The paradox of the efficient code and the neural Tower of Babel

«A pervasive metaphor in neuroscience is the idea that neurons “encode” stuff: some neurons encode pain; others encode the location of a sound; maybe a population of neurons encode some other property of objects. What does this mean? In essence, that there is a correspondence between some objective property and neural activity: when I feel pain, this neuron spikes; or, the image I see is “represented” in the firing of visual cortical neurons. The mapping between the objective properties and neural activity is the “code”. How insightful is this metaphor?

An encoded message is understandable to the extent that the reader knows the code. But the problem with applying this metaphor to the brain is only the encoded message is communicated, not the code, and not the original message. Mathematically, original message = encoded message + code, but only one term is communicated. This could still work if there were a universal code that we could assume all neurons can read, the “language of neurons”, or if somehow some information about the code could be gathered from the encoded messages themselves.

Unfortunately, this is in contradiction with the main paradigm in neural coding theory, “efficient coding”.
The efficient coding hypothesis stipulates that neurons encode signals into spike trains in an efficient way, that is, it uses a code such that all redundancy is removed from the original message while preserving information, in the sense that the encoded message can be mapped back to the original message (Barlow, 1961; Simoncelli, 2003). This implies that with a perfectly efficient code, encoded messages are undistinguishable from random. Since the code is determined on the statistics of the inputs and only the encoded messages are communicated, a code is efficient to the extent that it is not understandable by the receiver. This is the paradox of the efficient code.
In the neural coding metaphor, the code is private and specific to each neuron. If we follow this metaphor, this means that all neurons speak a different language, a language that allows expressing concepts very concisely but that no one else can understand. Thus, according to the coding metaphor, the brain is a Tower of Babel.»

Лонг-рід з Chat-GPT промптами для технічних команд

Блог Genesis зібрав лайфхаки промпт-інжинірінгу для всіх, хто багато працює з кодом і вже звик щось питати в чат-гпт. Поради стосуються таких юз-кейсів, як генерація, тестування та конвертація коду, рефакторинг, пошук помилок, архітектура, девопс та інженерія даних. Деякі запити дійсно очевидні, але я думаю ви знайдете там для себе цікавинки.

Особливо зверніть увагу на архітектурну частину статті. Наша команда теж користується схожими методами, коли шукає архітектурні рішення.

#shared_link
#machine_learning

It is precisely this question that the team has answered. They proved that the maximum robustness is proportional to the logarithm of the fraction of all possible sequences that map to a phenotype, with a correction which is given by the sums of digits function sk(n), defined as the sum of the digits of a natural number n in base k. For example, for n = 123 in base 10, the digit sum would be s10(123) = 1 + 2 + 3 = 6. 

*insert random joke about gematria*

Кому математичну модель макарон

https://www.researchgate.net/publication/349158902_The_Factory_Must_Grow_Automation_in_Factorio