computer-science

Neuromorphic Computing

Brain-inspired computing architectures — spiking neural networks, spike-timing-dependent plasticity, memristive crossbar arrays, reservoir computing, and event-driven neuromorphic vision systems.

neuromorphicspiking neural networkSTDPmemristorreservoir computingneuromorphic visionbrain-inspired computing

Neuromorphic computing reimagines computation by mimicking the architecture of biological nervous systems. Instead of the clock-driven, von Neumann paradigm, neuromorphic chips use massively parallel networks of artificial neurons that communicate through discrete spikes, achieving remarkable energy efficiency for tasks like pattern recognition and sensory processing.

These simulations let you fire spiking neurons with Hodgkin–Huxley dynamics, observe spike-timing-dependent plasticity reshape synaptic weights, program memristor crossbar arrays for analog matrix operations, explore reservoir computing with recurrent dynamics, and process visual events through neuromorphic retinas — all with interactive, real-time visualizations grounded in computational neuroscience.

5 interactive simulations

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Memristor Crossbar Array

Simulate a memristive crossbar array — explore how conductance states, voltage pulses, and array size enable analog matrix-vector multiplication for neural networks
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Neuromorphic Vision (Event Camera)

Simulate a neuromorphic event camera — explore how contrast threshold, pixel bandwidth, and latency enable microsecond-resolution motion capture with minimal data
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Reservoir Computing (Echo State Network)

Simulate a reservoir computer — explore how recurrent network size, spectral radius, and input scaling transform temporal signals into linearly separable representations
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Spiking Neuron Model (LIF)

Simulate a leaky integrate-and-fire neuron — explore how input current, membrane time constant, and threshold voltage shape spike trains in neuromorphic hardware
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Spike-Timing-Dependent Plasticity (STDP)

Simulate STDP learning — explore how the relative timing of pre- and post-synaptic spikes strengthens or weakens synaptic connections