Transcript Neurons and their Connections

Cognitive Architectures
Neurons and Their
Connections
Based on book Cognition, Brain and Consciousness ed. Bernard J. Baars
Janusz A. Starzyk
EE141
1
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.

2
EE141
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
3
EE141
Neurons’ Growth

This growth can be
observed in the lab and
under stimuli the network
can learn a control function
4
EE141
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.
5
EE141
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.
6
EE141
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.
EE141
7
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
8
EE141
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.
Stable collections of neurons form Hebbian cell assemblies
9
EE141
A simple reflex circuit

An example of a spinal
(knee-jerk) reflex.

Sensory neurons pick
up the tap and transmit
it to the spinal cord.

An interneuron links
the sensory impulses
to motor neurons
 bypassing higher level
brain function and
making the leg jump
10
EE141
A simple reflex circuit





While reflex circuits can be triggered by
outside stimuli, they are integrated into
voluntary, goal driven activities.
Many times 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 communicate with higher
centers while carrying sensorimotor
reflexes and return feedback signals to
brain.
11
EE141
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


90% of neurons go
backwards towards the
source
Most sensory and motor
pathways split and
cross over the midline of
the body
12
EE141
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.
13
EE141
Sensory Interactions
Sensory regions interact with thalamic nuclei (RTN)
Notice similarities between cortical input and output layers
14
in all these senses
EE141
Lateral Interactions
Lateral
This
inhibition is used to differentiate between neighboring cells
gives better resolution at various levels of sensory perception


In retina it helps to spot a tiny point
At higher level it helps to differentiate e.g. between ‘astronomy’ and
‘astrology’
15
EE141
Lateral Interactions
Visual
demonstration of
lateral inhibition
Notice
that lateral
inhibition applies to
adjacent black
squares, color
perception, and
even perception of
direction
16
EE141
Mapping of the brain


Visual quadrants map to
cortical quadrants
Mapping is observed for
various senses
17
EE141
Neuron organization

EE141
Neurons organize into layers. The figure below
shows a single layer of pyramid neurons at 200
micrometers.
18
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’

EE141
19
Layers have 2-way connections


EE141
Neuronal layers have both feed-forward and feedback
connections between layers/arrays.
Lower levels tend to be sensitive to simpler stimuli, while higher
20
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.
21
EE141
Ambiguous stimuli

EE141
Ambiguous stimuli pose choices for interpretation. It
all depends on how the image is perceived and what
ever preconceived notions you may have.
22
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.
23
EE141
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.

24
EE141
A pattern recognition network

EE141
An example of an auto-associative network that
matches its output with its input.
25
A self-organizing network

Self-organizing
networks appear
often in biological
organisms.

A self-organizing
network can be
used for face
recognition.
26
EE141
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.
Thus selectionism leads to flexible adaptation.
27
EE141
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.

28
EE141
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.
29

EE141
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 30
synch are weakened.
EE141

Summary








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 arranged 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.
31
EE141