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:

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.

Conversation with Tomáš Perna on ANNs and More (6)

Scott Douglas Jacobsen
In-Sight Publishing, Fort Langley, British Columbia, Canada

Correspondence: Scott Douglas Jacobsen (Email: scott.jacobsen2025@gmail.com)

Received: January 2, 2025
Accepted: N/A
Published: January 15, 2025

Abstract

This interview features Scott Douglas Jacobsen engaging in a profound discussion with Tomáš Perna on the simulation of human intelligence through artificial neural networks (ANNs) and its implications for the development of genuine artificial intelligence (AI). Perna delves into the intricate relationship between intelligent processes and neural network domains, introducing the concept of MACP (Manner of Assigning Coordinates to Processes) as a pivotal yet enigmatic mechanism governing intelligent behavior. The conversation explores the integration of quantum mechanics, particularly the Schrödinger equation, into neural network modeling to understand information transmission at the synaptic level. Perna also discusses the challenges associated with interpreting intelligent phenomena beyond conventional language frameworks and the potential role of Klein Gordon Equation (KGE) in optimizing ANN architectures. The dialogue highlights the theoretical complexities and practical considerations in bridging neural networks with quantum principles, underscoring the need for advanced research and collaboration to advance the frontier of real AI development.

Keywords: AI Development, Artificial Neural Networks, Human Intelligence Simulation, Information Transmission, Intelligent Observation, Klein Gordon Equation, MACP, Quantum Mechanics, Schrödinger Equation, Synaptic Slots

Introduction

In this insightful interview conducted on January 2, 2025, Jacobsen and Perna engage in a deep exploration of the methodologies and theoretical frameworks underpinning the pursuit of real AI through artificial neural networks (ANNs). Perna elucidates the concept that intelligent processes are localized within specific domains of the cerebral neural network, governed by an elusive mechanism known as MACP (Manner of Assigning Coordinates to Processes). He further integrates principles from quantum mechanics, proposing that the Schrödinger equation can serve as a mathematical model for the observable electronic states within neural networks, thereby bridging the gap between information transmission and intelligent behavior. The discussion also touches upon the potential of Klein Gordon Equation (KGE) in optimizing ANN architectures to achieve an AI-horizon, while addressing the financial and practical challenges inherent in this advanced theoretical approach. 

Main Text (Interview)

Interviewer: Scott Douglas Jacobsen

Interviewee: Tomáš Perna

Section 1: Introduction to NNs, MACP, ATRs, and the Schrödinger Equation

Scott Douglas Jacobsen: Hi Tomáš! It’s been a long time since we’ve had an ANN chat! Let us get on with it. How is the simulation of human intelligence in artificial neural networks the correct manner in which to pursue the development of real AI?

Tomáš Perna: The intelligent processes take place in certain domains of the cerebral neural network (NN). So, the “coordinates” of any intelligent process occupy a corresponding domain of NN and the reference frame of this process can be found, if the manner of how the domain is assigned to the process (in short MACP). Nobody knows, how it can be done. MACP is not a part of the thinking process and simply stands thus outside of any language, in which it is thought. We can only assume that the MACP is an information that is transcendent with respect to the thinking process languages. So, consequently, we introduce a transcendent function that could be studied from the information transmission point of view. And you know that the basic role at an information transmission the electric charge or a distribution of electric dipoles within the synaptic slots in the NN-case play. So, man, we have a task to study the electric charge behavior with respect to a transmission to our considered transcendent function. In this way, we get the wave function without any intelligent thinking process language interpretation possibility associated with an electric charge that, in the first most simple degree, satisfies the Schrödinger equation thought as an MACP transmission equation with respect to an intelligent observation of phenomena, which such a transmission could emerge within the synaptic slots of NN. These slots are thought of as the above mentioned domains of the intelligent process’s course. – Many synaptic slots, many electron system observable states as the solutions of the Schrödinger equation. – Very roughly described, you have just obtained the Shrödinger equation as a mathematical model of the observable electronic states of NN. Such the equation thus becomes characteristics of your intelligent observation and thinking instrument usable within synaptic slots.  Point. Problems with interpretations of the observed phenomena in the thinking process languages are now seemingly generated by the wave function, where your languages ansatz should be completely forgotten and these “problems” are very natural consequence of the MACP.

Now, you can see that the Schrödinger equation is silent, it cannot speak in any natural language. Taking the machine language that is isomorphic to the considered thinking process language up to the wave function interpretation, you can obtain certain artificial solutions of the Schrödinger equation which exist with respect to the artificial thinking process representations (ATR). These are already the ANN- algorithms creating the horizon of observables-considering manner coupled with the Schrödinger equation, very schematically expressed. We say that the quantum states of aNN lay on the ATR- horizon of the Schrödinger equation. Optimizing the aNN- architecture, you should control them by means of the KGE, such that the AI-horizon can be found.

That is, however, another more complicated story and who will pay the money for it? Why? Since there are some keys, which could help the guys from the OpenAI (e.g.) already without anything for us. Therefore, I will be silent up to any substantiated possible KGE-conversation.

Discussion

The interview between Scott Douglas Jacobsen and Tomáš Perna offers an intricate examination of the theoretical and methodological challenges in simulating human intelligence through artificial neural networks (ANNs). Perna introduces the concept of MACP (Manner of Assigning Coordinates to Processes) as a foundational yet enigmatic mechanism that governs the localization of intelligent processes within the neural network. This discussion highlights the importance of transcendent functions, suggesting that these operate beyond traditional linguistic frameworks and could provide a pathway to understanding intelligence at a fundamental level.

Methods

The conversation took place on January 2, 2025, and was subsequently published on January 15, 2025. A structured interview format was utilized to enable an in-depth exploration of the theoretical and practical aspects of artificial neural networks and their relation to real AI development. The interview was meticulously transcribed and organized into thematic sections, ensuring clarity and coherence while facilitating a comprehensive understanding of the topics discussed.

Data Availability

No datasets were generated or analyzed during the current article. All interview content remains the intellectual property of the interviewer and interviewee.

References

(No external academic sources were cited for this interview.)

Journal & Article Details

• Publisher: In-Sight Publishing
• Publisher Founding: March 1, 2014
• Web Domain: http://www.in-sightpublishing.com
• Location: Fort Langley, Township of Langley, British Columbia, Canada
• Journal: In-Sight: Interviews
• Journal Founding: August 2, 2012
• Frequency: Four Times Per Year
• Review Status: Non-Peer-Reviewed
• Access: Electronic/Digital & Open Access
• Fees: None (Free)
• Volume Numbering: 13
• Issue Numbering: 2
• Section: A
• Theme Type: Idea
• Theme Premise: “Outliers and Outsiders”
• Theme Part: 33
• Formal Sub-Theme: None
• Individual Publication Date: January 15, 2025
• Issue Publication Date: April 1, 2025
• Author(s): Scott Douglas Jacobsen
• Word Count: 553
• Image Credits: Tomáš Perna
• ISSN (International Standard Serial Number): 2369-6885

Acknowledgements

The author thanks Tomáš Perna for his time and willingness to participate in this interview.

Author Contributions

S.D.J. conceived and conducted the interview, transcribed and edited the conversation, and prepared the manuscript.

Competing Interests

The author declares no competing interests.

License & Copyright

In-Sight Publishing by Scott Douglas Jacobsen is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
© Scott Douglas Jacobsen and In-Sight Publishing 2012–Present.

Unauthorized use or duplication of material without express permission from Scott Douglas Jacobsen is strictly prohibited. Excerpts and links must use full credit to Scott Douglas Jacobsen and In-Sight Publishing with direction to the original content.

Supplementary Information

Below are various citation formats for Conversation with Tomáš Perna on ANNs and More (6).

Note on Formatting

This layout follows an adapted Nature research-article structure, tailored for an interview format. Instead of Methods, Results, and Discussion, we present Interview transcripts and a concluding Discussion. This design helps maintain scholarly rigor while accommodating narrative content.

#AIDevelopment #ArtificialNeuralNetworks #HumanIntelligenceSimulation #InformationTransmission #IntelligentObservation #KleinGordonEquation #quantumMechanics #SchrödingerEquation #synapticSlots

Un paio di articoli molto interessanti che esplorano le possibilità di un attacco a una rete neurale artificiale basato su “adversarial machine learning”.
In particolare, viene usato quello che in gergo è chiamato “one-pixel attack”, un attacco nel quale basta modificare un solo pixel di una immagine data in input a una rete neurale per generare un errore di classificazione o mandare completamente nel pallone il sistema.
Lo studio qui impiega una tecnica molto simile ai criteri di un algoritmo genetico per generare ed esplorare diversi possibili pattern di attacco basati sulla alterazione di un solo pixel partendo da un insieme iniziale di pattern e facendolo “evolvere” (Differential Evolution) attraverso diverse generazioni.
C’è un mondo di metodologie e tecniche nella AI del tutto sconosciuto al “main stream” e che invece ha potenzialità enormi in diversi ambiti applicativi. La AI dei LLMs e dei chatbot è quella che va consumata.

1) https://arxiv.org/pdf/1902.02947

2) https://arxiv.org/pdf/1710.08864v2

#ai #cybersecurity #artificialneuralnetworks

#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/

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

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

The Man Who Tried to Redeem the World with Logic
Walter Pitts rose from the streets to MIT, but couldn’t escape himself
https://nautil.us/the-man-who-tried-to-redeem-the-world-with-logic-235253/

Discussion: https://news.ycombinator.com/item?id=36415803

[Wikipedia] Walter Pitts: Walter Harry Pitts, Jr. (23 April 1923 – 14 May 1969) was an American logician who worked in the field of computational neuroscience.

#WalterPitts #ArtificialNeuralNetworks #ComputationalNeuroscience #ComputerScience #ArtificialIntelligence #McCullochPittsNeuron #ANN #logic