Basics of Computational Neuroscience

1. Basics of ComputationalNeuroscience

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

3. Lecture: Computational Neuroscience, Contents 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

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

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

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

8. Neurophysiology: Behavior Reaction Environment Stimulus

9. Theoretical/Computational Neuroscience: Behavior Reaction Environment Stimulus

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

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

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

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

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

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

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

17. CNS Systems Areas Local Nets Neurons Synapses Molekules Functional and anatomical subdivisions of the Cortex: Premotor cortex Higher sensorial areas Prefrontal association cortex Parietal-temporal- occipital assoc. cortex limbic association cortex primary sensor and motor areas

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 CNS Systems Areas Local Nets Neurons Synapses Molekules

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

20. CNS Systems Areas Local Nets Neurons Synapses Molekules 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. Retinotopic Maps in V1: 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. Their receptive field looks like this: CNS Systems Areas Local Nets Neurons Synapses Molekules 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. stimulus

22. HIER weiter

23. CNS Systems Areas Local Nets Neurons Synapses Molekules 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“. Superpositioning of maps in V1: 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“.

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

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

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

27. CNS Systems Areas Local Nets Neurons Synapses Molekules Structure of a Neuron: At the dendrite the incoming signals arrive (incoming currents) 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