February 5, 2025
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A brain-inspired AI expertise boosts effectivity and reduces power consumption
Researchers at FORTH have developed a brand new kind of synthetic neural community (ANN) that includes options of organic dendrites. This modern design permits for correct and strong picture recognition whereas utilizing considerably fewer parameters, paving the best way for extra compact and energy-efficient AI programs.
Synthetic Intelligence (AI) performs a vital position in driving innovation and bettering effectivity throughout varied industries, providing smarter options to complicated issues and enhancing our day by day lives. Nevertheless, present AI programs are enormous, comprising millions-to-billions of parameters, thus consuming large quantities of power, which limits their widespread use.
By integrating neuro-inspired options into AI, we are able to create smaller and smarter programs that mimic how our brains course of data, bettering their effectiveness in recognizing patterns and making choices. This results in extra environment friendly and efficient AI functions.
Dendrites are the branched extensions of nerve cells that resemble tree branches. Their principal perform is to obtain data from different neurons and transmit it to the cell physique. For a few years, the position of dendrites in data processing was unclear, however latest research have revealed that they’ll carry out complicated calculations independently of the primary neuron. Moreover, dendrites are important for the mind's plasticity, which is its skill to adapt to altering environments.
In a latest article revealed within the journal Nature Communications, Dr. Panayiota Poirazi's crew on the Institute of Molecular Biology and Biotechnology (IMBB) of FORTH proposed a novel structure for synthetic neurons that includes completely different options of organic dendrites, and examined it in varied picture recognition situations.
The findings present that these dendritic ANNs are extra proof against overfitting and might match or exceed the efficiency of conventional ANNs whereas utilizing a lot fewer sources, specifically trainable parameters and studying steps.
This enchancment arises from a singular studying strategy, whereby a number of nodes within the community contribute to the encoding of various classes. That is opposite to conventional ANNs, whereby most nodes are category-specific. General, the analysis means that incorporating dendritic options could make ANNs smarter and extra environment friendly.
This work was led by Dr. Chavlis, a postdoctoral researcher at IMBB-FORTH, below the supervision of Dr. Poirazi.
Extra data: Spyridon Chavlis et al, Dendrites endow synthetic neural networks with correct, strong and parameter-efficient studying, Nature Communications (2025). DOI: 10.1038/s41467-025-56297-9
Journal data: Nature Communications Offered by Basis for Analysis and Know-how – Hellas Quotation: A brain-inspired AI expertise boosts effectivity and reduces power consumption (2025, February 5) retrieved 5 February 2025 from https://techxplore.com/information/2025-02-brain-ai-technology-boosts-efficiency.html This doc is topic to copyright. Other than any honest dealing for the aim of personal examine or analysis, no half could also be reproduced with out the written permission. The content material is offered for data functions solely.
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