The document discusses the complexities of creating artificial intelligence modeled on biological neural networks, highlighting the dynamic and interconnected nature of neurons. It emphasizes that biological systems do not require vast amounts of data and suggests that while adding complexity may be beneficial, one should start with simpler models. Ultimately, it questions the necessity of building overly complex networks when biological systems provide effective solutions.

1. CREATING AI USING
BIOLOGICAL NETWORK
TECHNIQUES
Dr Janet Bastiman, Chief Science Officer Story-Stream.com
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2. “Human subtlety will never
devise an invention more
beautiful, more simple or
more direct than does
nature”
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3. - Neurons have memory
- Connections are dynamic
- Computational models are simplistic
Integrate and fire is convenient but
easy.
NEURONS ARE NOT
TRANSISTORS

4. Multiple connections of different
types gives complexity and
adaptability.
Circuit break at synapses gives
some interesting dynamics.
BIOLOGICAL CONNECTIONS ARE COMPLEX

5. SYNAPSES PROVIDE
CONTROL
Different synapses are affected in different ways.
• Signal fatigue/strengthening
• Amplitude and frequency of action potential
• External influence
Frequency of firing effects the signal
strength.

6. Still feed forward
Memory within layers to impact future
events
Does very well on predictions
Could we do better?
LONG SHORT TERM MEMORY
NETWORKS, IS THIS ENOUGH?

7. NEURONS ARE
PACKED DENSELY
This is not a simple feed forward model.
• Multiple pathways can affect each other
• Diffusion can alter neurons without direct
connection
• Circular connectivity in places
Neurons exist in 3D space with major
interconnectivity.

8. ADDING SPATIAL
COORDINATES
Adding location allows diffusion to connect layers
differently
Responsiveness to diffusion can be trained to
achieve its own levels of activation and fatigue
• Level of detail depends on problem
• Overhead to reward not suitable for all
problems
Start simple – add complexity as needed.
Affect disparate parts of your network
at run time.

9. General intelligence is hard
Some specific tasks are also difficult with
existing techniques
“More data” not always possible
Biology doesn’t need large amounts of data so
what can we learn
Why make networks more complex
than they already are?

10. STORY-STREAM.COM
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NOBODY WANTS AN
EMOTIONAL CAR, OR
DO WE?

11. STORY-STREAM.COM
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Thank You
@yssybyl
http://janjanjan.uk
linkedin.com/in/janetbastiman