Spiking neural network

The insect is controlled by a spiking neural network to find a target in an unknown terrain.

Spiking neural networks (SNNs) are artificial neural networks (ANN) that more closely mimic natural neural networks.[1] These models leverage timing of discrete spikes as the main information carrier.[2]

In addition to neuronal and synaptic state, SNNs incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not transmit information at each propagation cycle (as it happens with typical multi-layer perceptron networks), but rather transmit information only when a membrane potential—an intrinsic quality of the neuron related to its membrane electrical charge—reaches a specific value, called the threshold. When the membrane potential reaches the threshold, the neuron fires, and generates a signal that travels to other neurons which, in turn, increase or decrease their potentials in response to this signal. A neuron model that fires at the moment of threshold crossing is also called a spiking neuron model.[3]

Although it was previously believed that the brain encoded information through spike rates, which can be considered as the analogue variable output of a traditional ANN,[4] research in the field of neurobiology has indicated that high speed processing cannot solely be performed through a rate based scheme. For example humans can perform an image recognition task at rate requiring no more than 10ms of processing time per neuron through the successive layers (going from the retina to the temporal lobe). This time window is too short for a rate based encoding. The precise spike timings in a small set of spiking neurons also has a higher information coding capacity compared with a rate based approach.[5]

The most prominent spiking neuron model is the leaky integrate-and-fire model.[6] In the integrate-and-fire model, the momentary activation level (modeled as a differential equation) is normally considered to be the neuron's state, with incoming spikes pushing this value higher or lower, until the state eventually either decays or—if the firing threshold is reached—the neuron fires. After firing, the state variable is reset to a lower value.

Various decoding methods exist for interpreting the outgoing spike train as a real-value number, relying on either the frequency of spikes (rate-code), the time-to-first-spike after stimulation, or the interval between spikes.

  1. ^ Maass W (1997). "Networks of spiking neurons: The third generation of neural network models". Neural Networks. 10 (9): 1659–1671. doi:10.1016/S0893-6080(97)00011-7. ISSN 0893-6080.
  2. ^ Auge, Daniel; Hille, Julian; Mueller, Etienne; Knoll, Alois (2021-12-01). "A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks". Neural Processing Letters. 53 (6): 4693–4710. doi:10.1007/s11063-021-10562-2. ISSN 1573-773X.
  3. ^ Gerstner W, Kistler WM (2002). Spiking neuron models : single neurons, populations, plasticity. Cambridge, U.K.: Cambridge University Press. ISBN 0-511-07817-X. OCLC 57417395.
  4. ^ Wang, Xiangwen; Lin, Xianghong; Dang, Xiaochao (2020-05-01). "Supervised learning in spiking neural networks: A review of algorithms and evaluations". Neural Networks. 125: 258–280. doi:10.1016/j.neunet.2020.02.011. ISSN 0893-6080. PMID 32146356. S2CID 212638634.
  5. ^ Taherkhani, Aboozar; Belatreche, Ammar; Li, Yuhua; Cosma, Georgina; Maguire, Liam P.; McGinnity, T. M. (2020-02-01). "A review of learning in biologically plausible spiking neural networks". Neural Networks. 122: 253–272. doi:10.1016/j.neunet.2019.09.036. ISSN 0893-6080. PMID 31726331. S2CID 207904985.
  6. ^ Ganguly, Chittotosh; Bezugam, Sai Sukruth; Abs, Elisabeth; Payvand, Melika; Dey, Sounak; Suri, Manan (2024-02-01). "Spike frequency adaptation: bridging neural models and neuromorphic applications". Communications Engineering. 3 (1): 22. doi:10.1038/s44172-024-00165-9. ISSN 2731-3395. PMC 11053160.