Title: Theoretical Neuroscience:
Author: Tobestar
Last saved by: Sebastian Bitzer

Theoretical Neuroscience

Vorlesung vom 23. Oktober 2002

0	Overview
I	History
II	Research Disciplines
III	Introduction

0	Overview

-  How does the brain work? 🡪 (Neuro-) Biology
-  How can we build intelligent machines? 🡪 (Neuro-) Informatics

Common subject: Neural networks – Neuronale Netze

Originally: Networks of biological nerve cells

Around 1940		Parallel computing technology
And since 1990	Processes = artificial neurons, primitive features of biological neurons, adaptive (Adaption ist wichtigste Übernahme aus der Biologie)

Computational Neuroscience:
Theory based on computer models and simulations of neural networks

Connectionism:
Artificial neural networks in AI, psychology, linguistics, robotics, related to human brains

I	History

1. 	Neural Networks

-	McCulloch & Pitts (1943): A logical calculus of the ideas immanent in nervous activity
-	Hebb (1949): The Organization of behaviour (cell assemblies; simple learning rule for synapse = Hebb rule)
-	Rosenblatt (1958): The perception: A probabilistic model for information storage and organization in the brain (simple two-layered neural networks with fixed and adaptive connections; perception learning rule)
-	Hopfield (1982): Neural Networks and physical systems with emergent collective computational capabilities (neural networks as physical systems (spin glasses) used for combinatorial optimization (TSP); travelling salesman problem)
-	Rummelhart, Hinton & Williams (1986): Learning internal representations by error propagation (learning rule for fully adaptive, multilayered nns) (already developed in 1974 by Paul Werbos)

2.	(Biological) Cybernetics

-	Wiener (1948): Cybernetics or control and communication in the animal and the machine (feedback principle for error correction, e.g. helmsman (Steuermann) of a ship; biological feedback systems: body temperature, blood temperature)

3.	Brain Research

-	19^th century: localization of mental abilities in different regions
-	Ramon y Cajal (1906): The structure and connection of neurons (anatomical studies of brain areas; staining methods; brain as network of neurons)
-	Sherington (1906): The integrative action of the nervous system (physiological understanding of synapses; excitation or inhibition)

II	Research Disciplines

-	Brain Research
o	Perception, memory, movement control, higher mental functions
-	Computational Neuroscience
o	Mathematical analysis and computer simulations for modelling the structure and function of nervous systems
-	Artificial Intelligence
o	Intelligent behaviour in machines (computers, robots etc.)
-	Cognitive Psychology
o	Mental abilities as information processing systems, models not necessarily neural
-	Connectionism
o	Models of human mind and behaviour
o	Parallel distributed systems of neuron-like elements
o	Not necessarily realistic
-	Philosophy
o	Mind-body problem, consciousness
-	Cognitive Neuroscience
o	Clinical data, brain imaging, human cognition, single cell recording in animals

Interaction between Modelling and Experiment:

Frog:	two behaviours:	- approaches small moving objects
			- flees from large moving objects

Model:
	Perception:	small, large
	Action:		approach, flee

	Perception	Action
	Small           🡪	approach
	Large           🡪 	flee

Hypothesis about brain regions:
	tectum:		perception of small objects
	Pretectum:	perception of large objects

Experiment:	Lesion of the pretectum

Conjecture:	small objects 🡪 normal action
		large objects 🡪 no action

Result:		frog approaches small AND large objects

Need of a new model:
	Tectum:	perception of moving objects; activate approach
	Pretectum:	perception of large objects; activate fleeing AND inhibit approaching

III	Introduction

Computational (= Theoretical) Neuroscience
-	theoretical study of nn and brain
-	uncover principles and mechanism that guide
-	development, organization, information processing, and mental abilities of the nervous system

Questions
-	How does the brain work?
-	What are the biological mechanisms?
-	How is it organized?
-	What information processing principles are there in perception?
-	How did the brain evolve?
-	How does the brain change during lifetime?
-	What is the effect of damage?
-	How can its diseases be treated?

Tools
-	genetic manipulation
-	in vivo, in vitro recording of cell activities
-	optical imaging
-	functional magnetresonance scanning
-	psychophysical measurement
-	computer simulation

Focus
-	develop and test hypothesis about nn
-	develop and evaluation of models:
o	formulas, systems of equation, computer programs, computer hardware
-	synonymous to Theoretical Neuroscience:
o	theory = formal models + computer simulations + mathematical proofs
-	compare with Theoretical Computer science
-	mostly analytically intractable models
-	measure models against experimental data

Levels of organization

CNS		1 m		dynamic interaction of several brain areas
System		10 cm		perform higher mental functions (e.g. visual system)
Maps		1 cm		distributed representation of information (e.g. of sensations, e.g. 
somatosensory map)
Network	1 mm		perform complex tasks not present in single neurons
Neuron		100 µm		basic processing units
Synapses	1 µm 		plasticity of nervous system
Molecules	1 Å		electric potential of neurons; signal transmission between neurons

Models
-	simplification (evaluations, test, predictions)
-	equations, computer programs, prose (not recreations of reality)
-	are necessary:
o	to investigate particular questions,
o	to demonstrate particular features of complex real world systems

Models in Theoretical Neuroscience
-	single neurons
-	networks of neurons
-	architectures capturing brain organization
🡪 interpret experimental data
🡪 reveal information processing principles in the brain

Computer-Brain Analogy

Computer
-	small number of processes
-	logical gates (as and, or, not; operate on bits 0,1) as elementary units
-	complex processors
-	variety of basic operations
-	information stored in separate memory
-	must be programmed

Brain
-	many (ca. 10^10) processing elements
-	more complex units than logical gates
-	connected in networks
-	has emergent capabilities (not found in smaller parts)
-	information stored in connections
-	learns from experience (examples, trial and error)