#artificialneuralnetworks — Public Fediverse posts on home.social

💧🌏 Greg Cocks @[email protected] · 2025-11-25 · 03:21 UTC

Groundwater Level Forecasting In Response To Climate Change Scenarios In Southwestern Saskatchewan Using Wavelet Decomposition And Artificial Neural Networks
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https://doi.org/10.1016/j.gsd.2025.101550 <-- shared paper
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#climatechange #machinelearning #artificialneuralnetworks #sustainablegroundwatermanagement #waveletdecomposition #water #hydrology #groundwater #extremeweather #drought #rainfall #precipitation #AI #forecast #waterresources #pumping #extraction #network #historicaldata #model #modeling #ANN #groundwaterlevel #forecasting #recharge #saskatchewan #canada #watermanagement #strategy #aquifer #GWLs #wells #modelresults #results #sustainability #climate

#climatechange #machinelearning #artificialneuralnetworks #sustainablegroundwatermanagement #waveletdecomposition #water

Harald Sack @[email protected] · 2025-07-24 · 13:00 UTC

This week's ISE 2025 lecture was focussed on artificial neural networks. In particular, we were discussing how to get rid of manual feature engineering and doing representation learning from raw data with convolutional neural networks.

#AI #ArtificialNeuralNetworks #cnn #deeplearning #machinelearning #ise2025 @fizise @fiz_karlsruhe @sarahjamielewis @enorouzi

#ai #artificialneuralnetworks #cnn #deeplearning #machinelearning #ise2025

Laurent Perrinet @[email protected] · 2025-04-30 · 16:29 UTC

The remarkable energy efficiency of the Human brain: One #Spike Every 6 Seconds !

In the groundbreaking paper "The Cost of Cortical Computation" published in 2003 in Current Biology, neuroscientist Peter Lennie reached a stunning conclusion about neural activity in the human brain: the average firing rate of cortical neurons is approximately 0.16 Hz—equivalent to just one spike every 6 seconds.

This finding challenges conventional assumptions about neural activity and reveals the extraordinary energy efficiency of the brain's computational strategy. Unconventional? Ask a LLM about it, and it will rather point to a baseline frequency between 0.1Hz and 10Hz. Pretty high and vague, right? But how did Lennie arrive at this remarkable figure?

The Calculation Behind the 0.16 Hz Baseline Rate

Lennie's analysis combines several critical factors:

1. Energy Constraints Analysis

Starting with the brain's known energy consumption (approximately 20% of the body's entire energy budget despite being only 2% of body weight), Lennie worked backward to determine how many action potentials this energy could reasonably support.

2. Precise Metabolic Costs

His calculations incorporated detailed metabolic requirements:

Each action potential consumes approximately 3.84 × 109 ATP molecules
The human brain uses about 5.7 × 1021 ATP molecules daily

3. Neural Architecture

The analysis factored in essential neuroanatomical data:

The human cerebral cortex contains roughly 1010 neurons
Each neuron forms approximately 104 synaptic connections

4. Metabolic Distribution

Using cerebral glucose utilization measurements from PET studies, Lennie accounted for energy allocation across different neural processes:

Maintaining resting membrane potentials
Generating action potentials
Powering synaptic transmission

By synthesizing these factors and dividing the available energy budget by the number of neurons and the energy cost per spike, Lennie calculated that cortical neurons can only sustain an average firing rate of approximately 0.16 Hz while remaining within the brain's metabolic constraints.

Implications for Neural Coding

This extremely low firing rate has profound implications for our understanding of neural computation. It suggests that:

Neural coding must be remarkably sparse — information in the brain is likely represented by the activity of relatively few neurons at any given moment
Energy efficiency has shaped brain evolution — metabolic constraints have driven the development of computational strategies that maximize information processing while minimizing energy use
Low baseline rates enable selective amplification — this sparse background activity creates a context where meaningful signals can be effectively amplified

The brain's solution to energy constraints reveals an elegant approach to computation: doing more with less through strategic sparsity rather than constant activity.

This perspective on neural efficiency continues to influence our understanding of brain function and inspires energy-efficient approaches to #ArtificialNeuralNetworks and #neuromorphic computing.

#spike #artificialneuralnetworks #neuromorphic

Mathew Thomas @[email protected] · 2024-10-09 · 07:57 UTC

#GeoffreyHinton #Scientist #GodfatherofAI
Gets #NobelPrize in #Physics
For work on #artificialneuralnetworks
https://www.msn.com/en-in/money/news/godfather-of-ai-gets-nobel-prize-in-physics-shares-his-big-ai-worry/ar-AA1rW4UA?ocid=msedgntp&pc=U531&cvid=4931bd277c7b4d518e71977d7829bd1b&ei=34

#geoffreyhinton #scientist #godfatherofai #nobelprize #physics #artificialneuralnetworks

IT News @[email protected] · 2024-10-08 · 17:15 UTC

In stunning Nobel win, AI researchers Hopfield and Hinton take 2024 Physics Prize - On Tuesday, the Royal Swedish Academy of Sciences awarded the 2024 Nobel P... - https://arstechnica.com/ai/2024/10/in-stunning-nobel-win-ai-researchers-hopfield-and-hinton-take-2024-physics-prize/ #royalswedishacademyofsciences #artificialneuralnetworks #machinelearning #geoffreyhinton #neuralnetworks #johnhopfield #nobelprize #science #biz⁢ #sweden #ai

#ai #sweden #biz #science #nobelprize #johnhopfield

SubtleBlade ⚔️ @[email protected] · 2024-05-25 · 12:22 UTC

#AI [#LLMs] Is a Black Box. #Anthropic Figured Out a Way to Look Inside

What goes on in #ArtificialNeuralNetworks work is largely a mystery, even to their creators. But researchers from Anthropic have caught a glimpse.
https://www.wired.com/story/anthropic-black-box-ai-research-neurons-features/

#artificialneuralnetworks #anthropic #llms #ai

Technische Universität München @[email protected] · 2024-05-14 · 11:58 UTC

Our university welcomes two professors who were honored with a Humboldt Professorship!👏Stefanie Jegelka and Suvrit Sra will conduct research in #artificialneuralnetworks and #machinelearning methods, respectively: http://go.tum.de/658203

#AI

📷private

#artificialneuralnetworks #machinelearning #ai

Gottfried Szing @[email protected] · 2023-12-13 · 12:06 UTC

Artificial neural networks have made significant strides in simulating human brain processes. By mimicking the structure and function of the brain's neurons, these networks have enhanced the ability of machines to learn, reason, and make decisions.

#artificialneuralnetworks #neuralnetworks #technology #software #BroughtToYouByChatGPT #adventcalendar