Week 9: Cognitive Neuroscience Flashcards
(37 cards)
Cognitive Neuroscience
Cognitive neuroscience: the study of the neurophysiological basis of cognition
Involves an understanding of the nervous system and of the individual units that comprise that system
Builds a bridge between neurophilopsgy/ biology of human brain which crosses to the human mind
Ancient Views of the Brain
Egypt (17th century BCE):
* Earliest known reference to the “brain”
Greece (~5th century BCE):
* Aristotle: heart is seat of the mind; the brain is
just a cooling center for the blood
* Hippocrates: “[People] ought to know that from the brain, and from the brain only, arise our pleasures, joys, laughter and jests, as well as our sorrows, pains, griefs and tears.”
But: “These things that we suffer all come from the brain, when it is not healthy, but becomes abnormally hot, cold, moist, or dry, or suffers any other unnatural affection to which it was not accustomed. Madness comes from its moistness.”
Not common knowledge that the brain is receipt of the mind
Brain appeared 8 times in ancient Egyptian writing
6/8 mentions come from this scroll
Air was vehicle of cognition
Aristotle thought it wasn’t lungs, it was the heart that was the seed of emotions
Brain was the cooling system for heart
Noticed that body temp on head is warm, so it cools off the blood
Not everyone agreed
Hippo: moisture part of brain is responsible for hallucinations
19th Century
Phrenology
Tab into the localization of function
Skull measurements and correlate them with personality traits
E.g., a bump on skull here explained
why brain area was bigger and responsible for advancement such as strong writing abilities.
They applied it to everyone even though only tested at one person
Racist and sexist policies
20th and 21st Century
Brain waves
Eeg caps
Technology that looks inside brain to check state of brain
Check neural activity
Fluctuations that occur that serve cognition
Levels of Analysis
Topics can be examined in multiple ways, from multiple different perspectives and angles (i.e., “viewpoints”).
Each “viewpoint” can add small amounts of information which, when considered together, leads to greater understanding
Computers - imagine birthday coming up and computer put you through life and everything so you want a new computer
High level analysis - does the computer have good battery, graphics etc
You could look at subcomponents
Go even deeper, look at structural components such as CPU and GPU
Even deeper, the type of GPU
This is a multi-level analysis
Building Blocks of Nervous System
Neurons:
cells specialized to create, receive, transform, and transmit information in the nervous system
Nerve Nets
1800s
Researchers didn’t have access to tools
Microscope would show a web type of image
Based on this, determined that neural tissue was comprised of nerve net
All of the connections were interconnected and signaling could go any way
Not much evidence that suggested contrary
INCORRECT
No Nerve Nets
Santiago Ramón y Cajal
Nobel Prize in 1906 With Camillo Golgi
Santiago alluciated community to neurons with the help of camillo
Golgi developed a new staining method
When apply stain to cell, only stained a fraction of cells in tissue
Stains them entirely
Cajal applied the stains to neural tissue and from newborn animals as they werent as dense
Cajal was also gifted artist and drew out the neurons that he saw
Still use them to this day
Neurons, not nerve nets
Neurons not all connected
Had sensible connections with eachother and created circuits
Staining allowed visualization of gap between neurons and synapse
Nerve Nets Cont.
Contradicted by the neuron doctrine:
Individual nerve cells transmit signals, and are not continuous with other cells
Neurons
They can look different from each other
All have dendrites - branch structures that stretch out from body
They pick up signals from other cells
Cell body - contains genetic material and other cells that transport and keep cell alive
Axon/nerve fiber - where info leaves cell body and sent to other neurons
Signals go down to axon terminal which another neuron will pick up signal
Synapse but dont touch with other neurons
Neuron before synapse - presnyaptic neuron
After synapse - postsynaptic neuron
Synaptic Transmission
- Post-synaptic potentials from dendrites and cell body summate at axon hillock. If threshold for an action potential (AP) is reached, an AP will fire down axon
- APs propagate down axon towards axon terminal
- Arrival of AP at axon terminal causes release of neurotransmitters, which diffuse across synapse & bind to receptors
- Causes electrochemical change in post- synaptic cell: post-synaptic potentials can be excitatory (EPSPs) or inhibitory (IPSPs)
Transmission
Neruon recieving info onto dendrite: causes change in voltage in neuron
Signals are called: postsnyaptic potentials: voltage difference where it changes across membrane
Charge changing onto one side of membrane to other
EPSP makes it more likely to fire (neuron), IPSP is less
How Neurons Communicate
Action potentials are signals fired down a neuron after it has received enough exiditory input
Pressure sensitive neuron - touched by something, fires depending on pressure of touch
Monitor shows action potential
Reference electrode - shows difference between inside and outside charge of cell
Go from -70 to +40 mv because of excitement
When it leaves, membrane voltage begins to drop and will eventually go back to resting when positive cells leave completely
Sometimes too many positive cells leave, so there is a refracter period which makes it not completely at rest
Last action potential lasts a millisecond
Either it happens or not
Coding for Stimulus Intensity
Light pressure: not firing too frequently
Medium: neuron starts firing more rapidly
Strong: starts very rapidly
Changing rate of what neuron fires can change way we experience different intensities of stimulations
Representation of the Brain
David Hubel and Torsten Wiesel: discovered feature detectors
Visual cortex has neurons that preferentially focus on different features of a stimulus
In cats
Electrodes into cats, recorded activity
Found that there were neurons that preferentially responded to bars of light at different orientations
In correct field for neuron, then it fired strongly
If not correctly, it was silent
Called these feature detectors
Researchers discovered this by accident as they shined black spots onto screen and black dot worked as it was slipping the peice of glass across the screen which gave a response
Visual Cortex Organization
Neurons in VC are organized in columns
All respond to same kind of stimulus (columns)
Depending on angle of bar light
ODC: left likes pink angle and white likes right
Critical Period for Visual Cortex Development
Much easier to learn language when infant
One eye covered, 3 weeks to 3 months of age, lose ability to digest info from the one eye
Can reverse this by covering the eye with both info
This can only occur from 3 week to 3 months in cat
Moving patterns can stimulate it
Failure of visual motor configuration
After couple of days, regains typical visual placing
Peak sensitivity : 28 days, one time exposure and prefer vertical edges
Hierarchical Processing
When we perceive different objects, we do in a specific order that moves from lower and higher parts of the brain
the ascension from lower to higher parts of the brain corresponds to perceiving object features that move from lower to higher levels of complexity
Perception focus
Receiving objects, brain does this in a way
Lower areas of cortex and as more aided competitions are required (e.g., water bottle from others), access higher/later of the brain to process this information
Combine edges and counters to form shapes
Higher processing: shapes get combined to form full objects
Re-enterant processing: go from simple to medium to complex features
Revisit areas to do so
Representation in the Brain
Group of neurons can differentially fire,
these patterns represent different kinds of neural representations
This can code for complex stimuli
3 types:
specificity
population
sparse
Specificity Coding
Representation of a specific stimulus by firing of specifically tuned neurones specialized just to respond to a specific stimulus
Within a set a neurons, one neuron is selective for stimulus and others are silent
Each face causes a different neuron to fire
Not enough resources to code for everything in world with just one neuron
Population Coding
representation of a particular object by the pattern of firing of a large number of neurons
Object such as face, represented by pattern of firing
Differential firing patterns within every population
Since you have different levels of firing, you actually now can code for wayyyyyy more stimuli
Sparse Coding
when a particular object is represented by a pattern of firing of only a small group of neurons, with the rest of the neurons remaining silent
Indicated by pattern of firing but smaller group of nuerons
Kind of a mix of both
Lobes of the Cerebral Cortex
Frontal cortex: front of head
Parietal: top
Occipital: back of head
Temporal: side of head
Insula: tucked in
