The Perceptron Model

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CS 621 Artificial Intelligence Lecture 33 - 07/11/05 Guest Lecture by Prof. Rohit Manchanda Biological Neurons - I

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Transcript of The Perceptron Model

Page 1: The Perceptron Model

CS 621 Artificial Intelligence

Lecture 33 - 07/11/05

Guest Lecture by

Prof. Rohit Manchanda

Biological Neurons - I

Page 2: The Perceptron Model

The Perceptron Model

A perceptron is a computing element with input lines having associated weights and the cell having a threshold value. The perceptron model is motivated by the biological neuron.

Output = y





Threshold = θ

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Step function / Threshold functiony = 1 for Σ > θ, y >1

=0 otherwise

Σwixi > θ




Features of Perceptron

• Input output behavior is discontinuous and the derivative does not exist at Σwixi = θ • Σwixi - θ is the net input denoted as net

• Referred to as a linear threshold element - linearity because of x appearing with power 1

• y= f(net): Relation between y and net is non-linear

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Perceptron vs. the point neuron

• Incoming signals from synapses are summed up at the soma

• , the biological “inner product”• On crossing a threshold, the cell “fires”

generating an action potential in the axon hillock region

The McCulloch and Pitt’s neuron

Synaptic inputs: Artist’s conception

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The biological neuron

Pyramidal neuron, from the amygdala (Rupshi et al. 2005)

A CA1 pyramidal neuron (Mel et al. 2004)

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What makes neuronal computation special ?

• Electrical activity in neurons

• Traveling action potentials

• Leaky, conducting membrane

• Highly branched structures

• A variety of electrical connections between neurons

• A varied range of possibilities in neural coding of information

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Dendritic computation

• Time delay• Attenuation, duration increase• Spatial and temporal

summation• Non linear intra branch

summation• Multiple layer configuration• Retrograde propagation of

signals into the dendritic arbor– Backprogating action

potentialsSpatial and temporal summation of proximal and distal inputs (Nettleton et al. 2000)

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Dendritic computationComparison of within-branch andbetween-branch summation.

(a-f)Black: individually Activated Re: simultaneously activatedBlue: arithmetic sum of individual responses.

(g) Summary plot of predicted versus actual combined responses. Coloured circles: within-branch summationDashed line: linear summation. Green diamonds: between-branch summation

(h) Modeling data: summation of EPSPsRed circles: within-branch summation Open green circles: between-branch summation

(Mel et al. 2003, 2004)

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Possible computational consequences of non-linear summation in dendritic sub-trees (Mel et al. 2003)

Dendritic computation: Single neuron as a neural


Another possibility

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Single neuron as a neural network

3 Layered Neural Network2 Layered Neural Network

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A multi neuron pathway in the hippocampus

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Classical Picture Emerging Picture

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Recent neurobiological discoveries

Changes as a result of stress (CIS)– Dendritic atrophy

Consequences– Loss of computational subunits– Changes in connectivity of the


Atrophy and de-branching as seen in the CA3 pyramidal cells from (Ajai et al. 2002)

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Increase in spine count (Amygdaloid neurons) (Rupshi et al. 2005)

Changes as a result of stress (CIS)

•Arbor growth•Increase in spine count


•Increase in computational subunits•Synaptic site increase

Recent neurobiological discoveries