Last modified: Jan 31 2026 at 10:09 PM • 9 mins read

What does this have to do with the brain?

Introduction
The Honest Answer
Why the Brain Analogy Persists
The Biological Neuron vs Artificial Neuron
What We Don’t Know About the Brain
The Limited Analogy
How to Think About Deep Learning Today
Domain-Specific Inspiration
The Evolution of the Analogy
Practical Implications
The Future
Key Takeaways

Introduction

You’ve learned how to implement forward and backward propagation for deep neural networks. Now let’s address a common question: What does deep learning have to do with the human brain?

Short answer: Not as much as the name “neural network” suggests!

This lesson clarifies:

Why the brain analogy exists (and why it’s appealing)
What artificial neurons actually do vs biological neurons
Why the analogy is oversimplified
When the analogy is (and isn’t) useful
How to think about deep learning today

The Honest Answer

The Punchline First

Deep learning has very little to do with how the human brain actually works.

Despite the name “neural networks” and the terminology we use:

Neurons
Layers
Activation
Connections

The actual algorithms we implement (forward prop, backprop, gradient descent) are mathematical optimization techniques, not biological processes.

Why the Brain Analogy Persists

Reason 1: Simplicity and Seductiveness

When you implement a neural network, you write:

\[Z^{[l]} = W^{[l]} A^{[l-1]} + b^{[l]}\] \[A^{[l]} = g^{[l]}(Z^{[l]})\]

The challenge: Explaining what these equations really do—learning complex functions through optimization—is difficult.

The temptation: Saying “it works like the brain” is:

Simple to understand
Intuitive for non-experts
Exciting for popular imagination
Media-friendly for news articles

Problem: This oversimplification obscures what’s actually happening and creates misconceptions.

Reason 2: Historical Origins

The original perceptron (1950s-1960s) was explicitly inspired by biological neurons:

Takes multiple inputs
Weights them
Produces an output if threshold exceeded

This initial inspiration gave us the terminology, but the field has evolved far beyond this simple model.

Reason 3: Public Appeal

The brain analogy captures public imagination:

Headlines that work:

“AI that thinks like a human brain!”
“Machines learning the way you do!”
“Computer with artificial neurons!”

Reality: Mathematical function approximators optimized with calculus

The gap between these is significant!

The Biological Neuron vs Artificial Neuron

Biological Neuron (Actual Brain Cell)

How it works (simplified):

Input: Receives electrical/chemical signals from other neurons
Integration: Combines signals (not just linear sum!)
Threshold: If combined signal exceeds threshold, neuron “fires”
Output: Sends electrical pulse down axon to other neurons

Key characteristics:

Chemical and electrical signaling
Complex temporal dynamics (timing matters!)
Non-linear integration mechanisms
Adaptive thresholds
Multiple neurotransmitters
Synaptic plasticity (connections change)

Artificial Neuron (In Neural Network)

    Inputs: x₁, x₂, x₃
         ↓  ↓  ↓
    Weights: w₁, w₂, w₃
         ↓  ↓  ↓
    Linear: z = Σ(wᵢxᵢ) + b
         ↓
    Activation: a = g(z)
         ↓
    Output: a

How it works:

\[z = w_1 x_1 + w_2 x_2 + w_3 x_3 + b\] \[a = g(z)\]

Where $g$ might be sigmoid, ReLU, or tanh.

Key characteristics:

Simple weighted sum
Deterministic activation function
No temporal dynamics (timing ignored)
No chemical processes
Fixed computational model
Parameters updated via gradient descent

The Comparison

Aspect	Biological Neuron	Artificial Neuron
Inputs	1,000-10,000 synapses	Typically 10-1,000
Processing	Complex, poorly understood	Simple: $z = Wx + b$, $a = g(z)$
Timing	Critical (millisecond precision)	Ignored (static computation)
Plasticity	Continuous adaptation	Discrete updates (gradient steps)
Energy	Extremely efficient (~20W for brain)	Power-hungry (GPUs use 100s of watts)
Speed	Slow (~1-100 Hz firing rate)	Fast (billions of ops/sec)
Understood?	Very little	Completely

Key insight: Even neuroscientists don’t fully understand what a single biological neuron does!

What We Don’t Know About the Brain

Mystery 1: Single Neuron Complexity

Current knowledge: A biological neuron is far more complex than our models suggest.

Recent neuroscience discoveries:

Neurons have internal compartments with different functions
Dendrites (input branches) perform computations themselves
Non-linear signal integration in dendrites
Hundreds of ion channels with complex dynamics
Multiple types of neurotransmitters with different effects

Reality check: A single biological neuron may be as complex as an entire small neural network!

Mystery 2: Learning Mechanisms

Question: How does the brain learn?

Honest answer: We don’t really know.

What we know it’s NOT:

Almost certainly not backpropagation (no mechanism for it)
Almost certainly not gradient descent (no global cost function)

Possibilities being researched:

Local learning rules (Hebbian: “neurons that fire together wire together”)
Spike-timing-dependent plasticity (STDP)
Reward-based learning (dopamine signals)
Predictive coding
Some completely unknown mechanism

Key point: Whether the brain uses anything resembling backpropagation remains an open question in neuroscience.

Mystery 3: Network Organization

The brain’s architecture is nothing like our feed-forward networks:

Artificial networks:

Input → Layer 1 → Layer 2 → ... → Layer L → Output
(feed-forward, clean hierarchy)

Brain:

Massive recurrent connections
Feedback loops everywhere
Hierarchical AND lateral connections
Dynamic routing of information
Attention mechanisms
Working memory

The brain is vastly more complex than our simplified architectures.

The Limited Analogy

Where the Analogy Holds (Weakly)

Similarity 1: Computation from inputs

Both take inputs and produce outputs:

Artificial	Biological
$a = \sigma(w^T x + b)$	Neuron fires based on inputs

Similarity 2: Thresholding

Both have activation thresholds:

Artificial	Biological
ReLU: $\max(0, z)$	Fire only if threshold exceeded

Similarity 3: Distributed representation

Both use multiple units working together:

Artificial	Biological
Many neurons per layer	Many neurons per brain region

Where the Analogy Breaks Down

Difference 1: Learning mechanism

Artificial	Biological
Backpropagation + gradient descent	Unknown (likely very different)

Difference 2: Computation style

Artificial	Biological
Synchronous, layer-by-layer	Asynchronous, massively parallel

Difference 3: Temporal dynamics

Artificial	Biological
Static (timing irrelevant)	Dynamic (timing is everything)

Difference 4: Energy efficiency

Artificial	Biological
Requires huge power (GPUs)	Human brain: ~20 watts total

Difference 5: Generalization

Artificial	Biological
Needs millions of examples	Humans learn from few examples

How to Think About Deep Learning Today

The Modern Perspective

Deep learning: A mathematical framework for learning complex functions from data using gradient-based optimization.

What deep learning is GOOD for:

Learning flexible functions: $f: X \rightarrow Y$
Input-output mappings: Supervised learning tasks
Pattern recognition: Images, speech, text
Function approximation: Any smooth function
Feature learning: Automatic representation discovery

What makes it work:

Universal approximation: Neural networks can approximate any function
Gradient descent: Efficient optimization algorithm
Backpropagation: Efficient way to compute gradients
Big data: Lots of examples to learn from
Computational power: GPUs make training feasible

Not because it mimics the brain!

A Better Mental Model

Instead of thinking:

“Neural networks work like the brain”

Think:

“Neural networks are function approximators optimized with calculus and linear algebra”

The process:

Data: $(x^{(1)}, y^{(1)}), (x^{(2)}, y^{(2)}), \ldots, (x^{(m)}, y^{(m)})$
Model: $\hat{y} = f(x; W, b)$ (parameterized function)
Loss: $\mathcal{L}(\hat{y}, y)$ (how wrong are we?)
Optimization: Adjust $W, b$ to minimize $\mathcal{L}$ using gradients
Result: Function that maps $x \rightarrow y$ accurately

This is mathematical optimization, not biological simulation.

Domain-Specific Inspiration

Computer Vision: More Brain-Inspired

Observation: Computer vision has taken slightly more inspiration from neuroscience than other fields.

Examples:

Hierarchical processing
- Brain: V1 → V2 → V4 → IT cortex
- CNNs: Conv1 → Conv2 → Conv3 → FC layers
Local receptive fields
- Brain: Neurons respond to local image regions
- CNNs: Convolutional filters operate locally
Feature hierarchy
- Brain: Edges → shapes → objects → concepts
- CNNs: Low-level → mid-level → high-level features

But even here: The implementation details are completely different!

Other Domains: Less Brain Connection

Domain	Brain Inspiration	Actual Implementation
NLP	Minimal	Transformers, attention (not brain-like)
Speech	Some (auditory cortex)	Signal processing + deep learning
Reinforcement Learning	Some (dopamine, rewards)	Bellman equations, Q-learning
Time Series	Minimal	RNNs, LSTMs (mathematical constructs)

The Evolution of the Analogy

Historical Perspective

1950s-1960s: Strong brain inspiration

Perceptrons explicitly modeled after neurons
Enthusiasm about “thinking machines”

1980s-1990s: Mathematical focus

Backpropagation derived from calculus
Universal approximation theorems
Focus on optimization

2000s-2010s: Engineering success

Deep learning works on practical problems
Brain analogy revived for marketing
But practitioners know it’s optimization

2020s (Today): Pragmatic view

Brain analogy used for intuition only
Research focuses on what works, not biological plausibility
Neuroscience and AI are separate (though interacting) fields

Personal Perspective (from Andrew Ng)

“When I think of deep learning, I think of it as being very good at learning flexible, complex functions—learning $X$ to $Y$ mappings in supervised learning.”

“The brain analogy maybe was useful once, but I think the field has moved to the point where that analogy is breaking down.”

“I tend not to use that analogy much anymore.”

This is the expert view: The brain analogy is historical, not technical.

Practical Implications

What This Means for You

When learning deep learning:

✅ Do: Focus on the mathematics
- Understand forward propagation (function composition)
- Understand backpropagation (chain rule)
- Understand gradient descent (optimization)
✅ Do: Think in terms of functions
- Neural networks learn $f: X \rightarrow Y$
- Layers compose functions
- Training finds good parameters
❌ Don’t: Get distracted by brain analogies
- They don’t help you implement better models
- They don’t explain why things work
- They can be misleading
❌ Don’t: Expect biological plausibility
- We’re not simulating brains
- We’re solving engineering problems
- Different goals entirely

When the Analogy Is Useful

The brain analogy can help with:

High-level intuition:

“Multiple processing units working together”
“Hierarchical feature learning”
“Distributed representations”

Communication with non-experts:

Explaining what neural networks are
Generating interest and excitement
Avoiding heavy mathematics

But always clarify: It’s a loose metaphor, not a technical description!

The Future

Diverging Paths

Neuroscience and AI are increasingly separate fields with different goals:

Neuroscience goals:

Understand how biological brains work
Explain consciousness, memory, cognition
Medical applications (treating brain disorders)

Deep learning goals:

Build practical systems that work
Maximize performance on specific tasks
Scale to large datasets efficiently

Overlap: Minimal and shrinking.

Potential Reconnection

Possible future:

If we really understand how the brain learns, we might borrow those principles
Brain-inspired algorithms might be more efficient
But we’re nowhere near that understanding yet

Current reality: Deep learning advances through:

Engineering innovations
Mathematical insights
Empirical experimentation

Not through neuroscience discoveries.

Key Takeaways

Deep learning ≠ brain: Despite the name, neural networks are mathematical optimization tools, not brain simulations
Oversimplified analogy: The brain comparison is seductive but misleading
Biological neurons are complex: Far more sophisticated than our models
Learning is mysterious: We don’t know how the brain learns (probably not backprop!)
Artificial neurons are simple: Just $z = Wx + b$ and $a = g(z)$
No temporal dynamics: Our networks ignore timing (crucial in brains)
Unknown learning mechanism: Brain likely doesn’t use gradient descent
Energy efficiency: Brain uses ~20W, GPUs use 100s of watts
Different organization: Brain has massive recurrence, feedback, and dynamics
Think mathematically: Focus on function approximation and optimization
Computer vision exception: Slightly more brain-inspired than other domains
Historical artifact: Brain analogy comes from 1950s-1960s origins
Modern perspective: Practitioners don’t rely on brain analogy
Separate fields: Neuroscience and AI have different goals
What matters: Deep learning works because of math, data, and compute—not biological plausibility!