How Patterned Connections Can Be Set Up by Self-Organization D.J. Willshaw C. Von Der Malsburg.

Slides:



Advertisements
Similar presentations
EXAM 3 REVIEW Katherine & Tina. Developments of Neural Circuits Lecture 19.
Advertisements

Chapter 2.
2806 Neural Computation Self-Organizing Maps Lecture Ari Visa.
LGN, superior colliculus, and optic tectum development and innervation Chris Strang Ph.D. Vision Sciences WORB
What is vision Aristotle - vision is knowing what is where by looking.
$ recognition & localization of predators & prey $ feature analyzers in the brain $ from recognition to response $ summary PART 2: SENSORY WORLDS #09:
Activity-Dependent Development I April 23, 2007 Mu-ming Poo 1.Development of OD columns 2.Effects of visual deprivation 3. The critical period 4. Hebb’s.
Un Supervised Learning & Self Organizing Maps. Un Supervised Competitive Learning In Hebbian networks, all neurons can fire at the same time Competitive.
5/16/2015Intelligent Systems and Soft Computing1 Introduction Introduction Hebbian learning Hebbian learning Generalised Hebbian learning algorithm Generalised.
Artificial neural networks:
2002/01/21PSCY , Term 2, Copyright Jason Harrison, The Brain from retina to extrastriate cortex.
Exam in 12 days in class assortment of question types including written answers.
Question Examples If you were a neurosurgeon and you needed to take out part of the cortex of a patient, which technique would you use to identify the.
Blue= rods Green = Cones Pathways from the Retina In the brain, retinal ganglion axons travel to… –the hypothalamus: control bodily rhythms.
Exploring how the brain is shaped and wired
Exam 1 week from today in class assortment of question types including written answers.
How does the visual system represent visual information? How does the visual system represent features of scenes? Vision is analytical - the system breaks.
Visual Processing Structure of the Retina Lateral Inhibition Receptive Fields.
1 Activity-Dependent Development Plasticity 1.Development of OD columns 2.Effects of visual deprivation 3. The critical period 4. Hebb’s hypothesis 5.
The Human Visual System Short Overview. Terms: LGN, cortex, primary visual cortex, V1.
How does the mind process all the information it receives?
Color vision Different cone photo- receptors have opsin molecules which are differentially sensitive to certain wavelengths of light – these are the physical.
Axon Guidance How does an axon find the right target?
Artificial neural networks.
The visual system Lecture 1: Structure of the eye
Module : Development of the Nervous System Lecture 6 Synapse formation & refinement.
1 Activity-dependent Development (2) Hebb’s hypothesis Hebbian plasticity in visual system Cellular mechanism of Hebbian plasticity.
Connected Populations: oscillations, competition and spatial continuum (field equations) Lecture 12 Course: Neural Networks and Biological Modeling Wulfram.
Critical periods A time period when environmental factors have especially strong influence in a particular behavior. –Language fluency –Birds- Are you.
Unsupervised learning
Higher Processing of Visual Information Lecture I --- April 2, 2007 by Mu-ming Poo 1.Overview of the Mammalian Visual System 2.Retinotopic Maps and Cortical.
Lecture 12 Self-organizing maps of Kohonen RBF-networks
Synaptic Rearrangement Objectives: At the end of this lecture, you should be able to perform the following on a written examination. 1.Identify the series.
The BCM theory of synaptic plasticity.
The Brain from retina to extrastriate cortex. Neural processing responsible for vision photoreceptors retina –bipolar and horizontal cells –ganglion cells.
Neural Information in the Visual System By Paul Ruvolo Bryn Mawr College Fall 2012.
Artificial Neural Network Unsupervised Learning
1 Computational Vision CSCI 363, Fall 2012 Lecture 3 Neurons Central Visual Pathways See Reading Assignment on "Assignments page"
Cognition, Brain and Consciousness: An Introduction to Cognitive Neuroscience Edited by Bernard J. Baars and Nicole M. Gage 2007 Academic Press Chapter.
THE VISUAL SYSTEM: EYE TO CORTEX Outline 1. The Eyes a. Structure b. Accommodation c. Binocular Disparity 2. The Retina a. Structure b. Completion c. Cone.
The Visual Cortex: Anatomy
15 1 Grossberg Network Biological Motivation: Vision Eyeball and Retina.
Behavioral components of prey capture in the frog
Self Organizing Feature Map CS570 인공지능 이대성 Computer Science KAIST.
Modelling and measuring maps of nerve connections Part A David Willshaw Institute for Adaptive & Neural Computation School of Informatics University of.
Mind, Brain & Behavior Friday February 21, Types of Cones  Three types of cones respond preferentially to different wavelengths of light: Short.
Version 0.10 (c) 2007 CELEST VISI  N BRIGHTNESS CONTRAST: ADVANCED MODELING CLASSROOM PRESENTATION.
Computational Cognitive Neuroscience Lab Today: Model Learning.
Innervation of the Eye and Orbit Part 1: The Optic Nerve and
Ch 5. The Patterning of Neural Connections 5.5 ~ 5.6 Adaptive Cooperative Systems, Martin Beckerman, Summarized by Kwonill, Kim Biointelligence Laboratory,
Genetic Analysis of Ephrin-A2 and Ephrin-A5 Show Their Requirement in Multiple Aspects of Retinocollicular Mapping Interdisciplinary Program in Brain Science.
1 RETINA n Review its structure and function. 2 TRANSDUCTION n Mechanism:dark current->light response n Amplification n Adaptation/recovery n Transmission.
Activity-dependent Development
Sensory Neural Systems 5 February 2008 Rachel L. León
1 Perception and VR MONT 104S, Spring 2008 Lecture 3 Central Visual Pathways.
Figure 23.1 Growth cones guide axons in the developing nervous system.
Grades for Exam I BCS 249 Range: Average: 71 Before curve:
Axon Guidance How does an axon find the right target?
Brain development.
Early Processing in Biological Vision
Ascending Visual Pathways
Volume 56, Issue 2, Pages (October 2007)
Presented by Rhee, Je-Keun
Grossberg Network.
Ch. 5 The Patterning of Neural Connections 5. 1 ~ 5
Lowry A. Kirkby, Georgeann S. Sack, Alana Firl, Marla B. Feller  Neuron 
What the Fish’s Eye Tells the Fish’s Brain
Outline Announcements Human Visual Information Processing
Volume 56, Issue 2, Pages (October 2007)
Chapter 23: Wiring the Brain
Presentation transcript:

How Patterned Connections Can Be Set Up by Self-Organization D.J. Willshaw C. Von Der Malsburg

Early Visual Pathway Retinal ganglion cells project to LGN of the Thalamus and optic tectum in midbrain Optic tectum is the primary visual area in lower vertebrates (e.g. frogs, fish)

Outline 2 early hypothesis for map formation –Gradient models –Correlated activity models Willshaw and von der Malsburg’s model Retinal waves

How are maps initially formed? 2 possibilities : Axons project randomly. Only appropriate connections with congruent activity survive. Paul Weiss OR Chemospecificity Hypothesis. Axons are guided to targets via chemical markers. Roger Sperry

Chemospecificity Hypothesis Retinal axons returned to original, maladaptive tectal targets

Gradient Models topographic branching results from repulsive ligand gradients Growth cones have different densities of ligand receptors Multiple ligands create complex branching

Example Ligands Ephrin-A family boundaries vary Monschau et al. (1997).

Q: How do maps become fine-tuned?

Q: How do maps become fine-tuned? A: Correlated neural activity all-to-all connectivity  selective connectivity Input layer neighbors  output layer neighbors tectum retina

Willshaw & von der Malsburg 1976 Sperry-type models assume axons seek targets independently using neuron specific labels W & vdM’s model uses the lateral connections within input and output layers Goal of model is to encode the geometrical proximity of input cells using their correlated neural activity.

General Structure Short range excitatory connections Long range inhibitory connections Competitive, Hebbian synapses Spontaneous activity within input layer tectum retina

Equations H j * = activity in post-syn cell j A i * = state of pre-cell i; 1 if active at time t, 0 otherwise s ij = connection weight i  j e kj = excitatory connection of post- cell k  post-cell j i kj = inhibitory connection of post- cell k  post-cell j Weight update: Normalization: M = # pre cells N = # post cells

Orientation of the map orientation of map can be fixed using polarity markers bias weights of a small pre-syn region in the desired orienation with a small post-syn region

Mapping results Mean coordinates of weighted pre-cells projecting to each post- cell. Maps shift to accommodate new cells.

Correlated Firing: Retinal Waves Feller et al, (1996) Segregation of retinal inputs in LGN is complete before birth TTX on optic chiasm disrupts segregation, suggests activity dependence Spontaneous waves of synchronous RGC firing might organize mapping

Properties of Retinal Waves Occur spontaneously Appear randomly Spread to a limited region: local excitation; global inhibition

Movie Time!

Summary Retino-tectal maps are initially formed using chemical gradients. Correlated activity is used to fine tune connections. Exploiting lateral connections allows for more efficient genetic coding versus Sperry type models. Retinal waves share many properties of Willshaw and von der Malsburg’s model.