SPIKING NEURAL NETWORKS

Find how to make your network more efficient

with third-generation Neural Networks

Neural Networks are computational model that attempts to simulate the behavior of the human brain. We are currently in the third generation of neural networks, Spiking Neural Networks.

These new types of algorithms attempt to emulate the human brain with an even greater degree of accuracy.

These intelligent methods are very ambitious. It is still unclear, even at the biological level, how the brain manages to learn, making it challenging to reproduce it artificially.

Thanks to the many years of experience in the Telco world, Net Reply is able to provide the necessary skills for the development of innovative solutions based on Neural Neural Networks.


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SCENARIO

Neural networks in telco systems can be helpful in the planning phase, where it is crucial to understand the state in which the network is or will be.

Traditional neural networks are suitable, but spiking can have an edge. The data from networks are almost always event-based, consisting of alarms, errors, and numerical readings, a time series where the time and frequency of events are essential.

Spiking networks are executed when an event is received and not continuously, so the consumption would be minimal for functioning networks since errors and alarm events should be very low.

COMPETENCES

Spiking Neural Networks use a different neuron than traditional networks and add a neuron membrane potential concept. That is, when the neuron receives an impulse, the potential on the neuron is charged by a certain amount relative to the impulse received.

If the neuron does not receive impulses for a while, the membrane's potential is discharged. Instead, suppose the neuron continues to be stimulated until the potential reaches the threshold value.

In that case, it emits an output impulse and discharges the potential to its minimum value. The membrane potential lets the network have time capabilities.

USE CASE

The applications of spiking neural networks are the same as those of traditional neural networks.

However, mainly they are suitable for image and video classification precisely because of their nature. They have been tested on a time series of measurements from a system's event collector.

The measure was of the average throughput of events over ten seconds from a northbound telco interface. And they showed excellent performance.


VALUE PROPOSITION

  • Energy Efficiency

  • High accuracy in time series datasets

  • Shorter execution time