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Artificial vs Biological Neural Networks: models and debates. A presentation based on Lehky & Sejnowski’s network model of shape-from-shading. Presented by Clara Boyd and Angelos Stavrou. Different Types: ( if the neurons of one of the net's layers may be connected among each other)

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artificial vs biological neural networks models and debates

Artificial vs Biological Neural Networks: models and debates

A presentation based on

Lehky & Sejnowski’s network model of shape-from-shading

Presented by Clara Boyd and Angelos Stavrou

a brief overview of artificial neural networks

Different Types:

(if the neurons of one of the net's layers may be connected among each other)

    • Feed Forward
    • Feed Back

A Brief Overview of Artificial Neural Networks

  • Different Learning Algorithm:

(A mathematical algorithm that a neural net uses to learn specific problems)

    • Backpropagation
    • Delta Learning Rule
    • Forward Propagation
    • Hebb Learning Rule
    • Simulated Annealing
a brief overview of artificial neural networks3

Perceptron

    • The Perceptron was first introduced by F. Rosenblatt in 1958

A Brief Overview of Artificial Neural Networks

Type:

Feedforward

Neuron layers:

1 input layer

1 output layer

Input value types:

Binary

Learning Method:

Supervised

a brief overview of artificial neural networks4

Multi-Layer-Perceptron

    • The Multi-Layer-Perceptron was first introduced by M. Minsky and S. Papert in 1969

Type:

Feedforward

Neuron layers:

1 input layer

1 or more hidden layers 1 output layer

Input value types:

Binary

Learning Method:

Supervised

A Brief Overview of Artificial Neural Networks

a brief overview of artificial neural networks5

Backpropagation Network

    • The Backpropagation Net was first introduced by G.E. Hinton, E. Rumelhart and R.J. Williams in 1986

Type:

Feedforward

Neuron layers:

1 input layer

1 or more hidden layers 1 output layer

Input value types:

Binary

Learning Method:

BackPropagation

A Brief Overview of Artificial Neural Networks

slide6

Hubel & Wiesel

  • Area V1 in the Monkey:
  • Receptive Fields (orientation selectivity to bar of light)
  • Vision based on a set of EMERGENT properties
  • Each cortical cell extracts a different feature of the visual image

Simple Cell

Complex Cell

slide7

Macrocircuitry Between Visual Areas

MT

1.Redundancy of Connections

PO

V3

VP

PIP

V2

2. Bidirectional Transport

V1

3. Hierarchical Organization

4. Parallel Pathways

slide8

Hierarchical

Arrangement

Of Visual

Processing

Stages

slide9

The Visual Pathway

Decisions & Actions

(& Conscious Awareness?)

Prefrontal Areas

& Premotor Areas

“Higher” Visual Areas

(V2, V3, V4, Medial Temporal)

Striate Cortex

(V1/area 17)

Lateral Geniculate Nucleus

Retina

slide10

Microcircuitry: V1 Organization

Layer Specific

1. Main Input: from different parts (I,P,M) of LGN terminate in different

lamina (mostly lamina #4)

2. Other Inputs: (V2,V3,etc) avoid lamina #4

3. Resident Cells: characteristic for a given layer

a) lamina to lamina – recurrent/colateral branches form circuit

b) projection axons – exhibit lamina specificity

Highly Localized Processing

- most V1 projections don’t go very far

- more vertical than horizontal

Many Synapses

- convergence and divergence

- stellate cells/local interneurons & pyramidal neurons

discussion and open questions

Discussion and Open Questions

  • Learning using a Back propagation technique vs pure Feed Forward models of Hubel & Wiesel
  • How extensive is the inherited genetic knowledge?

Equivalency of models of Artificial neural networks to Biological systems (Strong / Weak)

discussion and open questions16

Discussion and Open Questions

  • But is our knowledge of learning adequate?
  • How the Feed-Forward network is created?

Although 80% of the artificial neural networks work using Back propagation there is no strong biological support this rule.

  • Different “modes” of learning Feed-Forward vs Back-Propagation but same result?
  • Intrinsic properties are necessary in any case of a biological network, evidence of “prenatal neural networks”
discussion and open questions17

Discussion and Open Questions

  • Master / Slave approach and Rule based Learning.

But is Back-propagation learning achieved by an “outer” and bigger environment/network?

  • Maybe the truth is a hybrid of genetically inherited knowledge and learning rules on hierarchical unstructured neural networks.