There are many people trying to show how neural networks are still very different from humans, but I fail to see in what way human brains are different from neural models in anything but complexity.

The way we learn is similar, the way we process information is similar, the ways we predict outcomes and generate outputs are similar. Give a model enough processing power, enough training samples, and enough time and you can train a human.

So, what is the difference between human (brains) and neural networks?

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    $\begingroup$ You might want to check out the Penrose argument. (Some regard it as an argument that the brain is not a computer, but I merely see it as suggesting that the Turing-Church model, in and of itself, may not be sufficient.) Bear in mind it is only a hypothesis, and my take on it is entirely my own: en.wikipedia.org/wiki/The_Emperor%27s_New_Mind $\endgroup$
    – DukeZhou
    Jan 16, 2019 at 21:31

2 Answers 2


One incredibly important difference between humans and NNs is that the human brain is the result of billions of years of evolution whereas NNs were partially inspired by looking at the result and thinking "... we could do that" (utmost respect for Hubel and Wiesel).

Human brains (and in fact anything biological really) have an embedded structure to them within the DNA of the animal. DNA has about 4 MB of data and incredibly contains the information of where arms go, where to put sensors and in what density, how to initialize neural structures, the chemical balances that drive neural activation, memory architecture, and learning mechanisms among many many other things. This is phenomenal. Note, the placement of neurons and their connections isn't encoded in dna, rather the rules dictating how these connections form is. This is fundamentally different from simply saying "there are 3 conv layers then 2 fully connected layers...". There has been some progress at neural evolution that I highly recommend checking out which is promising though.

Another important difference is that during "runtime" (lol), human brains (and other biological neural nets) have a multitude of functions beyond the neurons. Things like Glial cells. There are about 3.7 Glial cells for every neuron in your body. They are a supportive cell in the central nervous system that surround neurons and provide support for and insulation between them and trim dead neurons. This maintenance is continuous update for neural structures and allows resources to be utilized most effectively. With fMRIs, neurologists are only beginning to understand the how these small changes affect brains.

This isn't to say that its impossible to have an artificial NN that can have the same high level capabilities as a human. Its just that there is a lot that is missing from our current models. Its like we are trying to replicate the sun with a campfire but heck, they are both warm.

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    $\begingroup$ Even more fascinating is the idea that evolution itself may be a type of neural network, able to perform computations over many generations newscientist.com/article/… (paywall) $\endgroup$
    – Marc
    Jan 17, 2019 at 9:13

Comparing Unlike Objects

The comparison between a person and an artificial network cannot be made on an equal basis. The former is a composition of many things that the later is not.

Unlike an artificial network sitting in computer memory on a laptop or server, a human being is an organism, from head to toe, living in the biosphere and interacting with other human beings from birth.

Human Training

We have latent intelligence in the zygotes that met to form us and solidified as our genetic code during meiosis, but it is not yet trained. It cannot be until the brain grows from its first cells, directed by the genetic expressions of the brain's metabolic, sensory, cognitive, motor control, and immune structure and function. After nine months of growth, a newborn baby's intelligence is not yet exhibited in motion, language, or behavior other than to suck liquid food.

Our intelligence begins to emerge after initial basic behavioral training and does not reach the ability to pass a test indicating academic abilities until the corresponding stages of development in a family structure and components of education are complete. These are all observations well studied and documented by those in the field of developmental psychology.

Artificial Networks are Not Particularly Neural

An artificial network is a distant and distorted conceptual offspring of a now obsolete model of how neurons behave in networks. Even when the perceptron was first conceived, it was known that neurons reacted to activation from electrical pulses transmitted across synapses from other neurons arranged in complex micro-structures, not by performing an activation function to a vector-matrix product. The parameter matrix at the input of artificial neurons are summing attenuated signals, not electro-chemically reacting to pulses that may only be roughly aligned in time.

Since then, imaging and in vetro study of neurons are revealing the complexities of neuro-plasticity (genetically directed morphing of the network topology of neurons), the many varieties of cell types, the groupings of cells to form function geometrically, and the involvement of energy metabolism in the axon.

In the human brain, chemical pathways of dozens of compounds that regulate function and comprise global and regional states and the secretion, transmission, agonist and antagonist reception, interaction, and uptake of those components is under study. There is barely, if at all, an equivalent in the environment of the artificial networks deployed today, although nothing stops us from designing such regulation systems, and some of the most recent work has pushed the envelope in that direction.

Sexual Reproduction

Artificial networks are also not brains inside individuals produced by sexual reproduction, therefore potentially exhibiting in neurological capacity the best of two parents, or the worst. We do not yet spawn artificial networks from genetic algorithms, although that has been thought of and it is likely to be researched again.

Adjusting the Basis for Comparison

In short, the basis for comparison renders it meaningless, however, with some adjustment based on the above, another similar comparison can be considered that is meaningful and on a more equal basis.

What is the difference between a college student and an artificial network that has billions of artificial neurons, well configured and attached to five senses and motor control, integrated inside a humanoid robot that has been nurtured and educated like a member of a family and a community for eighteen years since its initial deployment?

We don't know. We can't even simulate such a robotic experience of eighteen years or properly project what might happen with scientific confidence. Many of the AI components of the above are not yet well developed. When they are — and there is no particularly compelling reason to think they cannot — then we will find out together.

Research that May Provide an Answer

From further cognitive science development, real time neuron level imaging, work on the genetic expressions out of which brains grow, artificial neuron designs will likely progress beyond perceptrons and the more temporally aware LSTM, B-LSTM, and GRU varieties and the topologies of neuron arrangements may break from their current Cartesian structural limitations.

The neurons in a brain are not arranged in orthogonal rows and columns. They form clusters that exhibit closed loop feedback at low structural levels. This can be simulated by a B-LSTM type artificial network cell, but any electrical engineer schooled in digital circuit design understands that simulation and realization are miles apart in efficiency. A signal processor can run thousands of times faster than its simulation.

From development of computer vision, hearing, tactile-motor coordination, olfactory sensing, materials science support, robotic packaging, and miniature power sources far beyond what lithium batteries can produce may come humanoid robots that can learn while interacting. At that time it would probably be easy to find a family that cannot have children that would adopt an artificial child.

Scientific Rigor

Progress in these areas is necessary for such a comparison to be made on a scientific basis and for confidence in the comparison results to be published and pass peer review by serious researchers not interested in media hype, making the right career moves, or hiking their company's stock prices.


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