1. Basics of Computational Neuroscience

2. What is computational neuroscience ? The Interdisciplinary Nature of Computational Neuroscience

3. 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

4. What is computational neuroscience ? The Interdisciplinary Nature of Computational Neuroscience

6. Neuroscience: Environment Stimulus Behavior Reaction Different Approaches towards Brain and Behavior

7. Psychophysics (human behavioral studies): Environment Stimulus Behavior Reaction

8. Environment Stimulus Neurophysiology: Behavior Reaction

9. Environment Stimulus Theoretical/Computational Neuroscience: Behavior Reaction

10. Levels of information processing in the nervous system Molecules 0.1  m Synapses 1m1m Neurons 100  m Local Networks 1mm Areas / „Maps“ 1cm Sub-Systems 10cm CNS 1m

11. CNS (Central Nervous System): Molekules Synapses Neurons Local Nets Areas Systems CNS

12. Cortex: Molekules Synapses Neurons Local Nets Areas Systems CNS

13. Where are things happening in the brain. Is the information represented locally ? The Phrenologists view at the brain (18th-19th centrury) Molekules Synapses Neurons Local Nets Areas Systems CNS

14. Untersuchungen von Patienten Sehen != Erkennen Molekules Synapses Neurons Local Nets Areas Systems CNS

15. Results from human surgery Molekules Synapses Neurons Local Nets Areas Systems CNS

16. Results from imaging techniques Molekules Synapses Neurons Local Nets Areas Systems CNS

17. 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 areas Premotor cortex Prefrontal association cortex

18. Visual System: More than 40 areas ! Parallel processing of „pixels“ and image parts Hierarchical Analysis of increasingly complex information Many lateral and feedback connections Molekules Synapses Neurons Local Nets Areas Systems CNS

19. Primary visual Cortex: Molekules Synapses Neurons Local Nets Areas Systems CNS

20. 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.

21. 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

22. 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

23. Local Circuits in V1: Selectivity is generated by specific connections stimulus Orientation selective cortical simple cell Molekules Synapses Neurons Local Nets Areas Systems CNS

24. Layers in the Cortex: Molekules Synapses Neurons Local Nets Areas Systems CNS

25. Local Circuits in V1: Molekules Synapses Neurons Local Nets Areas Systems CNS LGN inputsCell typesLocal connections To subcortical areas Coll. Sup., Pulvinar, Pons LGN, Claustrum To different cortex areas Spiny stellate cell Smooth stellate cell

26. At the dendrite the incoming signals arrive (incoming currents) Molekules Synapses Neurons Local Nets Areas Systems CNS At the soma current are finally integrated. At the axon hillock action potential are generated if the potential crosses the membrane threshold The axon transmits (transports) the action potential to distant sites At the synapses are the outgoing signals transmitted onto the dendrites of the target neurons Structure of a Neuron: