computers with brains? a neuroscience perspective

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Computers with Brains? A neuroscience perspective

Khurshid Ahmad, Professor of Computer Science,

Department of Computer ScienceTrinity College,

Dublin-2, IRELANDOctober 29th , 2009.

https://www.cs.tcd.ie/Khurshid.Ahmad/Teaching/ComputersBrains.pdf

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Brain – The Processor!

http://www.cs.duke.edu/brd/Teaching/Previous/AI/pix/noteasy1.gif

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Brain – The Processor!Neural computing systems are trained on the principle that if a network can compute then it will learn to compute. Multi-net neural computing systems are trained on the principle that if two or more networks learn to compute simultaneously or sequentially , then the multi-net will learn to compute.

Our project is to build a neural computing system comprising networks that can not only process unisensory input and learn to process but that the interaction between networks produces multisensory interaction, integration, enhancement/suppression, and information fusion.

Tim Shallice (2006). From lesions to cognitive theory. Nature Neuroscience Vol 6, pp 215 (Book Review: Mark D’Esposito (2002). Neurological Foundations of Cognitive Neuroscience

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What humans do?

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What animals do?

Dendritic computation. The task of a brainstem auditory neuron performing coincidence detection in the sound localization system of birds is to respond only if the inputs arriving from both ears coincide in a precise manner (10–100 μs), while avoiding a response when the input comes from only one ear.

London, Michael and Michael Häusser (2005). Dendritic Computation. Annual Review of Neuroscience. Vol. 28, pp 503–32

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What computer scientists do?

The study of the behaviour of neurons, either as 'single' neurons or as cluster of neurons controlling aspects of perception, cognition or motor behaviour, in animal nervous systems is currently being used to build information systems that are capable of autonomous and intelligent behaviour.

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What animals do?

Neurons are known to be involved in much more sophisticated computations, such as face recognition [..]. An algorithm to solve a face recognition task is one of the holy grails of computer science. At present, we do not know precisely how single neurons are involved in this computation. An essential first step is feature extraction from the image, which clearly involves a lot of network pre-processing before features are fed into the individual cortical neuron. The flowchart implements a three-layer model of dendritic processing […] to integrate the input. The way such a flowchart is mapped onto the real geometry of a cortical pyramidal neuron remains unknown.


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What animals do?


Neurons, and indeed networks of neurons perform highly specialised tasks. The dendrites bring the input in, the soma processes the input and then the axon outputs. However, it appears that the dendrites also have processing power: it is the equivalent of the wires that connects your computer to its printer and the network hub performing computations – helping the computer to perform computations!!!

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What humans think about what computers will do?

http://www.longbets.org/1

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What humans do? They have an intricate

brain

11http://en.wikipedia.org/wiki/Neural_network#Neural_networks_and_neuroscience

Neural Networks &Neurosciences

Observed Biological Processes (Data)

Biologically PlausibleMechanisms for Neural Processing & Learning

(Biological Neural Network Models)

Theory(Statistical Learning Theory &

Information Theory)

Neural Nets and Neurosciences

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Real Neuroscience

Cognitive neuroscience has many intellectual roots.

The experimental side includes the very different methods of systems neuroscience, human experimental psychology and, functional imaging.

The theoretical side has contrasting approaches from neural networks or connectionism, symbolic artificial intelligence, theoretical linguistics and information-processing psychology.

Tim Shallice (2006). From lesions to cognitive theory. Nature Neuroscience Vol 6, pp 215 (Book Review: Mark D’Esposito (2002). Neurological Foundations of Cognitive Neuroscience

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Real Neuroscience


Brains compute. This means that they process information, creating abstract representations of physical entities and performing operations on this information in order to execute tasks. One of the main goals of computational neuroscience is to describe these transformations as a sequence of simple elementary steps organized in an algorithmic way. The mechanistic substrate for these computations has long been debated. Traditionally, relatively simple computational properties have been attributed to the individual neuron, with the complex computations that are the hallmark of brains being performed by the network of these simple elements.

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Real Neuroscience

I compute, therefore I am (intelligent):

I can converse in natural languages;I can analyse images, pictures (comprising images), and scenes (comprising pictures);I can reason, with facts available to me, to infer new facts and contradict what I had known to be true;I can plan (ahead);I can use symbols and analogies to represent what I know;I can learn on my own, through instruction and/or experimentation;I can compute trajectories of objects on the earth, in water and in the air;I have a sense of where I am physically (prio-perception)I can deal with instructions, commands, requests, pleas;I can ‘repair’ myself;I can understand the mood/sentiment/affect of people and groupsI can debate the meaning(s) of life;

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Real Neuroscience

I cannot compute, therefore I am (not intelligent?):

I cannot add/subtract/multiply/divide with consistent accuracy;I forget some of the patterns I had once memorised;I confuse facts;I cannot recall immediately what I know;I cannot solve complex equations;I am influenced by my environment when I make decisions, ask questions, pass comments;I will (eventually) loose my faculties and then die!!

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Real Neuroscience

I can and annot not compute because of my nervous system

I use language, can see things, represent knowledge, learn, plan, reason …. Because of my nervous system?

I cannot do arithmetic consistently, cannot recall and/or forget…. Because of my nervous system?

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DEFINITIONS: Artificial Neural Networks

Artificial Neural Networks (ANN) are computational systems, either hardware or software, which mimic animate neural systems comprising biological (real) neurons. An ANN is architecturally similar to a biological system in that the ANN also uses a number of simple, interconnected artificial neurons.

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Artificial neural networks emulate threshold behaviour, simulate co-operative phenomenon by a network of 'simple' switches and are used in a variety of applications, like banking, currency trading, robotics, and experimental and animal psychology studies.

These information systems, neural networks or neuro-computing systems as they are popularly known, can be simulated by solving first-order difference or differential equations.

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The real neurons are different!

Real neurons co-operate, compete and inhinbit each other. In multi-modal information processing, convergence of modalities is critical.

From Alex Meredith, Virginia Commonwealth University, Virginia, USA

MultisensoryEnhancement

Cross-modalSuppression

Cross-modalFacilitation

Cross-modalFacilitationInhibition-Dependent

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Computation and its neural basis

Much of modern computing relies on the discrete serial processing of uni-modal data

Much of the computing in the brain is on sporadic, multi-modal data streams

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Analysis of neuroscience experiments is carried out with simple models without the capability of learning

Neural computing emphasises the learning aspects of behaviour

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Decision making invariably involves fusion of multi-modal data streams in the brain involving emotion and reasoning

Learning to make decisions in noisy environments is something very human; key areas are image annotation, sentiment analysis

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Network Epicentre 1 Epicentre 2

Spatial awareness Posterior Parietal Cortex Frontal eye fields

Language Wernicke’s Area Broca’s area

Explicit memory/emotion Hippocampal-entorhinal complex The Amygdla

Face-Object recognition Mid-temporal cortex Temporo-polar cortex

Working memory-executive function Lateral pre-frontal cortex Posterior Parietal Cortex (?)

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What computers can do? Artificial Neural Networks In a restricted sense artificial neurons are simple emulations of biological neurons: the artificial neuron can, in principle, receive its input from all other artificial neurons in the ANN; simple operations are performed on the input data; and, the recipient neuron can, in principle, pass its output onto all other neurons.

Intelligent behaviour can be simulated through computation in massively parallel networks of simple processors that store all their long-term knowledge in the connection strengths.

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What computers can do? Artificial Neural Networks

According to Igor Aleksander, Neural Computing is the study of cellular networks that have a natural propensity for storing experiential knowledge.

Neural Computing Systems bear a resemblance to the brain in the sense that knowledge is acquired through training rather than programming and is retained due to changes in node functions.

Functionally, the knowledge takes the form of stable states or cycles of states in the operation of the net. A central property of such states is to recall these states or cycles in response to the presentation of cues.

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What can computers do?

http://search.eb.com.elib.tcd.ie/eb/art-68188/Moores-law-In-1965-Gordon-E-Moore-observed-that-the

Computers help you in visualising the un-built environment

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http://www.nytimes.com/2009/07/26/science/26robot.html

Robots, containing sophisticated computers, can perform a variety of tasks in an automated factory, in bomb disposal and other applications. Robots need to be looked after – be energized, helped to navigate, tasks to be assigned, but, but ……….

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http://www.nytimes.com/2009/07/26/science/26robot.html & http://www.imdb.com/media/rm969447936/tt0062622

Robots, containing sophisticated computers, can perform a variety of tasks in an automated factory, in bomb disposal and other applications. Robots need to be looked after – be energized, helped to navigate, tasks to be assigned, but, but ……….

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http://www.ibm.com/ibm/ideasfromibm/us/roadrunner/20080609/index.shtml

Giga

Tera

RoadRunner = 100,000 Laptops (running simultaneously and exchanging information pico second by pico second)

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Computers dealing with problems in neuroscience

http://affect.media.mit.edu/pdfs/07.Teeters-sm.pdf

Dealing with idiosyncratic behaviour: Letting the autistic child/adult have feedback on self

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Computers dealing with problems in neuroscience

http://affect.media.mit.edu/pdfs/07.Teeters-sm.pdf

Dealing with idiosyncratic behaviour

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How do computers do what computers do?


The number of chips on the same area has doubled every 18-24 months; and has increased exponentially. However, the R&D costs and manufacturing costs for building ultra-small, high-precision circuitry and controls has had an impact on the prices

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The ever growing computer systems (1997)

http://www.transhumanist.com/volume1/moravec.htm

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The ever growing computer systems

http://en.wikipedia.org/wiki/File:Cmos-chip_structure_in_2000s_(en).svghttp://www.transhumanist.com/volume1/moravec.htm & http://www.loc.gov/webarchiving/faq.html

The industry took 35 years to reach 1GB (in 1991), 14 years more to reach 500GB (in 2005), and just two more years to reach 1TB (2007)

Giga

Tera

Library of Congress Minerva Web Presence

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The cost of computation is falling dramatically – an exponential decay in what we can get by spending $1000 (calculations per second):

In 1940: 0.01In 1950: 1In 1960: 100In 1970: 500-1000In 1980: 10,000In 1990: 100,000In 2000: 1,000,000

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The cost of data storage is falling dramatically – an exponential decay in what we can get by spending the same amount of money:In 1980: 0.001 GBIn 1985: 0.01In 1990: 0.1In 1995: 1In 2000: 10In 2005: 100In 2010: 1000

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The ever growing computer systems:

Supercomputers of today

300 to 1400 Trillion Floating Point Operations per Second


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The intelligent computer:Turing Test

http://en.wikipedia.org/wiki/File:Cmos-chip_structure_in_2000s_(en).svg

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What humans think about what computers will do?

http://www.longbets.org/1

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http://crca.ucsd.edu/~hcohen/cohenpdf/furtherexploits.pdf

Aaron, an expert system that can ‘paint’ has had ITS exhibition in major galleries Here is Aaron’s Liberty & Freinds based on the Statute of Liberty

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Aaron, an expert system that can ‘paint’ has had ITS exhibition in major galleries Here is Aaron’s Theo

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What computers cannot do?

Hans Moravec (1998). When will computer hardware match the human brain? Journal of Evolution and Technology. 1998. Vol. 1 (at http://www.transhumanist.com/volume1/moravec.htm)

The Vision Problem 1967-1997Thirty years of computer vision reveals that

1 MIPS can extract simple features from real-time imagery--tracking a white line or a white spot on a mottled background.

10 MIPS can follow complex gray-scale patches--as smart bombs, cruise missiles and early self-driving vans attest.

100 MIPS can follow moderately unpredictable features like roads--as recent long NAVLAB trips demonstrate.

1,000 MIPS will be adequate for coarse-grained three-dimensional spatial awareness--.

10,000 MIPS can find three-dimensional objects in clutter--

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http://www.electronicspecifier.com/Industry-News/New-SH7724-processors-add-HD-video-playback-and-recording-support-to-Renesas-Technologys-popular-SH772x-series-of-low-power-multimedia-processors.asp

The Vision Problem – The story continues (2009)

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http://www.electronicspecifier.com/Industry-News/New-SH7724-processors-add-HD-video-playback-and-recording-support-to-Renesas-Technologys-popular-SH772x-series-of-low-power-multimedia-processors.asp

The Vision Problem – The story continues (2009)Processors add HD video playback and recording support to Renesas Technology's popular SH772x series of low power multimedia processors

News Release from: Renesas Technology Europe Ltd27/05/2009

Renesas has announced the release of the SH7724, the third product in the SH772x series of low power application processors designed for multimedia applications such as audio and video for portable and industrial devices.

When operating at 500 MHz, general processing performance is 900 million instructions per second (MIPS) and FPU processing performance is 3.5 giga [billion] floating-point operations per second (GFLOPS).

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What a smart computer does:

Represents knowledge!A Hierarchical Network•

salmonlays eggs; swims upstream,

is pink, is edible

ostrichruns fast, cannot fly,

is tall

canarycan sing, is yellow

birdcan fly, has wings,

has feathers

fishcan swim, has fins, has gills

animalcan breathe, can eat,

has skin

is-a

is-a

is-a

is-a

is-a

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What a smart computer does:Represents knowledge and reasons with

it!

DOG SUGAR MAPLE

BREAD MOULD

INTESTINAL BACTERIUM

POND ALGAE

KINGDOM Animalia (animals)

Plantae (plants)

Fungi (fungi)

Prokaryoke (bacteria)

Protoctista (algae, protozoa, slim moulds)

PHYLUM Chordata Magnoliphyk Zygomycota Omnibacteria Chlorophyta

CLASS Mammalia Rosidae Zygomycetes Enterobacteria Evanjugatae

FAMILY Canidae Aceraceae Mucoraceae (E. coli does not have a family classification)

Zygnematale

GENUS Canis Acer Rhizopis Escherichia Zygnemataceae

SPECIES C. familaris A. saccharum R. stolonifer E. coli S. crassa

ORDER Carnivora Sapindale Macorales Eubacteriales Zygnematales

Modern taxonomy recognises five kingdoms, into which the five million species of the world are organised:

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What humans do? Create new knowledge and keep contradictions

in

Modern taxonomy recognises five kingdoms, into which the five million species of the world are organised and then goes onto incorporate, in this fixed species world,

EVOLUTION Cladistics, or phylogenetic systematics, for me and you! Willi Henning

proposed a method for implementing Darwin’s concepts of ancestors and

descendants in a taxonomic description.

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What humans do? Create new knowledge and keep contradictions

in

Modern taxonomy recognises five kingdoms, into which the five million species of the world are organised and then goes onto incorporate, in this fixed species world, EVOLUTION Cladistics, or

phylogenetic systematics, for me and you! Willi Henning proposed a method for implementing Darwin’s concepts of ancestors and descendants in a taxonomic description.

LAMPREY SHARK SALMON LIZARD D A B C

x

y

z t0

t1

t2

Lizard (LZ) & salmon (SN) are more closely related to each other is to shark (SK) LZ & SN share a common ancestor

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What humans think about what computers do?

Minerva is a robot which was evaluated in action by people outside a laboratory

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What humans think about what computers do?

Minerva is a robot which was evaluated in action by people outside a laboratory

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Language can be viewed as 'a communicative process based on knowledge. Generally when humans use language, the producer and comprehender are processing information, making use of their knowledge of the language and of the topics of conversation. Language is a process of communication between intelligent active processors, in which both the producer and the comprehender(s) perform complex cognitive tasks

Winograd, Terry. (1983). Language as a Cognitive Process. Wokingham: Addison-Wesley Inc.,

What humans do? Talk, listen, read and write

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Producer

Current Goals

Cognitive Processing

Knowledge Base

Knowledge of the language

Knowledge of the

situation

Knowledge of the world

Comprehender

Understood Meaning

Cognitive Processing

Knowledge Base

Knowledge of the language

Knowledge of the

situation

Knowledge of the world

Medium

Speech

or

Writing


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Phonlogical Rules

Morphological Rules

Dictionary (items)

Grammar Rules

Dictionary Definitions

Semantic Rules

Deductive Rules

Inferential Rules

Phonological

Morphological

Lexical

Syntactic (Parsing)

Semantic

Reasoning

Sounds

Phonemes

Morphemes

Words

Syntactic Structures

Representation Structures

Stored Knowledge

Processes Assigned Structures

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DEFINITIONS: Artificial Neural Networks In a restricted sense artificial neurons are simple emulations of biological neurons: the artificial neuron can, in principle, receive its input from all other artificial neurons in the ANN; simple operations are performed on the input data; and, the recipient neuron can, in principle, pass its output onto all other neurons.

Intelligent behaviour can be simulated through computation in massively parallel networks of simple processors that store all their long-term knowledge in the connection strengths.

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DEFINITIONS:Neurons & Appendages

AXONCELL BODY

NUCLEUS

DENDRITES

A neuron is a cell with appendages; every cell has a nucleus and the one set of appendages brings in inputs – the dendrites – and another set helps to output signals generated by the cell

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AXONCELL BODY

NUCLEUS

DENDRITES

A neuron is a cell with appendages; every cell has a nucleus and the one set of appendages brings in inputs – the dendrites – and another set helps to output signals generated by the cell

The Real McCoy

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The human brain is mainly composed of neurons: specialised cells that exist to transfer information rapidly from one part of an animal's body to another.

This communication is achieved by the transmission (and reception) of electrical impulses (and chemicals) from neurons and other cells of the animal. Like other cells, neurons have a cell body that contains a nucleus enshrouded in a membrane which has double-layered ultrastructure with numerous pores.

Neurons have a variety of appendages, referred to as 'cytoplasmic processes known as neurites which end in close apposition to other cells. In higher animals, neurites are of two varieties: Axons are processes of generally of uniform diameter and conduct impulses away from the cell body; dendrites are short-branched processes and are used to conduct impulses towards the cell body.

The ends of the neurites, i.e. axons and dendrites are called synaptic terminals, and the cell-to-cell contacts they make are known as synapses.

SOURCE:http://en.wikipedia.org/wiki/Neurons

Nucleus

Dendrite

Soma

AxonTerminals

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DEFINITIONS: The fan-ins and fan-outs

–

– +

summation

10 fan-in4

1 - 100 meters per sec.

Asynchronousfiring rate, c. 200 per sec.

10 fan-out4

1010 neurons with 104 connections and an average of 10 spikes per second = 1015 adds/sec. This is a lower bound on the equivalent computational power of the brain.

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ANN’s: an Operational View

Input S

ignals yk

Output

Signal

wk3

wk1

wk2

wk4

Neuron xk

x1

x2

x3

x4

bk

Summing Junction

Activation Function

)(

)(0

****

:

1

1

1

44332211

THRESHOLDnetifnety

THRESHOLDnetify

functionidentitynegativenon

netyfunctionidentity

outputNeuronal

xwxwxwxwnet

sumweightedorinputNet

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ANN’s: an Operational

View

A schematic for an 'electronic' neuron

ykwk3

wk1

wk2

wk4

Neuron xkx1

x2

x3

x4bk

Input Signals

Output

Signal

Summing Junction

Activation Function

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Biological and Artificial NN’s

Entity Biological Neural Networks

Artificial Neural Networks

Processing Units Neurons Network Nodes

Input Dendrites(Dendrites may form synapses

onto other dendrites)

Network Arcs(No interconnection

between arcs)

Output Axons or Processes(Axons may form synapses onto

other axons)

Network Arcs(No interconnection

between arcs)

Inter-linkage Synaptic Contact (Chemical and Electrical)

Node to Node via Arcs

Plastic Connections Weighted Connections Matrix

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Biological and Artificial NN’s

Entity Biological Neural Networks

Artificial Neural Networks

Output Dendrites bring inputs from different locations: so does the brain wait for

all the inputs and then start up the summing

exercise or does it perform many different intermediate

computations?

All inputs arrive instantaneously and are summed up in the same

computational cycle: distance (or location)

between neuronal nodes is not an issue.

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The McCulloch-Pitts Network

. McCulloch and Pitts demonstrated that any logical function can be duplicated by some network of all-or-none neurons referred to as an artificial neural network (ANN).

Thus, an artificial neuron can be embedded into a network in such a manner as to fire selectively in response to any given spatial temporal array of firings of other neurons in the ANN.

Artificial Neural Networks for Real Neuroscientists: Khurshid Ahmad, Trinity College, 28 Nov 2006

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The McCulloch-Pitts

Network

Consider a McCulloch-Pitts network which can act as a minimal model of the sensation of heat from holding a cold object to the skin and then removing it or leaving it on permanently.

Each cell has a threshold of TWO, hence fires whenever it receives two excitatory (+) and no inhibitory (-) signals from other cells at a previous time.

Artificial Neural Networks for Real Neuroscientists: Khurshid Ahmad, Trinity College, 28 Nov 2006

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The McCulloch-Pitts

Network

1

3

A

B

42

-

+

+

+ +

++

+ +

+

+

Heat

Receptors Cold

Hot

Cold

Heat Sensing Network

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The McCulloch-Pitts

Network

1

3

A

B

42

-

+

+

+ +

++

+ +

+

+

Heat

Receptors Cold

Hot

Cold


Time Cell 1 Cell 2 Cell a Cell b Cell 3 Cell 4

INPUT INPUT HIDDEN HIDDEN OUTPUT OUTPUT

1 No Yes No No No No

2 No No Yes No No No

3 No No No Yes No No

4 No No No No Yes No

Truth tables of the firing neurons when the cold object contacts the skin and is then removed

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The McCulloch-Pitts

Network


‘Feel hot’/’Feel cold’ neurons show how to create OUTPUT UNIT RESPONSE to given INPUTS that depend ONLY on the previous values. This is known as a TEMPORAL CONTRAST ENHANCEMENT.

The absence or presence of a stimulus in the PREVIOUS time cycle plays a major role here.

The McCulloch-Pitts Network demonstrates how this ENHANCEMENT can be simulated using an ALL-OR-NONE Network.

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Indexing and AnnotationIn multi-modal information processing, convergence of modalities is critical.

Surveillance experts become experts because they are very good at the convergence.

Surveillance experts LEARN how to mix modalities

From Alex Meredith, Virginia Commonwealth University, Virginia, USA

MultisensoryEnhancement

Cross-modalSuppression

Cross-modalFacilitation

Cross-modalFacilitationInhibition-Dependent

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词图 CITU (C2) SystemA multi-modal image annotation and retrieval system that can learn to annotate images using artificial neural network systems

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词图 CITU (C2) System

Manual annotation

Cross modal learning

Database

Image analysis

Image pre-processing

Image segmentati

on

Feature extraction

Language processing

Frequency analysis

Collocations

Feature extraction

Image content

Image feature

Free text

Linguistic feature

Image and

linguistic Features

Cross modal

associations

Image content and free

text

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Ontogenesis of numerosity

An unsupervised multinet alternative: Simulating Fechners’

Law

Ahmad K., Casey, M. & Bale, T. (2002). Connectionist Simulation of Quantification Skills. Connection Science, vol. 14(3), pp. 165-201.

GatingNetwork

CardinalNumber

Response

“Three”

VisualScene

SubitisingExpert

CountingExpert

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Law


CardinalNumber

VisualScene

648-dVector

15-d to36-d

Vector

Translation-InvariantWeight-Sharing

Network

72-15

Scale-InvariantSecond-Order

Network

648:72

72-dVector

MappingModule

MagnitudeKohonen SOM

36-dVector

Bi-directionalHebbianNetwork

64-dVector

36-36 x 1 36-64 16-8 x 8

VerbalKohonen SOM

MagnitudeRepresentation

64-dVector

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Simulation of Aphasic Naming

John Wright and Khurshid Ahmad (1997). ‘Connectionist Simulation of Aphasic Naming’. BRAIN AND LANGUAGE Vol. 59, pp 367–389 (1997)

A modular connectionist architecture was developed, in which semantic-lexical and phonological knowledge are instantiated using self-organising Kohonen maps, while connections between them are implemented using Hebbian networks; a linear connectionist network (Madaline) is used to simulate non-word repetition. The Hebbian connections were lesioned in order to reproduce the patient’s naming errors.

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One

Tw o ThreeFour

Five

Six

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Kohonen Layer Node Number

Avera

ge A

cti

vati

on


Law


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Ontogenesis of multi-modal behaviour

Jacob Martin, Alex M. Meredith, and Khurshid Ahmad. (2009) ‘Modeling multisensory enhancement with self-organizing maps’. Frontiers in Computational Neuroscience (June 2009), Volume 3 (Article 8), pp 1-10.

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Sentiment analysis of Irish Newspaper Texts and the Irish Stock Exchange Index.

0.00

0.02

0.04

0.06

0.08

0.10

0.12

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Years

Vol

atili

ty

ISEQ

Negative

Positive

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Computers and Brain: A neuroscience perspective

Alan Turing (1950) ‘Computer Machinery and Intelligence’. Mind Vol. LIX (No. 2236), pp 433-460.

“Professor Jefferson's Lister Oration for 1949, from which I quote.

"Not until a machine can write a sonnet or compose a concerto because of thoughts and emotions felt, and not by the chance fall of symbols, could we agree that machine equals brain-that is, not only write it but know that it had written it.

No mechanism could feel (and not merely artificially signal, an easy contrivance) pleasure at its successes, grief when its valves fuse, be warmed by flattery, be made miserable by its mistakes, be charmed by sex, be angry or depressed when it cannot get what it wants."

computers with brains? a neuroscience perspective

Documents

networks of neurons

behaviour of neurons

dendritic computation

michael husser

single neurons

cluster of neurons

annual review of neuroscience

computer scientists