse, it can run on-chip in real time without a global training pass.

Question 6

Can I program a neuromorphic chip today?

Accepted Answer

Yes, mostly through research programmes. Intel's Loihi 2 is available to the Intel Neuromorphic Research Community and is programmed with the open-source Lava framework. SpiNNaker and BrainScaleS can be accessed through the EBRAINS platform, and simulators such as Nengo, Brian2 and snnTorch let you experiment on an ordinary laptop.

Question 7

Why do people say the brain uses only 20 watts?

Accepted Answer

An adult human brain consumes roughly 20 watts while running about 86 billion neurons. A single data-centre GPU can draw 700 watts. That gap is the motivating benchmark for the entire field, and it comes largely from sparse event-driven signalling and memory sitting next to compute.

Question 8

Is the simulation on this site a real neuron model?

Accepted Answer

It is a genuine leaky integrate-and-fire model — the standard simplified neuron used throughout computational neuroscience — running live in your browser. Membrane potential decays over time, incoming weighted input pushes it up, and a spike is emitted with a reset and refractory period when the threshold is crossed. The energy figures are illustrative order-of-magnitude estimates, not measurements from real silicon.