20 Minute Quiz

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20 Minute Quiz

• For each of the questions, you can use text,
  diagrams, bullet points, etc.
• Give three processes that keep neural computation
  from proceeding faster than at millisecond scale.
• How does pre-natal activity dependent visual
  tuning work?
• How does the knee-jerk reflex work? Describe
  another similar reflex found in humans.
• Identify two philosophical issues discussed in
  the assigned chapters of the M2M book.

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How does activity lead to structural change?

• The brain (pre-natal, post-natal, and adult)
  exhibits a surprising degree of activity
  dependent tuning and plasticity.
• To understand the nature and limits of the tuning
  and plasticity mechanisms we study
• How activity is converted to structural changes
  (say the ocular dominance column formation)
• It is centrally important
• to arrive at biological accounts of perceptual,
  motor, cognitive and language learning
• Biological Learning is concerned with this topic.

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Learning and Memory Introduction
facts about a situation
general facts
skills
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Skill and Fact Learning may involve different
mechanisms

• Certain brain injuries involving the hippocampal
  region of the brain render their victims
  incapable of learning any new facts or new
  situations or faces.
• But these people can still learn new skills,
  including relatively abstract skills like solving
  puzzles.
• Fact learning can be single-instance based. Skill
  learning requires repeated exposure to stimuli
• subcortical structures like the cerebellum and
  basal ganglia seem to play a role in skill
  learning
• Implications for Language Learning?

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Models of Learning

• Hebbian coincidence
• Recruitment one trial
• Supervised correction (backprop)
• Reinforcement delayed reward
• Unsupervised similarity

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Hebbs Rule

• The key idea underlying theories of neural
  learning go back to the Canadian psychologist
  Donald Hebb and is called Hebbs rule.
• From an information processing perspective, the
  goal of the system is to increase the strength of
  the neural connections that are effective.

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Hebb (1949)

• When an axon of cell A is near enough to excite
  a cell B and repeatedly or persistently takes
  part in firing it, some growth process or
  metabolic change takes place in one or both cells
  such that As efficiency, as one of the cells
  firing B, is increased
• From The organization of behavior.

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Hebbs rule

• Each time that a particular synaptic connection
  is active, see if the receiving cell also becomes
  active. If so, the connection contributed to the
  success (firing) of the receiving cell and should
  be strengthened. If the receiving cell was not
  active in this time period, our synapse did not
  contribute to the success the trend and should be
  weakened.

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LTP and Hebbs Rule

• Hebbs Rule neurons that fire together wire
  together
• Long Term Potentiation (LTP) is the biological
  basis of Hebbs Rule
• Calcium channels are the key mechanism

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Chemical realization of Hebbs rule

• It turns out that there are elegant chemical
  processes that realize Hebbian learning at two
  distinct time scales
• Early Long Term Potentiation (LTP)
• Late LTP
• These provide the temporal and structural bridge
  from short term electrical activity, through
  intermediate memory, to long term structural
  changes.

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Calcium Channels Facilitate Learning

• In addition to the synaptic channels responsible
  for neural signaling, there are also
  Calcium-based channels that facilitate learning.
• As Hebb suggested, when a receiving neuron fires,
  chemical changes take place at each synapse that
  was active shortly before the event.

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Long Term Potentiation (LTP)

• These changes make each of the winning synapses
  more potent for an intermediate period, lasting
  from hours to days (LTP).
• In addition, repetition of a pattern of
  successful firing triggers additional chemical
  changes that lead, in time, to an increase in the
  number of receptor channels associated with
  successful synapses - the requisite structural
  change for long term memory.
• There are also related processes for weakening
  synapses and also for strengthening pairs of
  synapses that are active at about the same time.

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LTP is found in the hippocampus
Essential for declarative memory (Episodic
Memory) In the temporal lobe Cylindrical
Structure
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The Hebb rule is found with long term
potentiation (LTP) in the hippocampus
Schafer collateral pathway Pyramidal cells
1 sec. stimuli At 100 hz
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During normal low-frequency trans-mission,
glutamate interacts with NMDA and non-NMDA (AMPA)
and metabotropic receptors.
With high-frequency stimulation
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Enhanced Transmitter Release
AMPA
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Early and late LTP

• (Kandel, ER, JH Schwartz and TM Jessell (2000)
  Principles of Neural Science. New York
  McGraw-Hill.)
• Experimental setup for demonstrating LTP in the
  hippocampus. The Schaffer collateral pathway is
  stimulated to cause a response in pyramidal cells
  of CA1.
• Comparison of EPSP size in early and late LTP
  with the early phase evoked by a single train and
  the late phase by 4 trains of pulses.

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Computational Models based onHebbs rule

• Many computational systems for modeling
  incorporate versions of Hebbs rule.
• Winner-Take-All
• Units compete to learn, or update their weights.
• The processing element with the largest output is
  declared the winner
• Lateral inhibition of its competitors.
• Recruitment Learning
• Learning Triangle Nodes
• LTP in Episodic Memory Formation

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A possible computational interpretation of Hebbs
rule
j
wij
i
How often when unit j was firing, was unit i also
firing?
Wij number of times both units i and j were
firing --------------------------------
---------------------- number of times
unit j was firing
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Extensions of the basic idea

• Bienenstock, Cooper, Munro Model (BCM model).
• Basic extensions Threshold and Decay.
• Synaptic weight change proportional to the
  post-synaptic activation as long as the
  activation is above threshold
• Less than threshold decreases w
• Over threshold increases w
• Absence of input stimulus causes the postsynaptic
  potential to decrease (decay) over time.
• Other models (Ojas rule) improve on this in
  various ways to make the rule more stable
  (weights in the range 0 to 1)
• Many different types of networks including
  Hopfield networks and Boltzman machines can be
  trained using versions of Hebbs rule

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Winner take all networks (WTA)

• Often use lateral inhibition
• Weights are trained using a variant of Hebbs
  rule.
• Useful in pruning connections
• such as in axon guidance

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WTA Stimulus at is presented
1
2
a
t
o
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Competition starts at category level
1
2
a
t
o
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Competition resolves
1
2
a
t
o
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Hebbian learning takes place
1
2
a
t
o
Category node 2 now represents at
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Presenting to leads to activation of category
node 1
1
2
a
t
o
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Presenting to leads to activation of category
node 1
1
2
a
t
o
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Presenting to leads to activation of category
node 1
1
2
a
t
o
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Presenting to leads to activation of category
node 1
1
2
a
t
o
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Category 1 is established through Hebbian
learning as well
1
2
a
t
o
Category node 1 now represents to
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Connectionist Model of Word Recognition
(Rumelhart and McClelland)
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Recruiting connections

• Given that LTP involves synaptic strength changes
  and Hebbs rule involves coincident-activation
  based strengthening of connections
• How can connections between two nodes be
  recruited using Hebbss rule?

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X
Y
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X
Y
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Finding a Connection
P (1-F) BK

• P Probability of NO link between X and Y
• N Number of units in a layer
• B Number of randomly outgoing units per unit
• F B/N , the branching factor
• K Number of Intermediate layers, 2 in the
  example

N
106 107
108
K
Paths (1-P k-1)(N/F) (1-P k-1)B
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Finding a Connection in Random Networks
For Networks with N nodes and branching
factor, there is a high probability of finding
good links. (Valiant 1995)
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Recruiting a Connection in Random Networks

• Informal Algorithm
• Activate the two nodes to be linked
• Have nodes with double activation strengthen
  their active synapses (Hebb)
• There is evidence for a now print signal based
  on LTP (episodic memory)

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Triangle nodes and recruitment
Posture
Push
Palm
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Recruiting triangle nodes
WTA TRIANGLE NETWORK
CONCEPT UNITS
A
L
B
recruited
C
K
free
E
G
F
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Has-color
Has-shape
Green
Round
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Has-color
Has-shape
GREEN
ROUND
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Hebbs rule is not sufficient

• What happens if the neural circuit fires
  perfectly, but the result is very bad for the
  animal, like eating something sickening?
• A pure invocation of Hebbs rule would strengthen
  all participating connections, which cant be
  good.
• On the other hand, it isnt right to weaken all
  the active connections involved much of the
  activity was just recognizing the situation we
  would like to change only those connections that
  led to the wrong decision.
• No one knows how to specify a learning rule that
  will change exactly the offending connections
  when an error occurs.
• Computer systems, and presumably nature as well,
  rely upon statistical learning rules that tend to
  make the right changes over time. More in later
  lectures.

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Hebbs rule is insufficient

• should you punish all the connections?

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So what to do?

• Reinforcement Learning
• Use the reward given by the environment
• For every situation, based on experience learn
  which action(s) to take such that on average you
  maximize total expected reward from that
  situation.
• There is now a biological story for reinforcement
  learning (later lectures).

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Models of Learning

• Hebbian coincidence
• Recruitment one trial
• Next Lecture Supervised correction (backprop)
• Reinforcement delayed reward
• Unsupervised similarity

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Constraints on Connectionist Models

• 100 Step Rule
• Human reaction times 100 milliseconds
• Neural signaling time 1 millisecond
• Simple messages between neurons
• Long connections are rare
• No new connections during learning
• Developmentally plausible

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5 levels of Neural Theory of Language
Spatial Relation
Motor Control
Pyscholinguistic experiments
Metaphor
Grammar
Cognition and Language
Computation
Structured Connectionism
abstraction
Neural Net and learning
SHRUTI
Triangle Nodes
Computational Neurobiology
Biology
Neural Development
Midterm
Quiz
Finals