1.6 KiB
1.6 KiB
Linearity
- Neurons can be linear or non-linear
- Network of non-linear neurons is non-linear
- Non-linearity is distributed
- Helpful if target signals are generated non-linearly
Input-Output Mapping
- Map input signal to desired response
- Supervised learning
- Similar to non-parametric statistical inference
- Non-parametric as in no prior assumptions
- No probabilistic model
Adaptivity
- Synaptic weights
- Can be easily retrained
- Can operate in non-stationary environments
- Can change weights in real-time
- In general, more adaptive = more robust
- Not always though
- Short time-constant system may be thrown by short-time spurious disturbances
- Stability-plasticity dilemma
Evidential Response
- Decisions are made with evidence not just declared
- Confidence value
Contextual Information
- Knowledge represented by structure and activation weight
- Any neuron can be affected by global activity
- Contextual information handled naturally
Fault Tolerance
- Hardware implementations
- Performance degrades gracefully with adverse conditions
- If some of it breaks, it won't cause the whole thing to break
- Like a real brain
VLSI Implementability
- Very large-scale integration
- Chips with millions of transistors (MOS)
- E.g. microprocessor, memory chips
- Massively parallel nature
- Well suited for VLSI
Uniformity in Analysis
- Are domain agnostic in application
- Analysis methods are the same
- Can share theories, learning algorithms
Neurobiological Analogy
- Design analogous to brain
- Already a demonstrable fault-tolerant, powerful, fast, parallel processor