Post on 02-Nov-2014
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Basics of ComputationalNeuroscience
What is computational neuroscience ?
The Interdisciplinary Nature of Computational Neuroscience
1. Computational neuroscience and the perspective of scientists versus that of behaving agents
2. Levels of Information processing in the brain
3. Neuron and Synapse: Biophysical properties, membrane- and action-potential
4. Calculating with Neurons I: adding, subtracting, multiplying, dividing
5. Calculating with Neurons II: Integration, differentiation
6. Calculating with Neurons III: networks, vector-/matrix- calculus, associative memory
7. Information processing in the cortex I: Correlation analysis of neuronal connections
8. Information processing in the cortex II: Neurons as filters
9. Information processing in the cortex III: Coding of behavior by poputation responses
10. Information processing in the cortex IV: Neuronal maps
11. Learning and plasticity I: Physiological mechanisms and formal learning rules
12. Learning and plasticity II: Developmental models of neuronal maps
13. Learning and plasticity III: Sequence learning, conditioning
14. Memory: Models of the Hippocampus
Lecture: Computational Neuroscience, Contents
What is computational neuroscience ?
The Interdisciplinary Nature of Computational Neuroscience
Neuroscience:
Environment
Stimulus
Behavior
Reaction
Different Approaches towards Brain and Behavior
Psychophysics (human behavioral studies):
Environment
Stimulus
Behavior
Reaction
Environment
Stimulus
Neurophysiology:
Behavior
Reaction
Environment
Stimulus
Theoretical/Computational Neuroscience:
Behavior
Reactiondx
)(xf
U
Levels of information processing in the nervous system
Molecules0.1m
Synapses1m
Neurons100m
Local Networks1mm
Areas / „Maps“ 1cm
Sub-Systems10cm
CNS1m
CNS (Central Nervous System):
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
Cortex:
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
Where are things happening in the brain.
Is the informationrepresented locally ?
The Phrenologists viewat the brain(18th-19th centrury)
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
Untersuchungen von Patienten
Sehen != Erkennen
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
Results from human surgery
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
Results from imaging techniques
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
Functional and anatomical subdivisions of the Cortex:
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
limbic association cortex primary sensor and motor areas
Parietal-temporal-occipital assoc. cortex
Higher sensorial areasPremotor cortex
Prefrontalassociation cortex
Visual System:
More than 40 areas !
Parallel processing of „pixels“ andimage parts
Hierarchical Analysis of increasingly complex information
Many lateral and feedback connections
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
Primary visual Cortex:
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
Retinotopic Maps in V1:V1 contains a retinotopic map of the visual Field. Adjacent Neurons represent adjacent regions in the retina. That particular small retinal region from which a single neuron receives its input is called the receptive field of this neuron.
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
V1 receives information from both eyes. Alternating regions in V1 (Ocular Dominanz Columns) receive (predominantely) Input from either the left or the right eye.
Each location in the cortex represents a different part of the visual scene through the activity of many neurons. Different neurons encode different aspects of the image. For example, orientation of edges, color, motion speed and direction, etc.
V1 dicomposes an image into these components.
Orientation selectivity in V1:
Orientation selective neurons in V1 change their activity (i.e., their frequency for generating action potentials) depending on the orientation of a light bar projected onto the receptive Field. These Neurons, thus, represent the orientation of lines oder edges in the image.
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
Their receptive field looks like this:
stimulus
HIER weiter
Superpositioning of maps in V1:Thus, neurons in V1 are orientation selective. They are, however, also selective for retinal position and ocular dominance as well as for color and motion. These are called „features“. The neurons are therefore akin to „feature-detectors“.
For each of these parameter there exists a topographic map.
These maps co-exist and are superimposed onto each other. In this way at every location in the cortex one finds a neuron which encodes a certain „feature“. This principle is called „full coverage“.
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
Local Circuits in V1:
Selectivity is generated by specific connections
stimulus
Orientation selectivecortical simple cell
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
Layers in the Cortex:
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
Local Circuits in V1:
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
LGN inputs Cell types Local connections
To subcortical areasColl. Sup., Pulvinar, Pons
LGN, Claustrum
To different cortex areas
Spiny stellatecell Smooth stellate
cell
At the dendrite the incomingsignals arrive (incoming currents)
Molekules
Synapses
Neurons
Local Nets
Areas
Systems
CNS
At the soma currentare finally integrated.
At the axon hillock action potentialare generated if the potential crosses the membrane threshold
The axon transmits (transports) theaction potential to distant sites
At the synapses are the outgoingsignals transmitted onto the dendrites of the targetneurons
Structure of a Neuron: