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Structures and Structures and Learning Learning SimulationsSimulations

Janusz A. Starzyk

Computational IntelligenceComputational Intelligence

Based on a course taught by Prof. Randall O'Reilly University of Colorado and Prof. Włodzisława DuchaUniwersytet Mikołaja Kopernika

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Neurons and rulesNeurons and rulesIndividual neurons allow to detect single features.

How can we use a neuron’s model?

Classical logic:

If A1 and A2 and A3 then Conclusion

e.g. If Headache and Muscle ache and Runny nose then Flu

Neural threshold logic:

If M of N conditions are fulfilled then Conclusion

Conditions can have various weights; classical logic can be easily realized with the help of neurons.

There’s a continuum between rules and similarity: for a few variables, rules are useful; for many others, similarity.

|WA|2 = |W|2 + |A|2 2W.A = 2(1 W.A), for normalized X, A,

so a strong excitation = a short distance (large similarity ).

5

1

2i ii

W A W A

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Pandemonium in actionPandemonium in actionDemon (gr. daimon - one who divides or distributes), Demons observing features: | vertical line D1-- horizontal line D2/ forward slash D3\ backslash D4

V demons 3, 4 => D5

T demons 1, 2 => D6

A demons 2, 3, 4 => D7

K demons 1, 3, 4 => D8

demons 6,7,8 => D9

The better it fits the louder they shout.

Demon making decisions: D9 doesn’t distinguish TAK from KAT ...

for this we need sequence recognition

Each demon makes a simple decision but the entirety is quite complex.

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SimulationsSimulationsWhat traits should we observe to understand speech? language?

images? faces? Act creatively? http://psych.colorado.edu/~oreilly/cecn_download.htmlSimulations: write equations describing how good is the fit, how loud

the demons should shout.

How to reduce biology to equations?

Synaptic efficiency: how strong

Activity of a presynaptic neuron: how many vesicles, how much neurotransmitter per vesicle, how much is absorbed by the postsynaptic

Postsynaptic: how many receptors, geometry, distance from the spine etc.

Extreme simplification: one number characterizing efficiency.

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Neurons and networksNeurons and networks1. What properties does a neural network have?2. How can we influence a neural network to do something interesting?

Biology: networks are in the cortex (neocortex) and subcortical structures.Excitatory neurons (85%) and inhibitory neurons (15%).

Generally excitations can be: mainly in one direction

signal transformation; in both directions

supplementing missing information agreeing upon hypotheses and strengthening weak signals. most excitatory neurons are bi-directional.

Inhibition: controls mutual excitations, necessary to avoid extra feedback (epilepsy).The entirety makes possible the interpretation of oncoming information in the light of knowledge of its meaning, encoded in the network structure.

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General network structureGeneral network structureDoes the cortex have some general properties or does its structure depend on the function: perceptive, motor, associative?

There is a functional specialization of the cortex, observable differences in various areas, from this comes the division into Brodmann’s fields.

The general scheme is retained: •A excitatory neurons

• main NT is glutamic acid, • AMPA receptor opens Na+ channels, excites long axons, communication

within and between neural groups• NMDA receptor opens Ca++ channels, leads to learning

• around 85%, mainly pyramidal cells, spiny stellate cells + ... •B inhibitory neurons

• main NT is GABA (gamma-aminobutyric acid), opens Cl- channels, interneurons, local projections, regulation of the excitation level;

• GABA-A short-term effect BAGA – B long-term effect• around 15%: basket cells and chandelier cells + ...

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Pyramidal, spiny stellate, basket, Pyramidal, spiny stellate, basket, chandelier, spindle cellschandelier, spindle cells

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Excitatory and inhibitory neuronsExcitatory and inhibitory neurons

Glutamic acid opens Na+ channels, (excitatory),

GABA works on Cl- channels inhibiting excitation.

Pyramidal and spiny stellate

Basketchandelier

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Laminar structureLaminar structureThe cortex has a thickness of 2-4 mm and consists of 6 layers, with different thicknesses in different parts of the brain.

A - the visual cortex has a thicker input layer 4a-c;

B - the parietal cortex has thicker hidden layers 2 and 3;

C - the motor cortex has thicker output layers 5-6;

D – the prefrontal cortex doesn’t have markedly thicker layers.

A B C D

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Layer connectionsLayer connectionsFunctional division of layers: input layer 4, receives information from the thalamus, senses; output layers 5/6, subcortical centers, motor commands; hidden layers 2/3, transform local information and information from distant neuron groups, coming through axons on layer 1.

In each layer we have local bi-directional connections.

Hidden layers: they extract certain attributes of the signal, strengthen some and weaken others; this enables the realisation of complex signal transformations. This kind of organization is also required by episodic memory.

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Connections in more detailConnections in more detail

1) Input layer 4, initial processing of outside information.

2) Hidden layers 2/3, further processing, associations, a little I/O.3) Output layers 5/6, subcortical centers, motor commands.

these blocks maybe connectedsequentially

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Simple transformations Simple transformations Unidirectional connections are rare, but this model can be generalized to a situation with feedback.

Bottom-up processing: collectively, neural detectors perform transformations, categorize chosen signals, differentiating similar from

dissimilar ones.

Detectors create a representation of information coming in to the hidden layer.

Simplest case: binary images of digits in a 5x7 grid on input, all images similar to the given digit should activate the same unit hidden in the 5x2 grid.

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Digit detector - simulationDigit detector - simulationWe activate the PDP++ simulator, leabra++.exe

(Menu Start, or PDP++/bin/CYGWIN)

In the PDP++ window we select .projects, open in root

we exit the catalog ../ twice, we enter sims/

we select chapter_3, then transform.proj.gz

The window ...Network_0 shows the network structure, two layers, input and output.

Looking at the weights connected with the selected hidden unit:

we click on r.wt, and then on the given unit: the weights are matched precisely to the digits.

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Digit detector - networkDigit detector - network

Input weights for the selected digit r.wt shows weights for hidden units, here all 0 or 1.

s.wt shows individual connections, eg. the left upper input corner =1 for 5 and 7.

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Digit detector - operationDigit detector - operationInstead of r.wt we select act (in ...network_0).

In the control window xform_ctrl we select step, activating one step, the presentation of successive digits.

The degree of activation of the hidden units for the selected digit is large (yellow or red color), for the others it is zero (gray color).

Those easily distinguished, eg. 4, have a higher activation than those which are less distinct, eg. 3.

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GridLogGridLogWhat is the activity of individual detectors in response to a single input image? In the control window xform_ctrl we select view, GridLog.

For each image all units activate to a certain degree; we can see here the large role of the thresholds, which allow us to select the correct unit; in the ctrl window we can turn off the thresholds (biases off) and see that some digits are not, recognized.

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Events windowEvents windowFrom the window xform_ctrl we select Events: we see all images,

here we can change them, remove or add new ones.

Adding bit 1 to the image: click on square; removing a bit: shift+click.

The selection in the lower left corner will show a larger window with this image.

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Similarity of images Similarity of images From the window xform_ctrl we select Cluster and Cluster digits.

Clustering with the help of a dendrogram illustrates the reciprocal similarity of vectors, the length of line d(A,B) = |AB|.

Hierarchical clustering of vectors representing input images: highly probable are the digits 8 and 3: 13 identical bits, as well as 4 and 0, only 4 common bits.

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Likelihood of distorted images Likelihood of distorted images From the window xform_ctrl we select Noisy_digits, Apply and Step observing in window Network_0, we watch act, awakening of hidden neurons.

We have image + 2 distorted,

likelihood of distorted digits is shown by the dendrograms.

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Leakage channels (potassium)Leakage channels (potassium)A change in the conductivity of the leakage channels affects the selectivity of neurons, for smaller values of ĝl the answers become gradual.In the window xform_ctrl we will decrease ĝl =6, to 5 i 4.More associations less precision.

ĝl =6 ĝl = 5 ĝl = 4

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LettersLettersWe will apply the network for digits to letters... only S resembles 8, the other hidden units don’t recognize anything. Detectors are specialized for specific tasks! We won’t recognize Chinese characters if we only know Korean.Dendrograms for the representation of letters before and after the transformation.

What will happen if we decrease the conductivity of the leakage channels ?

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Local and distributed representationsLocal and distributed representationsLocal representations : one hidden neuron represents one image

these neurons are referred to as grandmother cells.

Distributed representations : many neurons respond to one image, each neuron takes part in reactions to many images.

http://www.brain.riken.go.jp/labs/cbms/tanaka.html - nice demo.

Observation of the neuralresponse in the visual cortex of a monkey to different stimuli confirmsthe existence of distributedrepresentations

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Distributed representationsDistributed representationsImages can be represented in a distributed manner by an array of their traits (feature-based coding).Traits are present "to a certain degree." Hidden neurons can be interpreted as the degree of detection of a given feature – that's what you do in fuzzy logic.

Advantages of distributed representation (DR): Savings: images can be represented by combining the activation of

many units; n local units = 2n combinations. Similarity: similar images have comparable DR, partly overlapping. Generalization: new images activate various DR usually giving an

approximation to sensory response, between A and B. Resistance to damage, system redundancy. Exactness: DR of continuous features is more realistic than discrete

local activations. Learning: becomes easier for continuous small changes in DR.

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Experiment with RRExperiment with RRProject loc_dist_proj.gz from chapter_3.

To represent digits we now use 5 units.

The network reacts to the presence of certain features, eg. the first hidden neuron reacts to =>

Distributed representations can work even on randomly selected traits: new DR = projection of input images to some feature space.

Gridlog shows the distribution of net activation and output of the network, showing the degree of presence of a given feature.

The dendrogram looks completely different than for a local network.

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FeedbackFeedbackNetworks almost always have feedback between neurons.

Recurrence: secondary, repeated activation; from this come networks with recurrence (bidirectional).

Bottom-up and vice versa, or recognition and imagination.

Recurrence makes possible the completion of images, formation of resonances between associated representations, strengthening of weak activations and the initiation of recognition.

Example: recognize the second letter in simple words:

CART (faster) BAZS (slower)

Strange: text recognition proceeds from letters to words;

so how does a word help in the recognition of a letter?

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Recurrence for digitsRecurrence for digitsA network with a hidden layer 2x5, connected bi-directionally with the inputs.

Symmetrical connections: the same weights Wij=Wji.

The center pixel activates 7 hidden neurons,

each hidden neuron activates all the pixels of a given digit, but the inputs are here always taken from the images of the digits.

Combinations of hidden neuron activations

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Completion of imagesCompletion of imagesProject pat_complete.proj.gz in Chapter_3.A network with one 5x7 layer, connected bi-directionally with itself.

Symmetrical connections: the same weights Wij=Wji. Units belonging to image 8 are connected to themselves with a weight of 1, remaining units have a weight of 0.

Activations of input units here are not fixed by the images (hard clamping), but only initiated by the images (soft clamping), so they can change.Check the dependence of the minimum number of units sufficient to reconstruct the image from the conductivity of ion channels.

For a large ĝl start from a partial image requires a greater and greater number of correctly initialized pixels, for ĝl = 3 we need >6 pixels, for ĝl = 4 we need >8 pixels, for ĝl = 5 we need >11 pixels.

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Recurrent amplificationRecurrent amplificationProject amp_top_down.proj.gz in Chapter_3.From a very weak activation of some image, amplification processes can lead to full activation of the image or uncontrolled activation of the whole network. The network currently has two hidden layers.

The same effect can be achieved through feedback inside a single layer.

Weak activation of letters suffices for word recognition, but word recognition can amplify letter activation and accelerate the response.

A weak excitation leads to a growth in activation of neuron 2, and reciprocal activation of neuron 1.

For a large ĝl>3.5 the effect disappears.

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Amplifying DRAmplifying DRProject amp_top_down.dist.proj.gz in Chapter_3.Distributed activation can lead to uncontrolled activation of the whole network. Two objects: TV and Synthesizer; 3 features: CRT monitor, Speaker and Keyboard. The TV has CRT and Speaker, the Synthesizer has Speaker and Keyboard – one feature in common.

Starting from an arbitrary activation at the input, Speaker activates both neurons, TV and Synthesizer, and all 3 features in layer 1, in effect all elements are completely active.

Manipulating the value of ĝl ~ 1.737 shows how unstable these networks are => we need inhibition!

Feedback leads to activation of layer 1, then 2, then 1 again, repeatedly.

From the ctrl panel we select View/Grid_Log, then RunUniq.

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Inhibitory interactionsInhibitory interactionsWe need a mechanism which reacts dynamically, not a constant leak current – negative feedback, inhibitory neurons.

Two types of inhibition: thanks to the use of these same input projections, we can anticipate activations and inhibit directly; this selective inhibition allows for the selection of neurons best suited to specific signals.

Inhibition can also be a reaction to excessive activation of a neuron. Inhibition leads to rare distributed representations.

Feedforward inhibition: depends onthe activation of the lower level

Feedback inhibition: reacts toactivation within the layer

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Inhibitory parametersInhibitory parametersA model with inhibitory neurons is costly: there are additional neurons and the simulation must be done with small increments in time to avoid oscillation.We can use simpler models with competition among neurons, leading to the selection of a generally small number of active neurons (sparse distributed representation). Inhibitory parameters: g_bar_i_inhib, self-inhibition of a neuron g_bar_i_hidden, inhibition of hidden neurons scale_ff, weight of ff connections, input-inhibition scale_fb, weights of reciprocal connections

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WTA and SOM approximationWTA and SOM approximation

Winner Takes All, leaves just 1 active neuron and doesn't lead to distributed representation. In the implementation of Kohonen’s SOM the winner is chosen along with its neighborhood. Activation of the neighborhood depends on distance from the winner.Other approaches use combinations of excitatory and inhibitory neurons:

McClelland and Rumelhart – interactive activation and competition;

introduced the superiority of the higher layer over the lower, allowing for the supplementation of missing features and making predictions

Grossberg introduced bi-directional connections between layers using minicolumnar structures

separate inhibitory neurons

interactionpoziome

feedback

inhibition poziome

input activation

4

6

2/3

Layer 6 - next layer

5

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kWTA approximationkWTA approximationk Winners Take All, the most common approximation leaving only k active neurons.

Idea: inhibitory neurons decrease activation so that no more than k neurons can be active at the same time.

Find the k most active neurons in the layer; calculate what level of

inhibition is necessary so that only these remain above the threshold.

•The distribution of activation levels in a larger network should have a Gaussian character.•We have to find this level of threshold activation gi

so that for a value between k and k+1 it could be balanced by inhibition:

• Two methods: basic and averaged.

•Weaker winners are eliminated by the minimal threshold value•kWTA model constitutes a simplification of biological interactions

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Basic kWTABasic kWTA

Equilibrium for potential for which currents don't flow is established by the level of inhibitory conductance.

For confirmation, that only k neurons are above the threshold we take:

Typically constant q=0.25; depending on the distribution of excitation across the layer, we can have a clear separation (c) or inhibit highly active neurons (b).

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Averaged kWTAAveraged kWTA

In this version inhibitory conductance is placed between the average of the k most active neurons and the n-k remaining neurons.

An intermediate value is computed from:

Depending on the distribution we have in (b) a lower value than before but in (c) a higher value, which gives somewhat better results.

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Projects with kWTAProjects with kWTA

Project inhib.proj.gz in Chapter_3.

Input 10x10,

hidden layer 10x10

2x10 inhibitory neurons,

realistic proportions.

A bi-directional network has a second hidden layer; kWTA stabilizes excitation leaving few active neurons.

Detailed description: section 3.5.2 i 3.5.4

Project inhib_digits.proj.gz in Chapter_3.

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Constraint satisfactionConstraint satisfactionEnvironmental activations, and internal inhibition and activation compatible with the fixed parameters of the network, form a set of constraints on possible states; the evolution of activations in the network should lead to satisfaction of these constraints.

Attractor states maximize harmony between internal knowledge contained in the network parameters and information from the environment.

Attractor dynamics System energy The role of noise

Changes in time, starting from the "attractor basin,” or collection of different starting stages, approach a fixed state.

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Energy Energy The most general law of nature: minimize energy!

What does an energy function look like?

where the summation runs through all the neuron pairs.

Harmony = E is greatest when energy is lowest.

If the weights are symmetrical then the minimum of this energy function is at a single point (point attractor); if not then attractors can be cyclical, quasiperiodic or chaotic.

For a network with linear activation the output is =>

The derivative of harmony = yj shows the direction of growth in harmony.

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The role of noiseThe role of noiseFluctuations on the quantum level as well as the input from many coincidental processes in the greater neural network create noise. Noise changes the moments of impulse transmission, helps to prevent local solutions with low harmony, supplies energy effecting resonance, breaks impasses.

Noise doesn't allow a fall into routine, enables exploration of new solutions, is also probably necessary to creativity.

Noise in the motor cortex.

Demonstration of the role of noise in the visual system.

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Inhibition and constraint satisfactionInhibition and constraint satisfactionConstraint satisfaction is accomplished in the network by a parallel search through the neuron activation space.

Inhibition allows us to restrict the search space, speeding up the search

process if the solution still exists in an accessible area of the state space.

Without kWTA all states in the configuration of two neurons are accessible;

the effect of kWTA restriction is the coordination of activation of both neurons and a decrease in the search space.

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Constraint satisfaction: cats and dogsConstraint satisfaction: cats and dogs

The knowledge encoded in the network is in the table.

The exercise described in section 3.6.4 leads toA simple semantic network which can

Generalize and define unique characteristicsShow the relationship between characteristicsDetermine if characterstics are stableSupplement missing information

Project cats_and_dogs.proj.gz in Chapter_3.

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Constraint satisfaction: Necker cubeConstraint satisfaction: Necker cubeProject necker_cube.proj.gz in Chapter_3.

Bistability of perception: this cube can be seen with its closest face facing either left or right.

Bistable processes can be simulated allowing for noise.

The exercise is described in section 3.6.5.