Cognitive Neuroscience
How do we connect cognitive
processes in the mind with
physical processes in the brain?
Neural plausibility
• If a cognitive process cannot be
implemented by neurons, it cannot take
place in the brain.
• How do we establish whether a process
can be implemented by neurons?
Two Goals
• Localization of function - At the macro
level, Cognitive Neuroscience tries to
examine where in the brain various
cognitive operations take place.
• Neural computation - At the micro level,
we try to understand how the brain
performs various operations.
Localization of function
• Post-mortem lesion studies - Find someone
who displays an interesting cognitive deficit.
When they die, study their brain for where the
damaged tissue was. (Phineas Gage, Broca’s
& Wernicke’s areas)
• Human-lesion studies - These days, we can
take pictures of the brain while it’s still in the
skull (CAT, MRI) an determine where the
lesions are while someone is still alive.
(Prosopagnosia, optic aphasia)
More localization
• Animal lesion studies - Human lesions are messy
and uncontrolled. No two people ever have the
exact same lesion. With animals, we can control
the characteristics of the lesions. (Area MT)
• Single-cell recording - Also with animals, we can
attach electrodes to neurons and measure the
firing pattern of individual neurons. (Feature
detectors in area V1)
But wait! There’s more
• Brain imaging - Modern technology provides
us the ability to, in very broad strokes,
examine what the brain is doing while a
person is actively performing a cognitive
task. (Face recognition, spatial processing)
• Brain stimulation - People are kept awake
during brain surgery. Use the opportunity.
(Motor and somatosensory homunculi)
Neural computation
• Physical modeling - Understand the
properties of neurons, how they share
information and what-not, and try to
understand how these properties can lead to
complex computations. (opponent processes,
how feature detectors are calculated).
• Computational modeling - Neural networks
are computer models of how groups of
neurons behave. Use these models to try and
better understand cognitive processing in the
brain.
Neurons
• Neurons are the basic unit of the brain.
• Any information processed in the mind
is processed by neurons.
• How can simple neurons generate
complex behaviors?
Neural computation
• Three parts:
– Dendrite: Receives input from other
neurons
– Cell Body: Processes input
– Axon: Decides and generates output
Action Potential
• The signal a neuron generates down its
axon is called an action potential.
• All action potentials are the same
magnitude (strength).
• We determine how excited a neuron is
by its firing rate - how many action
potentials per second it generates.
Neurotransmitter
• Neurons communicate by sending chemical
messages called neurotransmitters to other
neurons.
• These neurotransmitters travel from axon to
either the dendrite or the cell body across the
synapse.
• Where a synapse is depends on what the
connection type is
– Excitatory: Axon to dendrite
– Inhibitory: Axon to cell body
Neurons as information
processors
•
The interesting question, from a
cognitive perspective, is how neurons
can process information. This leads to
two questions:
1. How can neurons be used to perform
computations?
2. How can neurons store information?
Neural Networks
• A group of neurons acting towards a
common purpose, such as a computation or
process, is a neural network.
– For performing computations, we speak of
activation that flows through the network.
• Activation represents information
• Flow represents the processing of that information
– Different types of information will cause different
patterns of activation as input to the network
• Different patterns of light and dark on the retina
activate different visual receptors.
How neurons learn
• How activation flows through a network is
dictated by the connections in the network
– Where they are
– Excitatory vs. inhibitory
• Neural networks learn by creating,
eliminating, or modifying connections
between neurons.
Information storage in NNs
• So where is “knowledge” stored in a
neural network?
The connections!