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NEURONAL DYNAMICS 2: ACTIVATION MODELS 2002.10.8 Chapter 3. Neuronal Dynamics 2 :Activation Models 3.1 Neuronal dynamical system Neuronal activations change with time. The way they change depends on the dynamical equations as following: x g(FX ,FY , ) (3-1) y h(FX ,FY , ) (3-2) 2002.10.8 3.1 ADDITIVE NEURONAL DYNAMICS first-order passive decay model In the absence of external or neuronal stimuli, the simplest activation dynamics model is: x i xi y j yj (3-3) (3-4) 2002.10.8 3.1 ADDITIVE NEURONAL DYNAMICS since for any finite initial condition xi (t ) xi (0)e t The membrane potential decays exponentially quickly to its zero potential. Passive Membrane Decay Passive-decay rate Ai 0 scales the rate to the membrane’s resting potential. xi Ai xi solution : Ai t x( t ) x ( 0 ) e i i Passive-decay rate measures: the cell membrane’s resistance or “friction” to current flow. 2002.10.8 property Pay attention to Ai property The larger the passive-decay rate,the faster the decay--the less the resistance to current flow. Membrane Time Constants The membrane time constant C i scales the time variable of the activation dynamical system. The multiplicative constant model: C i x i -A i x i (3-8) 2002.10.8 Solution and property solution x i (t ) xi (0)e Ai t Ci property The smaller the capacitance ,the faster things change As the membrane capacitance increases toward positive infinity,membrane fluctuation slows to stop. Membrane Resting Potentials Definition Define resting Potential Pi as the activation value to which the membrane potential equilibrates in the absence of external or neuronal inputs: C i x i -A i x i Pi (3-11) Solutions x i (t) x i (0)e - Ai t Ci Pi (1 - e Ai - Ai t Ci ) (3-12) 2002.10.8 Note The capacitance appear in the index of the solution, it is called time-scaling capacitance. It does not affect the asymptotic or steady-state P solution A and does not depend on the finite initial condition. i i Additive External Input Add input Apply a relatively constant numeral input to a neuron. x i -x i I i (3-13) solution x i (t) x i (0)e-t I i (1- e -t ) (3-14) Meaning of the input Input can represent the magnitude of directly experiment sensory information or directly apply control information. The input changes slowly,and can be assumed constant value. 3.2 ADDITIVE NEURONAL FEEDBACK Neurons do not compute alone. Neuron modify their state activations with external input and with the feedback from one another. This feedback takes the form of path-weighted signals from synaptically connected neurons. Synaptic Connection Matrices n neurons in field FX p neurons in field FY The ith neuron axon in FX jth neurons in FY a synapse m ij m ij is constant,can be positive,negative or zero. Meaning of connection matrix The synaptic matrix or connection matrix M is an n-by-p matrix of real number whose entries are the synaptic efficacies. m ij the ijth synapse is excitatory if m ij 0 inhibitory if m ij 0 The matrix M describes the forward projections from neuron field FX to neuron field FY The matrix N describes the backward projections from neuron field FY to neuron field F X Bidirectional and Unidirectional connection Topologies Bidirectional networks M and N have the same or approximately the same structure. N M T M NT Unidirectional network A neuron field synaptically intraconnects to itself.M nxn. BAM M is symmetric, network is BAM M MT the unidirectional 2002.10.8 Augmented field and augmented matrix Augmented field FX FY FZ FX FY M connects FX to FY ,N connects FY to FX then the augmented field FZ intraconnects to itself by the square block matrix B 0 B N M 0 2002.10.8 Augmented field and augmented matrix In the BAM case,when N M then B BT hence a BAM symmetries an arbitrary rectangular matrix M. T In the general case, P C N M Q P is n-by-n matrix. Q is p-by-p matrix. T If and only if, N M T P PT Q Q the neurons in FZ are symmetrically intraconnected C C T 2002.10.8 3.3 ADDITIVE ACTIVATION MODELS Define additive activation model n+p coupled first-order differential equations defines the additive activation model y j -A j y j x i -A i x i p S ( x )m i i ij Ij (3-15) Ii (3-16) j 1 p S ( y )n j j ji j 1 2002.10.8 additive activation model define The additive autoassociative model correspond to a system of n coupled first-order differential equations p x i -Ai x i S j ( x j )m ji I i (3-17) j 1 2002.10.8 additive activation model define A special case of the additive autoassociative model where Ri' x j xi r I S j ( x j )mij I i xi Ci x i Ri xi ' Ri (3-18) i ij j j is n 1 1 1 ' Ri Ri j rij (3-19) (3-20) rij measures the cytoplasmic resistance between neurons i and j. 2002.10.8 Hopfield circuit and continuous additive bidirectional associative memories Hopfield circuit arises from if each neuron has a strictly increasing signal function and if the synaptic connection matrix is symmetric xi Ci xi ' S j ( x j )mij I i Ri j (3-21) continuous additive bidirectional associative memories p j 1 p x i -Ai x i S j ( y j )mij I i y j -Aj y j Si ( xi )mij I j (3-22) (3-23) i 1 2002.10.8 3.4 ADDITIVE BIVALENT FEEDBACK Discrete additive activation models correspond to neurons with threshold signal function The neurons can assume only two value: ON and OFF. ON represents the signal value +1. OFF represents 0 or –1. Bivalent models can represent asynchronous and stochastic behavior. Bivalent Additive BAM BAM-bidirectional associative memory Define a discrete additive BAM with threshold signal functions, arbitrary thresholds and inputs,an arbitrary but constant synaptic connection matrix M,and discrete time steps k. p xik 1 S j ( y kj )mij I i (3-24) j 1 p y kj 1 Si ( xik )mij I j (3-25) i 1 2002.10.8 Bivalent Additive BAM Threshold binary signal functions 1 k Si ( xi ) Si ( xik 1 ) 0 1 k S j ( y j ) S j ( y kj 1 ) 0 if if xik U i xik U i if xik U i if if y kj V j y kj V j if y kj V j (3-26) (3-27) For arbitrary real-value thresholds U U1 , , U n for neurons FX V V1 , , V p for neurons FY 2002.10.8 A example for BAM model Example A 4-by-3 matrix M represents the forward synaptic projections from FX to FY . A 3-by-4 matrix MT represents the backward synaptic projections from FY to FX . 2 3 0 1 2 0 M 0 3 2 2 1 1 3 1 0 2 T M 0 2 3 1 2 0 2 1 A example for BAM model Suppose at initial time k all the neurons in FY are ON. So the signal state vector S (Yk ) at time k corresponds to S (Yk ) (1 1 1) Input X k ( x , x , x , x ,) (5 2 3 1) k 1 k 2 k 3 k 4 Suppose Ui V j 0 2002.10.8 A example for BAM model is: first:at time k+1 through synchronous operation,the result S ( X k ) (1 0 1 1) next:at time k+1 ,theseFX signals pass “forward” through the filter M to affect the activations of the FY neurons. The three neurons compute three dot products,or correlations. The signal state vector S ( X k ) multiplies each of the three columns of M. 2002.10.8 A example for BAM model the result is: 4 S ( X k ) M ( i 1 Si ( xik )mi1 , (5 ( y1k 1 4 3) y2k 1 4 i 1 Si ( xik )mi 2 , 4 k S ( x i i )mi3 ) i 1 y3k 1 ) Yk 1 synchronously compute the new signal state vector S (Yk 1 ): S (Yk 1 ) (0 1 1) A example for BAM model the signal vector passes “backward” through the synaptic filter S (Yk 1 ) at time k+2: S (Yk 1 )M T (2 2 5 0) ( x1k 2 X k 2 x2k 2 x3k 2 x4k 2 ) synchronously compute the new signal state vector S ( X k 2 ) (1 0 1 1) S ( X k ) : A example for BAM model since S ( X k 2 ) S ( X k ) then S (Yk 3 ) S (Yk 1 ) conclusion These same two signal state vectors will pass back and forth in bidirectional equilibrium forever-or until new inputs perturb the system out of equilibrium. A example for BAM model asynchronous state changes may lead to different bidirectional equilibrium keep the first FY neurons ON,only update the second and third FY neurons. At k,all neurons are ON. Yk 1 S ( X k ) M ( 5 4 3) new signal state vector at time k+1 equals: S (Yk 1 ) (1 1 1) A example for BAM model new FX activation state vector equals: X k 2 S (Yk 1 ) M T ( 1 1 5 2) synchronously thresholds S ( X k 2 ) (0 0 1 0) passing this vector forward to FY gives Yk 3 S ( X k 2 ) M (0 3 2) S (Yk 3 ) (1 1 1) S (Yk 1 ) A example for BAM model similarly, S ( X k 4 ) S ( X k 2 ) (0 0 1 0) for any asynchronous state change policy we apply to the neurons FX the system has reached a new equilibrium,the binary pair (0 0 1 0), (1 1 1)represents a fixed point of the system. conclusion conclusion Different subset asynchronous state change policies applied to the same data need not product the same fixed-point equilibrium. They tend to produce the same equilibria. All BAM state changes lead to fixed-point stability. Bidirectional Stability definition A BAM system ( Fx , F y , M ) is Bidirectional stable if all inputs converge to fixed-point equilibria. A denotes a binary n-vector in B denotes a binary p-vector in 0,1n 0,1p Bidirectional Stability Represent a BAM system equilibrates to bidirectional fixed point Af ) as M MT M MT M Af ( Af , B f A A' A' A '' MT B B B' B' Bf Bf Lyapunov Functions Lyapunov Functions L maps system state variables to real numbers and decreases with time. In BAM case,L maps the Bivalent product space to real numbers. Suppose L is sufficiently differentiable to apply the chain rule: L n i L dxi xi dt i L xi xi (3-28) Lyapunov Functions The quadratic choice of L 1 1 T L xIx 2 2 x i2 (3-29) i Suppose the dynamical system describes the passive decay system. xi xi (3-30) The solution xi (t ) xi (0)e t (3-31) Lyapunov Functions The partial derivative of the quadratic L: L xi xi L i (3-32) xi2 (3-33) or L i 2 xi (3-34) (3-35) In either case L 0 (3-36) At equilibrium L0 This occurs if and only if all velocities equal zero xi 0 conclusion A dynamical system is stable if some Lyapunov Functions L decreases along trajectories. L 0 A dynamical system is asymptotically stable if it strictly decreases along trajectories Monotonicity of a Lyapunov Function provides a sufficient not necessary condition for stability and asymptotic stability. Linear system stability For symmetric matrix A and square matrix B,the quadratic T L xAx form behaves as a strictly decreasing Lyapunov function for any linear dynamical system x xB if and only if the matrix ABT BA is negative definite. T L xA x x Ax T xAB T x T xBAx T x[ AB T BA]x T The relations between convergence rate and eigenvalue sign A general theorem in dynamical system theory relates convergence rate and eigenvalue sign: A nonlinear dynamical system converges exponetially quickly if its system Jacobian has eigenvalues with negative real parts. Locally such nonlinear system behave as linearly. (Jacobian matrix) A Lyapunov Function summarizes total system behavior. L0 A Lyapunov Function often measures the energy of a physical sysem. Represents system energy decrease with dynamical systems Potential energy function represented by quadratic form Consider a system of n variables and its potential-energy function E. Suppose the coordinate x i measures the displacement from equilibrium of ith unit.The energy depends on only coordinate x i ,so E E ( x1 , xn ) since E is a physical quantity,we assume it is sufficiently smooth to permit a multivariable Taylor-series expansion about the origin: Potential energy function represented by quadratic form E E (0, , 0) i 1 3! 1 2 i j i j k 2 E 1 xi x i 2 i j 3E xi x j x k xi j k E xi x j xi x j 1 xAx T Where2A is symmetric,since 2E 2E aij a ji xi x j x j xi 2E xi x j x i x j The reason of (3-42)follows First,we defined the origin as an equilibrium of zero potential energy;so E (0, , 0) 0 Second,the origin is an equilibrium only if all first partial derivatives equal zero. Third,we can neglect higher-order terms for small displacement,since we assume the higher-order products are smaller than the quadratic products. Conclusion: Bounded decreasing L funcs provide an intuitive way to describe global “computations” in nueral networks ad other dynamical system. Bivalent BAM theorem The average signal energy L of the forward pass of theFX Signal state vector S ( X ) through M,and the backward pass Of the FY signal state vector S (Y ) through M T : S ( X ) MS (Y )T S (Y ) M T S ( X )T L 2 since S (Y ) M T S ( X ) T [S (Y ) M T S ( X )T ]T S ( X ) M T S (Y )T L S ( X )M T S (Y )T n p S ( x )S ( y )m i i j i j j ij Lower bound of Lyapunov function The signal is Lyapunov function clearly bounded below. For binary or bipolar,the matrix coefficients define the attainable bound: L mij i j The attainable upper bound is the negative of this expression. Lyapunov function for the general BAM system The signal-energy Lyapunov function for the general BAM system takes the form L S ( X ) MS(Y )T S ( X )[I U ]T S (Y )[J V ]T Inputs I [ I1 , , I N ] and J [ J 1 , , J P ] and constant vectors of thresholds U [U1 , , U N ] V [V1 , , VN ] the attainable bound of this function is. L m [ I ij i j i i Ui ] [ J j j V j ] Bivalent BAM theorem Bivalent BAM theorem.every matrix is bidrectionally stable for synchronous or asynchronous state changes. Proof consider the signal state changes that occur from time k to time k+1,define the vectors of signal state changes as: S (Y ) S (Yk 1 ) S (Yk ) S1 ( y1 ), , S p ( y p ) , S ( X ) S ( X k 1 ) S ( X k ) S1 ( x1 ), , S n ( xn ) , Bivalent BAM theorem define the individual state changes as: S j ( y j ) S j ( y j k 1 ) S j ( y j k ) Si ( xi ) Si ( xi k 1 ) S i ( xi ) k We assume at least one neuron changes state from k to time k+1. Any subset of neurons in a field can change state,but in only one field at a time. For binary threshold signal functions if a state change is nonzero, Bivalent BAM theorem Si ( xi ) 1 0 1 Si ( xi ) 0 1 1 For bipolar threshold signal functions S i ( xi ) 2 Si ( xi ) 2 The “energy”change L Lk 1 Lk L Differs from zero because of changes in field FX or in field FY Bivalent BAM theorem L S ( X ) MS(Yk )T S ( X )[I U ]T S ( X )[S (Yk ) M T [ I U ]]T S ( x ) I S ( x )U S ( x ) S ( y ) m S ( x ) I S ( x )U S ( x )[ S ( y ) m I U ] Si ( xi )[ xik 1 U i ] S i ( xi ) S j ( y kj ) T mij i j i i i i i i i k T i i i ij j j i i 0 j i k T i j j j i i i i i i ij i i i i i Bivalent BAM theorem Suppose S i ( xi ) 0 Then Si ( xi ) Si ( xi k 1 ) Si ( xi k ) 1 0 k 1 This implies xi U i so the product is positive: Si ( xi )[xik 1 U i ] 0 Another case suppose S i ( xi ) 0 Si ( xi ) Si ( xi k 1 0 1 ) Si ( xi ) k Bivalent BAM theorem k 1 This implies xi Ui so the product is positive: Si ( xi )[xik 1 U i ] 0 So Lk 1 Lk 0 for every state change. Since L is bounded,L behaves as a Lyapunov function for the additive BAM dynamical system defined by before. Since the matrix M was arbitrary,every matrix is bidirectionally stable. The bivalent Bam theorem is proved. Property of globally stable dynamical system Two insights about the rate of convergence First,the individual energies decrease nontrivially.the BAM system does not creep arbitrary slowly down the toward the nearest local minimum.the system takes definite hops into the basin of attraction of the fixed point. Second,a synchronous BAM tends to converge faster than an asynchronous BAM.In another word, asynchronous updating should take more iterations to converge. Review 1.Neuronal Dynamical Systems We describe the neuronal dynamical systems by firstorder differential or difference equations that govern the time evolution of the neuronal activations or membrane potentials. x g ( FX , FY ,) y h( FX , FY ,) Review 4.Additive activation models p xi Ai xi S j ( y j )n ji I i j 1 n y j A j y j Si ( xi )mij J j i 1 Hopfield circuit: 1. Additive autoassociative model; 2. Strictly increasing bounded signal function ( S 0) ; 3. Synaptic connection matrix is symmetric ( M M T ). xi Ci xi S j ( x j )m ji I i Ri j Review 5.Additive bivalent models p xik 1 S j ( y kj )m ji I i j n y kj 1 Si ( xik )mij I j i Lyapunov Functions Cannot find a lyapunov function,nothing follows; Can find a lyapunov function,stability holds. Review A dynamics system is stable , if L 0 ; asymptotically stable, if L 0 . Monotonicity of a lyapunov function is a sufficient not necessary condition for stability and asymptotic stability. Review Bivalent BAM theorem. Every matrix is bidirectionally stable for synchronous or asynchronous state changes. • Synchronous:update an entire field of neurons at a time. • Simple asynchronous:only one neuron makes a statechange decision. • Subset asynchronous:one subset of neurons per field makes state-change decisions at a time. Chapter 3. Neural Dynamics II:Activation Models The most popular method for constructing M:the bipolar Hebbian or outer-product learning method binary vector associations: ( Ai , Bi ) bipolar vector associations: ( X i , Yi ) i 1,2, m 1 Ai [ X i 1] 2 X i 2 Ai 1 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models The binary outer-product law: m M AkT Bk k The bipolar outer-product law: m M X kT Yk k The Boolean outer-product law: m M AkT Bk k m ij max( a1i b1j , , a mi bmj ) 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models The weighted outer-product law: m M wk X kT Y k Where k m w k 1 holds. k In matrix notation: Where M X T WY X T [ X1T | | X mT ] Y T [Y1T | | YmT ] W Diagonal [w1 ,, wm ] 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.1 Optimal Linear Associative Memory Matrices Optimal linear associative memory matrices: MX Y * The pseudo-inverse matrix of X : X * XX * X X X XX X * X X (X X ) * * * T * XX ( XX ) * * T 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.1 Optimal Linear Associative Memory Matrices Optimal linear associative memory matrices: The pseudo-inverse matrix of If x is a nonzero scalar: If x is a nonzero vector: X : x* 1/ x T x x* T xx If x is a zero scalar or zero vector : For a rectangular matrix X X , if * x* 0 ( XX T ) 1exists: X X ( XX ) * T T 1 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.1 Optimal Linear Associative Memory Matrices Define the matrix Euclidean norm M as M Trace( MM T ) Minimize the mean-squared error of forward recall,to find M̂ that satifies the relation Y XMˆ Y XM for all M 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.1 Optimal Linear Associative Memory Matrices 1 X Suppose further that the inverse matrix exists. Then 0 0 Y Y Y - XX -1Y ˆ X 1Y So the OLAM matrix M̂ correspond to M 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models If the set of vector { X 1 ,, X m } is orthonormal 1 if X i X Tj 0 if i j i j Then the OLAM matrix reduces to the classical linear associative memory(LAM) : T ˆ MX Y For X is orthonormal, the inverse of X is X T . 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.2 Autoassociative OLAM Filtering Autoassociative OLAM systems behave as linear filters. In the autoassociative case the OLAM matrix encodes only the known signal vectors x i . Then the OLAM matrix equation (3-78) reduces to M X *X M linearly “filters” input measurement x to the output vector x by vector matrix multiplication: xM x . 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.2 Autoassociative OLAM Filtering The OLAM matrix X * X behaves as a projection operator[Sorenson,1980].Algebraically,this means the matrix M is idempotent: M 2 M . Since matrix multiplication is associative,pseudoinverse property (3-80) implies idempotency of the autoassociative OLAM matrix M: M 2 MM X * XX * X ( X * XX * ) X X*X M 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.2 Autoassociative OLAM Filtering Then (3-80) also implies that the additive dual matrix I X * X behaves as a projection operator: ( I X * X ) 2 ( I X * X )( I X * X ) I 2 - X * X - X * X X * XX * X I - 2X * X ( X * XX * ) X I - 2X * X X * X I - X*X We can represent a projection matrix M as the mapping M : Rn L 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.2 Autoassociative OLAM Filtering The Pythagorean theorem underlies projection operators. The known signal vectors X 1 , , X m span n some unique linear subspace L( X 1 , , X m ) of R L equals {im ci X i : for all ci R} , the set of all linear combinations of the m known signal vectors. L denotes the orthogonal complement space {x Rn : xy T 0 for all y L} ,the set of all real n-vectors x orthogonal to every n-vector y in L. 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.2 Autoassociative OLAM Filtering n 1. Operator X * X projects R onto L. 2. The dual operator I X * X projects R n onto L . Projection Operator X * X and I X * X uniquely decompose every R n vector x into a summed signal ~ vector x̂ and a noise or novelty vector x : x xX * X x ( I X * X ) xˆ ~ x x ~ x x̂ 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.2 Autoassociative OLAM Filtering The unique additive decomposition xˆ generalized Pythagorean theorem: || x ||2 || xˆ ||2 || ~ x ||2 ~ x obeys a 2 2 2 where || x || x1 x n defines the squared Euclidean or l 2 norm. Kohonen[1988] calls I X * X the novelty filter on R n . 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.2 Autoassociative OLAM Filtering Projection x̂ measures what we know about input x relative to stored signal vectors X 1 , , X m : m x̂ c i x i i for some constant vector ( c1 , , c n ) . ~ x The novelty vector measures what is maximally unknown or novel in the measured input signal x. 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.2 Autoassociative OLAM Filtering Suppose we model a random measurement vector x as a random signal vector x s corrupted by an additive, independent random-noise vector x N : x xs xN We can estimate the unknown signal * filtered output xˆ xX X . x s as the OLAM- 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.2 Autoassociative OLAM Filtering Kohonen[1988] has shown that if the multivariable noise distribution is radially symmetric, such as a multivariable Gaussian distribution,then the OLAM capacity m and pattern dimension n scale the variance of the randomvariable estimator-error norm || xˆ x s || : m || x x s ||2 n m || x N ||2 n V [|| xˆ x s ||] 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.2 Autoassociative OLAM Filtering 1.The autoassociative OLAM filter suppress noise if m n , when memory capacity does not exceed signal dimension. 2.The OLAM filter amplifies noise if m n , when capacity exceeds dimension. 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.3 BAM Correlation Encoding Example The above data-dependent encoding schemes add outer-product correlation matrices. The following example illustrates a complete nonlinear feedback neural network in action,with data deliberately encoded into the system dynamics. 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.3 BAM Correlation Encoding Example Suppose the data consists of two unweighted ( w1 w2 1) binary associations ( A1 , B1 ) and ( A2 , B2 ) defined by the nonorthogonal binary signal vectors: A1 1 0 1 0 1 0 B1 1 1 0 0 A2 1 1 1 0 0 0 B2 1 0 1 0 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.3 BAM Correlation Encoding Example These binary associations correspond to the two bipolar associations ( X 1 , Y1 ) and ( X 2 , Y 2 ) defined by the bipol –ar signal vectors: X1 1 1 1 1 1 1 Y1 1 1 1 1 X 2 1 1 1 1 1 1 Y2 1 1 1 1 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.3 BAM Correlation Encoding Example We compute the BAM memory matrix M by adding the bipol T –ar correlation matrices X 1 Y1 and X 2T Y2 pointwise. The first T correlation matrix X 1 Y1 equals 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 T X 1 Y1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.3 BAM Correlation Encoding Example T Observe that the i th row of the correlation matrix X 1 Y1 equals the bipolar vector Y 1 multipled by the i th element T of X 1 . The j th column has the similar result. So X 2 Y2 equals 1 1 1 1 1 1 1 1 1 1 1 1 X 2T Y2 1 1 1 1 1 1 1 1 1 1 1 1 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.3 BAM Correlation Encoding Example Adding these matrices pairwise gives M: M X 1T Y1 X 2T Y2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 0 0 2 1 0 2 2 0 1 1 1 2 0 0 2 1 1 1 2 0 0 2 1 1 1 0 2 2 0 1 1 1 2 0 0 2 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.3 BAM Correlation Encoding Example Suppose, first,we use binary state vectors.All update policies are synchronous.Suppose we present binary vector A1 as input to the system—as the current signal state vector at F X . Then applying the threshold law (3-26) synchronously gives A1 M ( 4 2 2 4 ) (1 1 0 0 ) B1 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.3 BAM Correlation Encoding Example T Passing B1 through the backward filter M , and applying the bipolar version of the threshold law(3-27),gives back A1 : B1M T ( 2 2 2 2 2 2 ) ( 1 0 1 0 1 0 ) A1 So ( A1 , B1 ) is a fixed point of the BAM dynamical system. It has Lyapunov “energy” L( A1 , B1 ) A1MB1T 6 , which equals the backward value B1 M T A1T 6 . ( A2 , B2 ) has the similar result:a fixed point with energy A2 MB2T 6 . 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.3 BAM Correlation Encoding Example So the two deliberately encoded fixed points reside in equally “deep” attractors. Hamming distance H equals l 1 distance. H ( Ai , A j ) counts the number of slots in which binary vectors Ai and A j differ: n H ( Ai , A j ) | a ik a kj | k 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.3 BAM Correlation Encoding Example Consider for example the input A ( 0 1 1 0 0 0 ) , which differs from A2 by 1 bit , or H ( A, A2 ) 1 . Then A2 1 1 1 0 0 0 AM ( 2 2 2 2)( 1 0 1 0 ) B2 Fig3.2 shows that BAM can return original balance regardless of the noise. bipolar 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.4 Memory Capacity:Dimensionality Limits Capacity Synaptic connection matrices encode limited information. We sum more correlation matrices ,then mij 1 holds more frequently. After a point,adding additional associations ( Ak , Bk ) Does not significantly change the connection matrix. The system “forgets”some patterns. This limits the memory capacity. 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.4 Memory Capacity:Dimensionality Limits Capacity Grossberg’s sparse coding theorem [1976] says , for deterministic encoding ,that pattern dimensionality must exceed pattern number to prevent learning some patterns at the expense of forgetting others. 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.5 The Hopfield Model The Hopfield model illustrates an autoassociative additive bivalent BAM operated serially with simple asynchronous state changes. Autoassociativity means the network topology reduces to only one field, F X ,of neurons: FX FY .The synaptic connection matrix M symmetrically intraconnects the n neurons in field M M T or mij m ji . 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.5 The Hopfield Model The autoassociative version of Equation (3-24) describes the additive neuronal activation dynamics: xik 1 S j ( x kj )m ji I i (3-87) j for constant input I i , with threshold signal function 1 k 1 S i ( x i ) S i ( x ik ) 0 如果 x ik 1 U i 如果 x ik 1 U i 如果 x ik 1 U i (3-88) 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.6.5 The Hopfield Model We precompute the Hebbian synaptic connection matrix M by summing bipolar outer-product(autocorrelation)matrices and zeroing the main diagonal: m M X kT X k mI k 1 (3-89) where I denotes the n-by-n identity matrix . Zeroing the main diagonal tends to improve recall accuracy by helping the system transfer function S (XM )behave less like the identity operator. 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.7 Additive dynamics and the noise-saturation dilemma Grossberg’s Saturation Theorem Grossberg’s Saturation theorem states that additive activation models saturate for large inputs, but multiplicative models do not . 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models The stationary “reflectance pattern” P ( p1 ,, pn ) confronts the system amid the background illumination I (t ) pi 0, and p1 pn 1 The i th neuron receives input I i .Convex coefficient p i defines the “reflectance” I i : I i pi I A : the passive decay rate [0, B ] : the activation bound 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models Additive Grossberg model: xi Axi ( B xi ) I i ( A I i ) xi BI i We can solve the linear differential equation to yield xi (t ) xi (0)e ( A I i ) t BI i [1 e ( A I )t ] A Ii i For initial condition xi (0) 0 , as time increases the activation converges to its steady-state value: BI i xi B A Ii As I 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models So the additive model saturates. Multiplicative activation model: x i Ax i ( B xi ) I i xi I j j i ( A I i I j ) xi BI i j i ( A I ) xi BI i 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models For initial condition x i ( 0) 0 ,the solution to this differential equation becomes I xi pi B (1 e ( A I ) t ) A I As time increases, the neuron reaches steady state exponentially fast: I xi pi B pi B A I (3-96) as I . 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models This proves the Grossberg saturation theorem: Additive models saturate ,multiplicative models do not. 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models In general the activation variable x i can assume negative values . Then the operating range equals [ C i , Bi ] for C i 0 .In the neurobiological literature the lower bound C i is usually smaller in magnitude than the upper bound B i : C i B i This leads to the slightly more general shunting activation model: x i Ax i ( B x i ) I i (C x i ) I j j 1 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models Setting the right-hand side of the above equation to zero, and we can get the equilibrium activation value: C (B C)I xi [ pi ] BC A I which reduces to (3-96) if C=0. pi C the neuron generates nonnegative activations. BC 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models ※3.8 General Neuronal Activations:Cohen-Grossberg and multiplicative models Consider the symmetric unidirectional or autoassociative case when FX FY , M M T , and M is constant . Then a neural network possesses Cohen-Grossberg[1983] activation dynamics if its activation equations have the form n x i ai ( xi )[bi ( xi ) S j ( x j )mij ] (3-102) j 1 The nonnegative function a i ( x i ) 0 represents an abstract amplification function. 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models Grossberg[1988]has also shown that (3-102) reduces to the additive brain-state-in-a-box model of Anderson[1977,1983] and the shunting masking-field model [Cohen,1987] upon appropriate change of variables. 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models If a i 1 / C i , bi ( x i / Ri ) I i , S i ( x i ) g i ( x i ) Vi and constant mij m ji Tij T ji , where C i and R i are positive constants , and input I i is constant or varies slowly relative to fluctuations in x i ,then (3-102) reduces to the Hopfield circuit[1984]: Ci x i xi V j Tij I i j Ri An autoassociative network has shunting or multiplicative activation dynamics when the amplification function a i is linear, and bi is nonlinear . 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models For instance , if a i x i , mii 1 (self-excitation in lateral inhibition) , and 1 bi [ Ai x i Bi ( S i I i ) xi ( S i I i ) Ci ( S j mij I i )] j i xi then (3-104) describes the distance-dependent (mij m ji ) unidirectional shunting network : xi Ai xi ( Bi xi )[Si ( xi ) I i ] (Ci xi )[ S j ( x j )mij I i ] j i 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models Hodgkin-Huxley membrane equation: Vi c (V p Vi ) g ip (V Vi ) g i (V Vi ) g i t V p , V and V denote respectively passive(chloride Cl ) , excitatory (sodium Na ) , and inhibitory (potassium K ) saturation upper bounds . 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models At equilibrium, when the current equals zero ,the HodgkinHuxley model has the resting potential V rest : V rest g tpV p g V g V g p g g Neglect chloride-based passive terms.This gives the resting potential of the shunting model as V rest g V g V g g 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models BAM activations also possess Cohen-Grossberg dynamics, and their extensions: p x i a( i x i )[bi ( x i ) S j ( y j )mij ] j n y j a j ( y j )[b j ( y j ) S i ( x i )mij ] i with corresponding Lyapunov function L , as we show in Chapter 6 : L S i S j mij 0x S i' ( i )bi ( i )d i 0y S 'j ( j )d j i i j i j j 2002.10.8 Chapter 3. Neural Dynamics II:Activation Models 谢谢大家! 2002.10.8