Transcript Mind and Brain - Ohio University

Cognitive Neuroscience
and Embodied Intelligence
Introduction to Cognitive
Neuroscience
Based on book Cognition, Brain and Consciousness ed. Bernard J. Baars
courses taught by Prof. Randall O'Reilly, University of Colorado, and
Prof. Włodzisław Duch, Uniwersytet Mikołaja Kopernika
and http://wikipedia.org/
http://grey.colorado.edu/CompCogNeuro/index.php/CECN_CU_Boulder_OReilly
http://grey.colorado.edu/CompCogNeuro/index.php/Main_Page
Janusz A. Starzyk
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… from the brain, and from the brain alone, arise our
pleasures, joys, laughter and jokes, as well as our
sorrows, pains, grief's and tears. Through it, in
particular, we think, see, hear, and distinguish the ugly
from the beautiful, the bad from the good, the pleasant
from the unpleasant…
» Attributed to Hippocrates, 5th century BC
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The Brain ...

The most interesting and the most complex
object in the known universe

How can we understand the workings of
the brain?

On what level should we attack this
question? An external description won’t
help much.

How can we understand the workings of a TV or computer?

Experiments won’t suffice, we must have an understanding of the
operating principles.

To verify that we understand how it works, we must make a model.
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How do we know anything?
An important question: how do we know things?
Examples: super diet based on dr. K, Chinese medicine
and other miracle methods. How do we know that
they work? How do we know that they are for real?
Gall noticed that the skull shape decides about ones
abilities. Thousands of cases confirmed his observations.
Craniometry: measuring the bones of the skull
to determine intelligence.
Do I know or I only believe that I know?
Not being certain allows to learn, certainty makes
learning difficult. If we know how easy it is to err
we could avoid a scientific fallacy.
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How to understand the brain?
To understand: reduce to simpler mechanisms?
Which mechanisms? Analogies with computers? RAM, CPU? Logic?
Those are poor analogies.
Psychology: first you must describe behavior, it looks for explanations
most often on a descriptive level, but how to understand them?
Physical reductionism: mechanisms of the brain.
Reconstructionism: using mechanisms to reconstruct the brain’s functions
To create: what must we know in order to create an artificial brain?
We can answer many questions only from an ecological and evolutionary
perspective: why is the world the way it is? Because that’s how it made
itself ... Why does the cortex have a laminar and columnar structure?
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From molecules through neural networks
10-10 m, molecular level: ion channels, synapses, properties of cell
membranes, biophysics, neurochemistry, psychopharmacology;
10-6 m, single neurons: neurochemistry, biophysics, LTP,
neurophysiology, neuron models, specific activity detectors,
emerging.
10-4 m, small networks: synchronization of neuron activity, recurrence,
neurodynamics, multistable systems, pattern generators, memory,
chaotic behaviors, neural encoding; neurophysiology ...
10-3 m, functional neural groups: cortical columns (104-105), group
synchronization, population encoding, microcircuits, Local Field
Potentials, large-scale neurodynamics, sequential memory,
neuroanatomy and neurophysiology.
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… to behavior
10-2 m, mesoscope networks: sensory-motor maps, self-organization,
field theory, associative memory, theory of continuous areas, EEG,
MEG, PET/fMRI imaging methods ...
10-1 m, transcortical fields, functional brain areas: simplified cortical
models, subcortical structures, sensory-motor functions, functional
integration, higher psychic functions, working memory,
consciousness; (neuro)psychology, psychiatry ...
Cognitive effects
Principles of
interactions
Neurobiological
mechanisms
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… to the mind
Now a miracle happens ...
1 m, CNS, the whole brain and organism:
An interior world arises, intentional behaviors, goal-oriented
actions, thought, language, everything that behavioral psychology
examines.
Approximations of neural models:
Finite State Machine, rules of behavior, models based on the
knowledge of cognitive mechanisms in artificial intelligence.
What happened to the psyche, the internal perspective?
Lost in translation: neurons => networks => behavior
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… to the mind
“What if … we were magically shrunk and
put into someone’s brain while he was
thinking. We would see all the pumps,
pistons, gears, and levers working away,
and we would be able to describe their
working completely, in mechanical terms,
thereby completely, describing the thought
process of the brain. But that description
would nowhere contain any mention of
thought! It would contain nothing but
descriptions of pumps, pistons, levers!’
- Gottfried Leibnitz 1690
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Levels of description
Summary (Churchland, Sejnowski 1988)
Sensing outside and inside the body
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Distance – from 10-10 m to one meter
 Small molecules can change brain functions and resulting behavior.
 Around year 1800 people were surprised to find out that nitrous
oxide (N2O) changes their behavior – it produces small amount of
neurotransmitter.
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Time scales - 10 orders of magnitude
 Neurons can fire as fast as 1000 Hz.
 Our brain deals with events on the time
range from years to milliseconds.
 100ms is about the fastest we can react to
an event.
– Slower reaction time would prevent humans from
protecting themselves from dangers and they would
have no chance to survive and reproduce,
– faster reaction time would overwhelm the brain to
combine sensory inputs and determine the direction
and speed of the attacker.
 Some skills take long time to master like
playing guitar or learning how to speak.
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Making Inferences

Inferences explanatory concepts
from raw observations
- play an important
role in science.

Figure showing
relation between
observations:
lights seen in the sky,
and the inferences
drawn,
path of the planets
around the sun.
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Working Memory

Observations, based on experimental data, are important in
cognitive science.

Concepts like working
memory and their size
(7+/-2) are not ‘given’
in nature but are
inferred from
experimental
observations.
Emerge from years of
testing, working
memory proposed
after a 2 decade study
of immediate memory

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Working Memory Models

EEG (Electroencephalography), fMRI (Functional
Magnetic Resonance Imaging), etc are inferential
measurements of brain.

Results for working memory
converge well with
behavioral measurement.

Combined sources of
evidence are widely used
for study in cognitive
neuroscience
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Neurocognitive Models
Computational cognitive neuroscience: detailed models of
cognitive functions and neurons.
Neurocognitive computing: simplified models of higher
cognitive functions, thinking, problem solving, attention,
language, cognitive and behavioral controls.
Example models:
Self-organization, dynamic net or biophysical spiking neurons.
Lots of speculations, but qualitative models explaining the results of
psychological experiments as well as the causes of mental illnesses are
developing quickly.
Even simple brain-like information processing yields results similar to the
real ones! Warning against excessive optimism based on behavioral
models.
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Model of self-organization
Topographical representations in numerous areas of
the brain:
sensory impulses, multimodal maps of orientation,
visual system maps and maps of the auditory cortex.
o
Model (Kohonen 1981):
competition between groups of
neurons and local cooperation.
x=data
o=weights of
neurons
x
o
o
o
o x
o
o
x
o
xo
N-dimensional
input space
o
o
o
Neurons react to signals
adjusting their parameters so
that similar impulses awaken
neighboring neurons.
Weights locate
points in N-D
neural network
w 2-D
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Dynamic model
Strong feedback, neurodynamics.
Hopfield model: associative memory, learning based on Hebb’s law,
synchronized dynamics, two-state neurons.
Vector of input potentials V(0)=Vini , i.e.
input = output.
Dynamics (iterations) 
Hopfield’s network reaches stationary
states, or the answers (vectors of
elemental activation) of the network to the
posed question Vini (autoassociation).
If the connections are symmetrical then
such a network trends to a stationary state
(local attractor).

Vi  t  1  sgn I i  t  1  sgn 
t = discrete time.

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

j WijV j   j 

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Biophysical model – spiking neurons
Synapses
Soma
I syn (t )
Spike
EPSP, IPSP
Rsyn
Csyn
Spike
Cm
Rm
“Spiking Neuron Models”,
W. Gerstner and W. Kistler
Cambridge University Press, 2002
http://icwww.epfl.ch/~gerstner//SPNM/SPNM.html
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Abstract neuron
Add all inputs considering synaptic strength.
Neurons activation cannot grow indefinitely: pass the total net input
through a sigmoidal limiting function:
Output activation does not exceed a
unit vale.
Is this how neurons respond? No, but
this is how the average number of
impulses per second changes as a
function of neuron’s activation.
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Molecular foundations
Action potentials are the result of currents
which flow through the ionic channels in
the cell membrane
Hodgkin and Huxley measured these
currents and described their dynamics
through differential equations.
-70mV
Na+
Action
potential
K+
Ca2+
Ions/protein
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Biological Neural Nets
Ions
flow through the neurons’ membranes
under the forces of electricity and concentration
gradients, changing their polarization Vm
.Sodium ions Na+, potassium K+, calcium C++, chloride Cl flow to
equalize the charge distribution; their imbalance creates the electrical
potential which restores the balance.
Ions flow through channels finding resistance I = V/R:
Conductivity is G=1/R, so I=VG (Ohms law)
Dyffusion generates current I in proportion to
ion concentration C
I = DC
(Fick's First law)
Equilibrium potential E counteracts diffusion:
I = G(VE)
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Ions and Neurons

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Glutamic acid opens Na+ channels, (excitatory),
GABA works on Cl- channels inhibiting excitation.
Liquid outside neurons similar to a sea water and contains: NaCl, KCl.
Sodium pomp polarizes membranes: removes sodium idons, and brings in
potassium ions. Sodium ions dominate, so the resting potential is +
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outside
and
–70mV
inside.
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Ions and Neurons


Glutamic acid opens Na+ channels, (excitatory),
GABA works on Cl- channels inhibiting excitation.
Liquid outside neurons similar to a sea water and contains: NaCl, KCl.
Sodium pomp polarizes membranes: removes sodium idons, and brings in
potassium ions. Sodium ions dominate, so the resting potential is +
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outside
and
–70mV
inside.
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Ions and Neurons
follows from the sodium pump, which creates the “dynamic
tension” for subsequent neural action.
Glutamateopens Na+ channelsNa+ enters (excitatory)
GABAopens Cl- channelsCl- enters if Vm < threshold (inhibitory)
Alcohol closes sodium channels Na.
General anesthesia: opens K.
Scorpions have various toxins, eg. opens Na, closes K.
Everything
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Putting it Together
To find potential Vm for each ion one needs to consider its equilibrium
potential Ec,
gc(t) is a fraction of ion channels that are open in a given moment,
ĝc is max. conductivity of all channels;
therefore a produce ĝc gc(t) gives us conductivity.
Considering diffusion, current for a given ion is:
Ic = ĝc gc(t) (Vm(t)Ec)
For equilibrium
potential Ic=0
Total current for 3 most
important channels:
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It's Just a Leaky Bucket
Good analogy:
ge rate of flow into bucket;
gi and gl rate of “leak” out of bucket.
Resulting water level?
Vm balance between these forces.
Or like a tug-of-war (rope pooling)
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Two inputs
As a result of the current flow Inet potential changes with some time
delay dtvm:
Two excitatory inputs at time t =10, assuming conductances
ge=ĝege(t) = 0.2 i 0.4 and gl=2.8
Current flows, buts stops at equilibrium. If there are constant
excitations (open channels) neuron reaches new equilibrium potential.
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Overall Equilibrium Potential
If Inet=0 then we can computeVm (normalized threshold 0.25):
Can now solve for the
equilibrium potential as a
function of inputs.
Simplify: ignore inhibition
for a moment, set Ee=1 a
Ei=0 (leaks always on El=1)
Membrane potential computes
a balance (weighted average)
of excitatory and inhibitory
inputs.
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Computational Neurons (Units) Summary
Use bias b and activation threshold Q to compute the output signal
with the total number of input connections N ([ . ]+ is a positive part):
Weights = synaptic efficacy;
weighted input = xiwij.
Net conductances (average
across all inputs)
excitatory (net = ge(t)),
inhibitory gi(t).
Function gx/(gx+1) combined with
Gassian noise is similar to sigmoidal,
Parameter g regulates the slope.
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Thresholded Spike Outputs
Neuron’s behavior is a result of currents equilibrium in
various channels.
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Parameters
Emergent allows to simulate
neurons with realistic
parameters.
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Emergent:
Emergent is a powerful tool for simulation of
biologically plausible, complex neural networks:
http://grey.colorado.edu/emergent
Emergent supports:
Simulating the brain functions
Classic back-propagation and recurrent back-propagation and variants,
Constraint-satisfaction (CS) including the Boltzmann Machine,
Interactive Activation and Competition, and other related algorithms;
Self-organized learning including Hebbian Competitive learning and
variants, with Kohonen's Self-Organizing Maps and variants
Leabra (``local error-driven and biologically realistic algorithm'')
Real Time Neural Simulator
Long Short Term Memory
Oscillating Inhibition Learning Mechanism
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Emergent and Other Simulators:
Emergent started as PDP (parallel
distributed processing) developed by
McClelland and Rumelhart in 1986
Other simulators:
GENESIS
NEURON
NEST
XPPAUT
SPLIT
Mvaspike
SNNS
Topographica NMS
FANN
Commercial simulators:
Matlab NN toolbox
Mathematica NN package
Peltarion Synapse
Robotics simulations with rigid body physics
Emergent is compared to other neural network simulators at:
http://grey.colorado.edu/emergent/index.php/Comparison_of_Neural_Network_Simulators
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Emergent: Unit.Proj
Emergent allows to simulate neurons with realistic parameters.
act_fun activation function: Noisy xx1; can be chosen without noise,
linear with or without noise, or spike.
g_bar_e determines fraction of channels is open during on/off_cycle.
e_rev_e is equilibrium potential for activating channels.
a = accommodation, increase of Ca++ concentration in neuron
=> opens inhibitory channels (usually K+) – section 2.9.
h = hysteresis, reaction slowdown, active neurons remain active for
some time after the excitation is removed.
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Emergent: results
Net = total excitation changes
from 0 to g_bar_e=1 (all channels
open).
I_net: current flows to the neuron,
equilibrium is reached, then it
flows out of the neuron.
V_m is axon potential, increases
from -70mV (here 0.15) to +50mV
(here 0.30).
Act = output activation; if spikes
are selected then they will show
on the figure
Single impulses; fluctuations result form noise,
here there is a small noise variance = 0.001
Act eq = equivalent of average rate-code.
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Advantages of model simulations
Models help to understand phenomena:
 enable new inspirations, perspectives on a problem
 allow to simulate effects of damages and disorders (drugs, poisoning)
 help to understand behavior
 models can be formulated on various levels of complexity
 models of phenomena overlapping in a continuous fashion (e.g. motion
or perception)
 models allow detailed control of experimental conditions and exact
analysis of the results
Models require exact specification of underlying assumptions:
 allow for new predictions
 perform deconstructions of psychological concepts (working memory?)
 allow to understand the complexity of a problem
 allow for simplifications enabling analysis of a complex system
 provide a uniform, cohesive plan of action
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Disadvantages of simulations
One must consider limitations of designed models:
 Models are often too simple, they should contain many levels.
 Models can be too complex, theory may give simpler explanation
 why there are no hurricanes on the equator? - due to Coriolis effect



It’s not always known what to provide for in a model.
Even if models work, that doesn’t mean that we understand the
mechanisms.
Many alternative yet very different models can explain the same
phenomenon.
Models need to be carefully designed to fit the observations:
 What’s important in building a model are general rules
 the more phenomena a model explains, the more plausible and universal it is.


Allowing for interaction and emergence (construction) is very
important.
Knowledge acquired from models should undergo accumulation.
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Cognitive motivation

Although the thinking process seems to be sequential information
processing, more detailed models predict parallel processing
 Gradual transition between conscious and subconscious processes
 Parallel processing of sensory-motor signals by tens of millions of
neurons

Specialized areas of memory responsible for various representations
e.g. shape, color, space, time
 Levels of symbolic representation
 More diffuse than binary logic

Learning mechanisms as a foundation for cognitive science
 When you learn, you change the method of information processing in
your brain
Resonance between ”bottom-up” representation and ”top-down”
understanding
 Prediction and competition of ideas

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Brain Landmarks


Most terms in
neuroscience are
Latin names, as it
was the language of
science.
Medial (midline) view
of the brain also
called mid-sagittal
section of the brain is
a slice from the nose
to the back of the
head.
Corpus callosum is a fiber bridge flowing between right and
left hemispheres, begins behind the frontal lobe and loops up
and ends in front of the cerebellum.
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Brain Landmarks

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

Lateral (side) view of left
hemisphere is shown
here.
Folds in the cortex are
important part of
anatomy.
Longitudinal fissure runs
along the midline
between right and left
hemispheres.
Lateral sulcus runs forward at a slant along the side of the brain
and divides the temporal lobe from the main cortex.
Central sulcus divides the rear half (posterior half) of the brain
from the frontal lobe.
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Brain Landmarks


Temporal lobe points in the direction of
the eye.
The three major planes of section (cuts)
are:
 Vertical section (sagittal) from the front of
the brain to the back.
– Slice through the midline is called midsagittal.
 Horizontal slice.
 Coronal section (named for its crown
shape).
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Body Landmarks

The three major
planes of section :
 Vertical section
(sagittal) from the
front to the back.
 Horizontal
(transverse) section.
 Frontal (coronal in
the brain) section.

Other important directions:
 Superior (dorsal) and inferior (ventral)
 Medial and lateral
 Anterior (rostral) and posterior (caudal).
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Mind and Brain
Visual perception: viewing natural imagery
we must understand ways of encoding
objects and scenes.
Spatial awareness: considering the interaction
between streams of visual information will let
us simulate concentration
Memory: modeling hippocampal structures allows us to understand
various aspects of episodic memory, and learning mechanisms show how
semantic memory arises.
Working memory: explaining the capacity to simultaneously hold in the
mind several numbers, while performing calculations requires specific
mechanisms in the neural model.
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Mind and Brain
Reading words: the network model in Emergent will learn to read and
pronounce words and then to generalize its knowledge to the
pronunciation of new words as well as to recreate certain forms of
dyslexia.
Semantic representations: analyzing a text on the basis of context, the
appearance of individual words, the network will learn the semantics of
many ideas.
Decision-making and task execution:
A model of the prefrontal cortex will be
able to keep attention on performed
tasks in spite of hindering variables.
Development of the representation of
the motor and somatosensory cortex:
through learning and controlled selforganization;
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Mind and Brain
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Andreas Vesalius (1514-1564), a
Belgian physician, published the
first known detailed anatomy
based on dissections of human
body.
He showed that both men and
women have the same number
of ribs.
Illustrations, like the brain shown
here, were done by Titian.
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Mind and Brain
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Paintings, like the Rembrandt
(The Anatomy Lesson of
Dr.Tulp), show the excitement
generated by dissection of
human cadavers.
René Descartes (1596 -1650) a
mathematician and philosopher
is considered as the originator
of modern mind/body
philosophy.
 He said most famously, cogito ergo sum ("I think, therefore I am").
 Thinking is thus every activity of a person of which he is immediately
conscious.
 Descartes' "error" pointed by António R. Damásio was the separation of
mind and body.
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Mind and Brain
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Charles Darwin (1809 –1882)
wrote a book “Expression of
emotions in man and animals”
pointing towards biological
origins of emotions and not
just cultural as people thought.
He also stressed the
importance of culture and
environment, that helps to
resolve “nature vs nurture”
debate.
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Mind and Brain

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
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Santiago Ramon y
Cajal (1852–1934)
founder of brain
science studied
properties of neurons.
He observed neurons under microscope
and showed that they are single cells
that end with synapses
Nerve impulses travel down the axon to synapses
In 1952 Hodgkin and Huxley constructed action potential
model for a spiking neuron
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Neurons

Cajal’s drawing of a slice of chicken brain
exposed using Gogli staining method

A Modern version
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Mind and Brain

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Pierre Paul Broca (18241880) discovered the region
in the brain responsible for
speech production
In 1861 he studied a patient
with epilepsy who lost ability
to speak
On the patient’s death Broca performed autopsy and
showed a damage to the posterior part of the third frontal
convolution in the left hemisphere and associated it to
speech production
Much of what we know about brain was first discovered by
studying various deficits
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Mind and Brain
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Wernicke’s area (W), in the left upper part of the
temporal lobe, is an important area for receptive
language (understanding).
Carl Wernicke (1848-1905) published his finding shortly
after Broca’s work
The two areas are connected
for speech comprehension
and production.
Damage (in or near) leads to:
 Broca’s area (B): Expressive aphasia,
 Wernicke’s area (W): Receptive aphasia,
 Fibers between B & W: Disconnection aphasia.
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Mind and Brain

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Left hemisphere is responsible for
language production and listening while
right hemisphere is concerned with
emotional aspects of language.
Angelo Mosso (1846-1910), found a
way to measure blood pressure during
demanding mental tasks.
Mosso’s work anticipated current
measures of brain blood flow like fMRI.
 fMRI measures local blood flow changes in
the brain.
 The fMRI responds to blood flow changes
whenever some brain regions require more
oxygen and glucose.
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Mind and Brain
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Nineteen century scientists were very interested in
consciousness.
William James (1890) declared psychology as a science of
conscious mental life.
Many scientists (Helmholtz, Loeb, Pavlov) disagreed – they
took on a physicalistic view of mental life.
Pavlov experiments with
dogs (1900) on classical
conditioning convinced
psychologists that all
behavior can be derived
from simple reflexes.
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Mind and Brain

In 1970-ies many
scientists were
dissatisfied with
behaviorism.

Different methods of
testing conscious and
unconscious brain
events were
developed
Figure compares results of study using visual backward
masking method based on fMRI to compare brain activity for
conscious and unconscious visual words.
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Conclusion

Ongoing debates in cognitive neuroscience:

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Local vs distributed functions in the brain
The question of consciousness
Unconscious inferences in vision
Capacity limits in the brain
Short-term and long-term memory – separate or not?
The biological basis of emotions
Nature vs nurture – genes vs environment
Cognitive neuroscience combines psychology, neuroscience and
biology to answer questions about mind and brain.
Modeling cognitive functions of the brain helps to understand
psychological phenomena and predict behavior.
It may simplify complex cognitive processing with full control of
experimental conditions.
It helps to build working models of embodied intelligence
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