Cognitive Neuroscience

1. Cognitive Neuroscience How do we connect cognitive processes in the mind with physical processes in the brain?

2. 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?

3. 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.

4. 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)

5. 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)

6. 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)

7. 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.

8. 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?

9. Neural computation • Three parts: • Dendrite: Receives input from other neurons • Cell Body: Processes input • Axon: Decides and generates output

11. 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.

12. 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

13. Neurons as information processors • The interesting question, from a cognitive perspective, is how neurons can process information. This leads to two questions: • How can neurons be used to perform computations? • How can neurons store information?

14. 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.

15. 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.

16. Information storage in NNs • So where is “knowledge” stored in a neural network? The connections!