Editing Neuromorphic (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Neuromorphic computing''' — Computing architectures inspired by the structure and function of biological brains.
* '''Spiking Neural Network (SNN)''' — A neural network model where neurons communicate via discrete spikes (events) rather than continuous activations.
* '''Spike''' — A discrete event (action potential analog) fired by a neuron when its membrane potential exceeds a threshold.
* '''Leaky Integrate-and-Fire (LIF)''' — The simplest neuron model: integrates incoming spikes, leaks charge over time, fires when threshold is reached.
* '''Temporal coding''' — Encoding information in the timing of spikes, not just their rate.
* '''Rate coding''' — Encoding information in the average spike frequency over a time window.
* '''STDP (Spike-Timing Dependent Plasticity)''' — A biologically plausible learning rule: synapses strengthen when the pre-synaptic neuron fires just before the post-synaptic neuron, and weaken otherwise.
* '''Intel Loihi''' — Intel's neuromorphic research chip supporting SNNs with on-chip learning.
* '''IBM TrueNorth''' — IBM's neuromorphic chip with 4096 cores, 1M programmable neurons, 256M synapses at 70mW.
* '''Event-driven computation''' — Processing only when an event (spike) occurs, not on a fixed clock cycle; enables extreme power efficiency.
* '''Synaptic plasticity''' — The ability of synaptic connections to strengthen or weaken over time; the basis of learning in biological brains.
* '''Memristor''' — A resistor with memory — resistance depends on past current — enabling synaptic weight storage in hardware.
* '''In-memory computing''' — Performing computation directly in memory arrays, eliminating the von Neumann memory bottleneck.
* '''Sparse activation''' — Only a small fraction of neurons fire at any given time in biological systems; SNNs exploit this for efficiency.
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