Offensive Security Researcher & Quantum Cryptography Analyst

How Are Neural Networks Inspired by the Human Brain?

Abdul Wahab Junaid
How are neural networks inspired by the human brain?

The term “neural network” itself is a nod to biology, and it’s common to hear deep learning models described as “mimicking the brain.” That framing is useful for intuition, but it can also be misleading if taken too literally. This article explores exactly where the inspiration came from, how far the analogy genuinely holds, and — just as important — where artificial neural networks diverge sharply from biological ones.

Table of Contents

  1. The Biological Neuron
  2. From Biology to Mathematics: The First Models
  3. Structural Parallels Between Brains and Networks
  4. Where the Analogy Breaks Down
  5. Learning: Synaptic Plasticity vs Backpropagation
  6. Mathematical Comparison
  7. Table: Biological vs Artificial Neurons
  8. Neuroscience-Inspired Architectures
  9. Code Example: A Biologically-Inspired vs Standard Neuron
  10. Advantages and Limitations of the Brain Analogy
  11. Best Practices When Using the Analogy
  12. Summary and References

1. The Biological Neuron

A biological neuron consists of dendrites (which receive signals from other neurons), a cell body or soma (which integrates these signals), and an axon (which transmits an output signal to other neurons via synapses). When the combined input signal exceeds a certain threshold, the neuron “fires,” sending an electrical pulse called an action potential down its axon.

This basic behavior — receive inputs, integrate them, and fire (or not) based on a threshold — is precisely what inspired the mathematical model of the artificial neuron.

2. From Biology to Mathematics: The First Models

In 1943, neurophysiologist Warren McCulloch and logician Walter Pitts proposed the first mathematical model of a neuron, representing it as a simple binary threshold unit. In 1958, Frank Rosenblatt built on this idea to create the perceptron, adding learnable weights:

$$ \text{output} = \begin{cases} 1 & \text{if } \sum_i w_i x_i + b \geq 0 \ 0 & \text{otherwise} \end{cases} $$

This equation is a direct, if highly simplified, mathematical analog of a biological neuron firing once its integrated input crosses a threshold.

3. Structural Parallels Between Brains and Networks

Biological ConceptArtificial Neural Network Analog
DendritesInput connections to a neuron
Synaptic strengthWeight values
Cell body (soma) integrationWeighted sum $z = \sum w_i x_i + b$
Firing thresholdActivation function
Axon outputNeuron’s output value
Network of billions of neuronsLayers of artificial neurons
Synaptic plasticity (learning)Weight updates via backpropagation

4. Where the Analogy Breaks Down

Despite the naming and superficial structural similarity, artificial and biological neural networks differ enormously:

5. Learning: Synaptic Plasticity vs Backpropagation

Biological learning is thought to rely heavily on Hebbian plasticity, informally summarized as “cells that fire together, wire together.” A simplified mathematical form is:

$$ \Delta w_{ij} = \eta , x_i , x_j $$

where the synaptic weight between neuron $i$ and neuron $j$ strengthens proportionally to their correlated activity, with no reference to a global error signal or explicit target output.

Artificial neural networks, by contrast, use backpropagation, which requires knowing the exact derivative of a global loss function with respect to every single weight — a computation with no known direct biological equivalent operating at brain scale. This is one of the most actively debated topics in computational neuroscience: how, if at all, biological brains solve a version of the “credit assignment problem” that backpropagation solves mathematically.

graph LR
    D1[Dendrite Input 1] --> S((Soma: Weighted Sum))
    D2[Dendrite Input 2] --> S
    D3[Dendrite Input 3] --> S
    S -->|Threshold Exceeded| AX[Axon: Output Signal]
    AX --> SYN[Synapse to Next Neuron]

6. Mathematical Comparison

Artificial neuron output:

$$ a = \sigma\left(\sum_{i=1}^{n} w_i x_i + b\right) $$

Simplified spiking neuron model (Leaky Integrate-and-Fire), closer to actual biological behavior:

$$ \tau \frac{dV}{dt} = -(V(t) – V_{\text{rest}}) + R \cdot I(t) $$

Here $V(t)$ is the membrane potential over time, $\tau$ is a time constant, $V_{\text{rest}}$ is the resting potential, $R$ is membrane resistance, and $I(t)$ is input current. The neuron fires a spike when $V(t)$ crosses a threshold, then resets. Notice how this model incorporates continuous time explicitly — something standard artificial neurons ignore entirely.

7. Table: Biological vs Artificial Neurons

FeatureBiological NeuronArtificial Neuron
SignalDiscrete electrical spikes over timeSingle continuous value per pass
Learning ruleLocal plasticity (Hebbian-like), neuromodulationGlobal gradient descent (backpropagation)
TimingInherently temporalTypically static, unless explicitly sequential
Energy use~20 watts for the whole brainLarge models use megawatts during training
ConnectivityDensely recurrent, evolved over millions of yearsEngineered, often feedforward or attention-based
Number of units~86 billion neuronsMillions to low trillions of parameters in largest models

8. Neuroscience-Inspired Architectures

Some research directions try to close the biological gap:

9. Code Example: A Biologically-Inspired vs Standard Neuron

import numpy as np

# Standard artificial neuron (no explicit time)
def artificial_neuron(x, w, b):
    z = np.dot(w, x) + b
    return max(0, z)  # ReLU activation

# Simplified Leaky Integrate-and-Fire neuron (discrete-time approximation)
def lif_neuron(input_current, steps=50, tau=10.0, v_rest=0.0, v_thresh=1.0, R=1.0, dt=1.0):
    v = v_rest
    spikes = []
    for t in range(steps):
        dv = (-(v - v_rest) + R * input_current) / tau
        v += dv * dt
        if v >= v_thresh:
            spikes.append(t)
            v = v_rest  # reset after spike
    return spikes

print("Artificial neuron output:", artificial_neuron(np.array([1.0, 2.0]), np.array([0.5, 0.3]), 0.1))
print("Spike times of LIF neuron:", lif_neuron(input_current=0.15))

This comparison highlights how differently the two models represent “activity” — a single static number versus a sequence of discrete spike events over time.

10. Advantages and Limitations of the Brain Analogy

Advantages

Limitations

11. Best Practices When Using the Analogy

12. The Ongoing Scientific Debate: Does the Brain Do Something Like Backpropagation?

One of the most active areas of computational neuroscience research asks whether the brain implements some biologically plausible approximation of gradient-based credit assignment, even if not literal backpropagation. Several proposals have been put forward:

None of these theories has definitively settled the question, and it remains one of the more fascinating open problems bridging neuroscience and machine learning.

13. What Artificial Networks Get Right (and Wrong) About Perception

Interestingly, despite the many differences outlined above, artificial neural networks trained on object recognition tasks have been found to develop internal representations that correlate surprisingly well with activity patterns recorded in the visual cortex of primates performing the same tasks. This suggests that, even without deliberately copying biological mechanisms, optimizing a network to solve the same functional problem (recognizing objects from images) can lead to convergent solutions with some biological validity — an intriguing, partial vindication of the loose brain analogy, even as the underlying learning mechanisms remain very different.

At the same time, artificial vision systems are far more easily fooled than human vision by adversarial examples — tiny, often imperceptible pixel perturbations that cause confident misclassifications — highlighting a real and significant gap between artificial and biological visual processing robustness.

14. Frequently Asked Questions

Does this mean AI could eventually “think” like a human brain? Current neural networks, however large, are trained with objectives and mechanisms (backpropagation, static datasets) fundamentally different from how biological brains develop and learn through lived experience, embodiment, and continuous real-time interaction with the world. Most researchers view today’s systems as narrow pattern-recognition tools rather than steps toward brain-equivalent general intelligence, though this remains a topic of active debate.

Why do we still use the word “neural” if the analogy is so loose? The terminology stuck for historical reasons — the field’s founders were directly inspired by neuroscience, and the name has remained even as the technical details diverged substantially from biology over the following decades.

Are there efforts to make artificial neural networks more biologically realistic? Yes — spiking neural networks and neuromorphic computing hardware (like Intel’s Loihi chips) explicitly aim for greater biological realism, primarily to achieve better energy efficiency, though these approaches remain less mainstream than standard deep learning due to training difficulty and less mature tooling.

15. Beyond Neurons: Other Brain-Inspired Ideas in AI

The influence of neuroscience on AI extends beyond the individual neuron model:

These examples show that the brain-AI relationship isn’t limited to the neuron model alone — it has shaped architectural thinking at many different levels of abstraction throughout the field’s history.

16. Glossary of Key Terms

17. Summary

Neural networks borrow their name and foundational intuition from biological neurons — weighted inputs, integration, and threshold-based firing — but the resemblance is best understood as a loose, historically important inspiration rather than a faithful simulation. Artificial networks learn through global gradient-based optimization (backpropagation), operate on static continuous values rather than temporal spikes, and consume vastly more energy per unit of “intelligence” than biological brains. The analogy remains valuable pedagogically and has genuinely inspired architectural breakthroughs, but real progress in the field increasingly comes from mathematics, engineering, and empirical experimentation rather than direct biological mimicry.

References

Exit mobile version