Transcript Neurons and Their Connections

Cognitive Neuroscience
and Embodied Intelligence
Neurons and Their
Connections
Based on book Cognition, Brain and Consciousness ed. Bernard J. Baars
Janusz A. Starzyk
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Introduction
Neurons did not
change much for
millions of years
 The brain can be
viewed as a hyper
complex surface of
neurons.
 Sensory and motor
cortex are viewed as
processing hierarchies
of neurons.

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Introduction
A single neuron may
have thousands of
inputs (dendrites) and
one or more outputs
(axons).
 Neurons grow
extending their axons
and connecting to
other neurons in the
interconnected
structure

A bipolar neuron
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Neurons’ Growth

This growth can be
observed in the lab and
under stimuli the network
can learn a control function
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Real and idealized neurons
Neurons have been idealized
into the classical integrate and
fire neuron (right).
 In this neuron inputs from
dendrites are accumulated and
if total voltage value exceeds
-50 mV it triggers fast traveling
action potential in the cell’s
axon.
 Neuron sends its signal by
firing spikes from the cell body
to terminals synapses.

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Excitation and Inhibition
Classical neurons are
connected by excitatory and
inhibitory synapses.
 There are many classes of
neurons, neurochemicals,
and mechanisms for
information processing
 Many factors determine
neuron activity – the sleepwaking cycle, availability of
chemicals, and more.

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Excitation and Inhibition (cont.)
Transmission of signals through axons is assisted by
wrapping the axons in Myelinating Schwann cells.
 The cells improve the conduction velocity of signals.
 At the breaks known as the nodes of Ranvier, the
action potentials are regenerated.

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A Synapse
A spike in the
presynaptic cell
triggers release of
neurotransmitter that
diffuses across the
synaptic gap and
changes potential of
postsynaptic cell.
 Efficiency of signal
transmission
corresponds to
synaptic weigh in
network of neurons

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Working Assumptions
Neurons adds graded voltage inputs until total membrane
voltage exceeds -50 mV and then fires.
 Connections are either excitatory or inhibitory and its
strengths is represented by the connection weight. The
weight can be normalized between -1 and 1.
 Artificial neural networks that use simple neuron models can
be used for pattern recognition or unknown function
approximation.
 Neurons can form one-way or bidirectional pathways to
transfer information from one part of the brain to other.
 Cortex is a massive 6-layer array of neurons. Arrays of
neurons are called maps.
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 Stable collections of neurons form Hebbian cell assemblies

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A simple reflex circuit
An example of a spinal
(knee-jerk) reflex.
 Sensory neurons pick
up the tap and transmit
it to the spinal court.
 An interneuron links
the sensory impulses
to motor neurons
(bypassing higher level
brain function) making
the leg jump

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A simple reflex circuit
While reflex circuits can be triggered by outside stimuli,
they are normally integrated into voluntary, goal driven
activities.
 In many time this is unconscious and almost automatic.
 Voluntary goal driven brain mechanisms, are associated
with cortex.
 Sophisticated subcortical activity is also engaged in
planning and executing actions.
 Spinal centers also communicate with higher centers
while carrying sensorimotor reflexes and return
feedback signals to brain.

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Different types of receptors
There
are several types of
receptors, however, they are
all similar in structure and
function.
Sensory nerves have
parallel pathways sending
sensory information to
thalamus and sending back
feedback information
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90% of neurons go
backwards towards
the source
Most sensory and
motor pathways split
and cross over the
midline of the body
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Similarities between sensor pathways

This image shows
the similarities
between the
different sensory
streams.
arm vs. leg,
high frequency vs. low
frequency, and
foveal vs. peripheral
vision.
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Sensory Interactions
Sensory regions interact with thalamic nuclei (RTN)
Notice similarities between cortical input and output layers
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in all these senses
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Lateral Interactions
Lateral
This
inhibition is used to differentiate between neighboring cells
gives better resolution at various levels of sensory perception

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In retina it helps to spot a tiny point
At higher level it helps to differentiate e.g. between ‘astronomy’ and
‘astrology’
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Lateral Interactions
Visual
demonstration of
lateral inhibition
Notice
that lateral
inhibition applies to
adjacent black
squares, color
perception, and
even perception of
direction
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Mapping of the brain


Visual quadrants map to
cortical quadrants
Mapping is observed for
various senses
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Neuron organization
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Neurons organize into layers. The figure below
shows a single layer of pyramid neurons at 200
micrometers.
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Visual Maps

Neuron connections form various pathways

In V1 the upper pathway is sensitive to location ‘where
The lower pathway is sensitive to color, shape contrast and object
identity ‘what’
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Layers have 2-way connections

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Neuronal layers have both feed-forward and feedback
connections between layers/arrays.
Lower levels tend to be sensitive to simpler stimuli, while higher
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levels respond to more complex stimuli.
Sensory and motor hierarchies

Sensory and motor
systems appear to be
arranged in hierarchies
with information
flowing between each
level of the sensory
and motor hierarchies.
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Ambiguous stimuli
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Ambiguous stimuli pose choices for interpretation. It
all depends on how the image is perceived and what
ever preconceived notions you may have.
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Hebbian Learning
“Neurons the fire
together, wire together”
 Long term potentiation
(LTP) and long term
depression (LTD)
 The figure depicts
Hebbian learning in cell
assemblies.



At t1 input is encoded into
connection weights.
Memory is retained at times t2
& t3.
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A simple network

Note the
combination of
excitory and
inhibitory
connections.
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A Three Layer Network
Hidden layer
makes the network
more flexible
 Backpropagation is
used to adjust
network weights to
match the input to
a desired output.

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A pattern recognition network
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An example of an auto-associative network that
matches its output with its input.
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A self-organizing network

Self-organizing
networks appear
often in biological
organisms.

A self-organizing
network can be
used for face
recognition.
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Neural Darwinism
Gerald Edelman proposed that brain is a massive
selectionist organ where neurons develop and make
connections following Darwinian principle of selection of
the fittest.
 In biological evolution, species adapt by reproduction,
mutation that leads to diverse forms, and selection.
 A similar process occurs in the immune system, where
millions of immune cells adapt to invading toxins.
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 Thus selectionism leads to flexible adaptation.
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Neural Darwinism

Selectionist learning principle was used to train simulated
rat to find a hidden platform in a Morris water maze

The second figure shows a selectionist robot that learns to
play soccer.
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Symbolic Processing
Neural nets can handle
both distributed numerical
values as well as
symbolic expressions.
 The figure shows proposed by
McClelland and Rogers merge
between symbolic features and
their associations expressed by
connections of a neural network
 Brain uses adaptation and
representation to learn the
world.

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Coordinating Neural Nets
Neurons’ activation is coordinated by
large-scale rhythms to signify their
activities.
 Epileptic seizures are also caused by
slow, intense, regular waves that lead
to a loss of consciousness
 Thus there must be a balance between
integration and differentiation.
 A high density of gamma rhythms has been related to conscious visual
perception and understanding of spoken words.
 Alpha rhythms are associated with an absence of focused attentional
tasks.
 Theta rhythms coordinate hippocampal region and the frontal cortex
during retrieval of memories.
 And delta rhythms signal deep sleep, are believed to group fast
neuronal activities to consolidate learned events.
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Coordinating Neural Nets
This figure illustrates hypothesis how brain rhythms coordinate large
number of neuron cells’ firing.
 Neurons that fire in synch with the dominant rhythm are strengthened
by feedback from many other neurons, while those that fire out of 32
synch are weakened.
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Summary
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The basic question in cognitive neuroscience is how the nerve
cells work together to perform cognitive functions like perception,
memory and action.
Models of neurons were developed and used to build functional
processing networks.
Artificial neural networks and biologically inspired networks are
useful to study cognitive processing.
Sensory and motor systems are complex hierarchies of neurons
organized in two or three dimensional arrays.
In vision, touch and motor control arrays of neurons are
topographically arranges as maps of the spatial surroundings.
Hierarchies are bidirectional pathways, that allow signals to travel
up, down and laterally.
A major function of downwards pathway is to resolve sensory
ambiguities.
Lateral inhibition is used to emphasize differences between
inputs.
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