SNN Risk Intelligence for Interpreting Dynamic Situations Through Temporal Spike Patterns
An SNN (Spiking Neural Network) is a neuromorphic AI model that maintains neuron membrane potential and firing timing as states while processing the order and changing patterns of inputs over time.
Robowink’s SNN Risk Intelligence research converts spatiotemporal events refined by DVS Event Vision into spike sequences and focuses on interpreting dynamic patterns and changing situations using neuron states accumulated over time.
SNN neurons accumulate incoming spikes over time, generate an output spike when the membrane potential exceeds a threshold, and then update their state.
Receives input events as time-ordered spikes and passes them to the neuron state.
Maintains the previous membrane potential while accumulating new inputs over time.
Generates an output spike at the moment the membrane potential exceeds the threshold.
Resets or decays the state after firing so inference can continue at the next time step.
Even with the same Event Vision data, temporal-information preservation and model complexity vary by spike representation, so we compare encoding methods according to the intended purpose.
Directly maps event location and occurrence time to spike inputs to preserve as much asynchrony as possible.
(x, y, t, p) → spikeRepresents feature magnitude or degree of change through how early the first spike occurs.
stronger → earlier spikeRepresents input intensity using the frequency of spikes occurring within a defined time window.
feature ∝ spike countDivides the time axis into short windows and organizes event density and sequence into a stable input tensor.
T → [t1, t2, ... tn]An SNN uses both the current input and the neuron state from the previous time step to distinguish brief changes from persistent motion patterns.
Time-ordered input spikes
Input importance reflected by weights
Accumulation of membrane-potential state
Formation of continuous change patterns
Extraction of temporal feature vectors
Situation representation and classification input
Rather than repeating the control-decision logic described in the Business section, this research focuses on which temporal patterns can be represented and distinguished reliably inside the spiking network.
Distinguishes brief noise from genuine motion patterns that persist over time.
Learns correlations among spikes occurring simultaneously or sequentially across multiple spatial regions.
Represents scene trends and transitions through neuron-state changes over time.
Improves result stability by using inference consistency over a time interval rather than relying on a single instantaneous output.
We make non-differentiable spike firing over time trainable and jointly optimize model structure and weight representation for deployment on edge hardware.
This research direction uses an approximate derivative for the non-differentiable spike-firing function to enable time-domain backpropagation training.
As a mid- to long-term research topic, we are examining local weight updates based on spike-timing differences and the potential for field-adaptive learning.
Evaluation considers not only accuracy, but also temporal stability, spike sparsity, computational latency, and model lightweighting.
SNN Risk Intelligence is an AI research layer that converts event sequences into temporal meaning; it is neither sensor preprocessing itself nor the final control function.
Provides refined event streams and spatiotemporal representations as SNN inputs.
Generates inference features through spike encoding, neuron states, and temporal-pattern learning.
Deploys the trained SNN model to edge hardware and handles interfaces and real-time execution.
We welcome joint research and PoC opportunities for Event-to-Spike processing, SNN training, temporal-feature extraction, and edge deployment.