Biointelligence Laboratory, Seoul National University Ch 8. Synaptic Plasticity 8.1 ~ 8.4 Adaptive Cooperative Systems, Martin Beckerman, 1997. Summarized by Kwonill, Kim Biointelligence Laboratory, Seoul National University http://bi.snu.ac.kr/

(C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/ Contents 8.1 Synaptic Plasticity in the Visual Cortex 8.1.1 Forms of Synaptic Plasticity 8.1.2 Use-Dependent Changes in Synaptic Efficiency 8.2 Models of Synaptic Modification 8.2.1 Feature Selectivity 8.2.2 Stability 8.2.3 Information Processing in the Visual System 8.3 Outline of the Chapter 8.4 Dynamics 8.4.1 Phase Space 8.4.2 Trajectories in Phase Space 8.4.3 Conservative and Dissipative Systems (C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/

(C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/ QnA What is the main model to discuss in this chapter? (C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/

Forms of Synaptic Plasticity Sprouting of new axonal and dendritic structures Pruning, retraction and disappearance of old arbors Changes in the membrane properties of existing growth Cell death Use-dependent changes in synaptic efficiency (C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/

Forms of Synaptic Plasticity – Cell Death Overproduced neurons  Cell death 15% ~ 85% loss during late prenatal and early postnatal development 1st Function: Numerically matching pre- and postsynaptic neural structures 2nd Function: Selectively removing cells whose axons project to inappropriate targets. (C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/

(C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/ Forms of Synaptic Plasticity – Use-dependent Changes in Synaptic Efficiency Critical period in the visual system Cat, monkey, … LTP, LTD Hippocampus of rat Adult plasticity in which cortical motor and sensory maps After injury Not static STDP (Spike-Timing Dependent Plasticity) ? (C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/

Use-dependent Changes in Synaptic Efficiency Hebb-Stent rule Highly local process of synaptic modification driven by correlated pre- and postsynaptic activity Cooperative processes initiated by spatiotemporally correlated afferent input NMDAR & BCM theory Critical period Kitten (C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/

Models of Synaptic Modification – Feature Selectivity Input activity vector: Random variable Stationary stochastic process with a time-invariant distribution in general Synaptic weight vector: Selectivity of to averaging The peak respose to a particular input (C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/

Models of Synaptic Modification – Feature Selectivity Related works The earliest model by Anderson, Kohonen and Cooper Linear associator Bounded weights by Malsburg Orientation selectivity and related properties By Perez, Glass, and Shlaer By Nass and Cooper (C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/

Models of Synaptic Modification – Stability BCM (Bienenstock, Cooper, and Munro) To explain the development of orientation selectivity and binocular interaction in kitten visual cortex Single-cell  Network by Cooper and Scofield Dynamic (nonlinear) equation for synaptic modification by Cooper, Liberman and Oja Modification threshold (moving) (C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/

(C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/ Models of Synaptic Modification – Information Processing in the Visual System Three viewpoints of BCM theory Single-cell & networks formulation, and its objective function Connections between the theory and biology Information-processing functions of BCM Synaptic systems constraint on the square of the summed synaptic strength Function as PCA Maximal information preservation A number of models of synaptic modification (C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/

(C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/ Outline of the Chapter 8.4~8.5 : a brief overview of the phase space behavior of dynamic systems 8.6~8.8 : how feature selective states develop from the Hebbian, BCM modification rule, a fixed network architecture, and patterned visual input 8.9~8.10 : the neurophysiological basis for the theory 8.11~8.13 : the information processing activities carried out by model neurons in the visual system (C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/

(C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/ Dynamics – Phase Space Phase space a space in which all possible states of a system are represented, with each possible state of the system corresponding to one unique point in the phase space In BCM theory, plane is interesting An physical example : Newton’s 2nd law (C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/

Trajectories in Phase Space Fixed points Equilibrium states of thermodynamic system Limit cycle Periodic motion (oscillator) (C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/

Conservative and Dissipative Systems Properties of phase space Trajectories in phase space are unique No intersection Collections of neighboring points obey certain invariance conditions A theorem of Cauchy’s Liouville’s theorem (C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/

(C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/ Summary Synaptic Plasticity Cell Death Use-Dependent Changes in Synaptic Efficiency Models of Synaptic Modification Feature Selectivity Stability : BCM theory Information Processing in the Visual System Outline of the Chapter Dynamics Phase Space Trajectories in Phase Space (C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/

(C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/ QnA What is the main model to discuss in this chapter? BCM theory (C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/