Our computational theory [2] of brain and mind includes two major parts rooted in the rich literature about biological brains [3] [4] : (A) dynamically emerging, motivation-free circuits and functions; and (B) motivation based on such circuits and functions.

The computation in the former (A) is carried out by target-precise neuron-to-neuron signal transmissions. Weng & Luciw 2012 [5] and Weng et al. 2013 [6] presented a computational theory for such brain circuits to process information spatial and temporal, respectively, using their distributed, emergent, and non-symbolic representations. As reviewed in those two articles, such brain circuits are also fundamentally different from many existing neural networks cited therein―the brain circuits are not only locally recurrent as many neural networks, but also globally recurrent in the sense that they all use motor as input concepts. As explained in Weng & Luciw [7] , the brain motors (or actions) correspond to all possible concepts that a human can learn and express from conception, through prenatal life, birth, childhood, infancy, and adulthood―such as location, type, scale, temporal context, goal, subgoal, intent, purpose, price, ways to use, and so on. These concepts are used by the brain circuits as states, like states in a Finite Automaton (FA) [8] , but such an FA is emergent and non-symbolic to be explained below.

The computation in the latter (B) is based on target-imprecise diffusion of neural transmitters that diffuse across brain tissue. Weng et al. 2013 [9] proposed a model for how reinforcement learning is carried out in such emergent brain circuits through two types of transmitter systems―serotonin and dopamine. Wang et al. 2011 [10] present a model about how individual neurons use two other types of transmitter systems―acetylcholine and norepinephrine―to automatically estimate uncertainty and novelty, so that each neuron can decide where it gets inputs from. These four types of neural transmitter systems―serotonin, dopamine, acetylcholine and norepinephrine―along with other neural transmitters but seemingly relatively less important than these four types [11] , amount to what we know as motivation. Various emotions are special cases of motivation [3] [4] .

This paper will not further discuss the motivation part of a biological brain and will instead concentrate on the former―(A) the basic brain circuits and functions. In other words, the theory below models any emotion-free brain. DNs with emotion such as pain avoidance and pleasure seeking will be only briefly discussed in Section 9.

This theory here does not claim that the FA-based brain model is indeed complete for an emotion-free brain, because there is no widely accepted and rigorous definition of a natural phenomenon such as a brain and therefore, there is always some limitation for any theory to explain a natural phenomenon. As such, as any theory can only approximate a natural phenomenon but can never exhaust such an approximation. The Newtonian physics is a good example because it is refined by the relativity theory.

All computational networks fall into two categories: Symbolic Networks (SNs) and Emergent Networks. The former category uses symbolic representations and the latter uses emergent representations. See the review for symbolic models and emergent models in Weng 2012 [12] which tried to clarify some common misconceptions on representations.

The class of SN [13] includes Finite Automata (FA), Markov Chains, Markov Models, Hidden Markov Models (HMM), Partially Observable Markov Decision Processes (POMDP), Belief Networks, Graphical Models, and all other networks that use at least some symbolic representations. The HMM and other probability-based models in the above list are symbolic because they add probability to the symbolic FA basis and therefore the basic nature of their representations is still symbolic―adding probability does not change the nature of symbolic representation. We will use FA as an example for SN because any SN includes FA as its basis.

The class of Emergent Network includes all neural networks that use exclusively emergent representations, such as Feed-forward Networks, Hopfield Networks, Boltzmann Machines, Restricted Boltzmann Machines, Liquid State Machines, and Reservoir Computing, and the newer Developmental Networks (DNs) [14] . However, traditional neural networks are not as powerful and complete as DN, because they do not have the logic of FA as explained first in [14] and we will be proved for DN in this paper.

The major differences between a Symbolic Network (SN) and a Developmental Network (DN) are illustrated in Figure 1.

Marvin Minsky 1991 [15] and others correctly argued that symbolic models were logical and clean, while connectionist models were analogical and scruffy. Neural networks are called emergent networks here because some networks were not emergent and partially symbolic. Michael Jodan 2014 [16] correctly raised fundamental questions that many researchers have not paid sufficient attention to. The logic capabilities of emergent networks are still unclear, categorically. This paper addresses some fundamental issues that Michael Jordan raised [16] recently.

Computationally, feed-forward connections serve to feed sensory features [17] to motor area for generating behaviors. It has been reported that feed-backward connections can serve as class supervision [18] , attention [19] , and storage of time information.

The work of Finite Automata (FA) played a major role in our theory about the brain. The work of Weng 2011 [14] and 2013 [20] was not the first to relate a network with an FA. Some researchers used neural networks to batch-com- pile a special kind of FA. Frasconi et al. 1995 [21] used a feed-forward network to explicitly compute the state transition function of an FA. Their network requires: 1) a special canonical binary coding of the states so that the Hamming distance is 1 between any source state q and any target state; 2) an additional intermediate state is added if the source state q and target state q are the same; 3) the entire state transition function δ is known a priori so that their algorithm can directly compute all the weights as a batch (i.e., programmed, instead of learned incrementally). This compiled network uses a layer of logic-AND nodes followed by a layer of logic-OR nodes. Frasconi et al. 1996 proposed a radial basis function as an alternative batch-compiled feed-forward network for the above logic network [22] since a finite number of samples are sufficient for completely characterizing the FA due to its symbolic nature. Omlin & Giles 1996 [23] proposed a second-order network for computing the state transition function of a fully given FA. By second order, the neuronal input contains the sum of weighted multiplications (hence the second order), between individual state nodes and individual input nodes. There does not seem to be known evidence that a biological neuron uses such a product. The network Omlin & Giles 1996 is also statically “programmed” by a human programmer based on a fully given FA. They positively contributed to neural network studies.

The above studies aimed successfully compute the state transition function using a programmed network, but they do not generate emergent representations, do not learn from observations of FA operations, do not deal with natural input images (or patterns), and do not deal with natural motor images (or patterns), and not incrementally learn.

As far as we know, the DN in Weng 2011 [14] was the first general-purpose emergent FA that

1) uses fully emergent representations,

2) allows natural sensory firing patterns,

3) allows the motor area to have subareas where each subarea represents either an abstract concept (location, type, scale, etc.) or natural muscle actions (e.g., driving a car or riding a bicycle),

4) uses a general-purpose and unified area function that does not need interactive approximation and does not have local minima in its high dimensional and nonlinear but non-iterative approximation,

5) learns incrementally―taking one-pair of sensory pattern and motor pattern at a time to update the network and discarding the pair immediately after―and,

6) uses an optimization scheme in which every update of the network realizes the maximum likelihood estimate of the network, conditioned on the limited computational resources in the network and the limited learning experience in the network’s “life time”.

Explained in Weng 2012 [2] , the DN model is inspired by biological brains, especially brain anatomy (e.g., [24] [26] ) and brain physiological experiments (e.g. [19] [26] ). But we will use computational language in the following material, so that the material is understandable by an analytical reader.

In the following, we analyze how the DN theory bridges the symbolic school and the connectionist school in artificial intelligence (AI). First, Section 2 presents the algorithm for the Developmental Program (DP) of the DN. Section 3 gives a temporal formulation of FA to facilitate understanding the brain theory. Then, Section 4 proposes that the framework for FA is complete. All FAs in this paper are Deterministic FA. So, we call them simply FAs. How a DN learns incrementally from an FA is discussed in Section 5. The three theorems are presented and proved in Section 6 through Section 8.

Theorem 1 states that for any FA that operates in real time, there is an emergent DN that learns the FA incre- mentally. It observes one state-and-input pair from the FA at a time, learns immediately and becomes error-free for all the FA transitions that it has learned, regardless how many times a transition has been observed―one is sufficient but more results in better optimality in the real world. The DN is equivalent to the part of FA that corresponds to all transitions that have demonstrated so far.

Theorem 2 establishes that if the FA-learned DN is frozen―computing responses only but not updating its adaptive parts, the frozen DN is optimal in the sense of maximum likelihood when it takes inputs from infinitely many possible cases in the world.

Theorem 3 asserts that the FA-learned DN, if it is allowed to continue to learn from infinitely many possible cases in the world, is optimal in the sense of maximum likelihood.

Section 9 briefly discusses experiments of DN. Section 10 provides concluding remarks and discussion.

The small DP algorithm self-programs logic of the world into a huge DN based on experiences in its physical activities. A DN has its area Y as a “bridge” for its two banks, X and Z, as illustrated in Figure 2(b).

Biologically, a DP algorithm models the collective effects of some genome properties of the cells of the nervous system―neurons and other types of cells in the nervous system [3] [4] [27] . Thus, in nature, the DP is a result of evolution across many generations of a species. The DP seems to be a more systematic way to understand natural intelligence than studies the response of a child or adult brain.

In artificial intelligence, a DP algorithm is the result of human understanding of the development of natural intelligence followed by a human DP design based such understanding. This approach, known as developmental approach [2] [28] , short-cuts the long and expensive process of cross-generation evolution.

Some parameters of DP (e.g., the number of cells in Y) could be experimentally selected by a genetic algorithm, but the DP as a whole seems to be extremely expensive for any artificial genetic algorithm to reach without handcrafting (e.g., see the handcrafted area function below).

Human design of DP algorithm [28] seems to be a more practical way to reach human-like mental capabilities and human-level performance in robots and computers for two main reasons: 1) Fully automatic development of intelligence (i.e., task-nonspecific and fully automatic learning) is the approach that the natural intelligence takes and has demonstrated success; 2) The design of the DP algorithm is a clean task, in contrast to traditional AI― modeling intelligence itself―which is a muddy task [2] [29] .

The quality in a human-designed DP, when the DP is widely used in the future, greatly affects all the capabilities in the developmental robots and computers that use the DP.

In the DN, if Y is meant for modeling the entire brain, X consists of all receptors and Z consists of all effectors―muscle neurons and glands. Additionally, the Y area of the DP can also model any Brodmann area in the brain and if so, the X and Z correspond to respectively, the bottom-up areas and top-down areas of the Brodemann area. From the analysis below, we can also see that the Y area of the DN can model any closely related set of neurons―Brodmann area, a subset, or a superset.

The most basic function of an area Y seems to be prediction―predict the signals in its two vast banks X and Z through space and time.

Algorithm 1 (DP) Input areas: X and Z. Output areas: X and Z. The dimension and representation

of X and Y areas are hand designed based on the sensors and effectors of the species (or from evolution in biology). Y is the skull-closed (inside the brain), not directly accessible by the outside.

1) At time, for each area A in, initialize its adaptive part and the response vector, where V contains all the synaptic weight vectors and stores all the neuronal ages. For example, use the generative DN method discussed below.

2) At time, for each A in repeat:

a) Every area A performs mitosis-equivalent if it is needed, using its bottom-up and top-down inputs and, respectively.

b) Every area A computes its area function, described below,

where is its response vector and and are the adaptive part of the area defined above, before and after the area update, respectively. Note that is not part of the domain of because is the model for any area A, not just for an individual neuron of A. Thus, does not use iterations, efficiently approximating lateral inhibitions and internal excitations.

c) For every area A in, A replaces: and.

The DN must update at least twice for the effects of each new signal pattern in X and Z, respectively, to go through one update in Y and then one update in Z to appear in X and Z.

In the remaining discussion, we assume that Y models the entire brain. If X is a sensory area, is always supervised. The is supervised only when the teacher chooses to. Otherwise, gives (predicts) motor output.

The area function, which is based on the theory of Lobe Component Analysis (LCA) [30] , is a model for self-organization by a neural area. Each area A has a weight vector. Its pre-response vector is:

which measures the degree of match between the directions of and.

To simulate lateral inhibitions (winner-take-all) within each area A, only top- winners are among the competing neurons fire. Considering, the winner neuron is identified by:

The area dynamically scale top-k winners so that the top-k respond with values in. For, only the single winner fires with response value and all other neurons in A do not fire. The response value approximates the probability for to fall into the Voronoi region of its where the “nearness” is.

All the connections in a DN are learned incrementally based on Hebbian learning―cofiring of the pre- synaptic activity and the post-synaptic activity of the firing neuron. If the pre-synaptic end and the post- synaptic end fire together, the synaptic vector of the neuron has a synapse gain. Other non-firing neurons do not modify their memory. When a neuron fires, its firing age is incremented and then its synapse vector is updated by a Hebbian-like mechanism:

where is the learning rate depending on the firing age (counts) of the neuron and is the retention rate with. Note that a component in the gain vector is zero if the corresponding component in is zero.

The simplest version of is which corresponds to:

where is the firing time of the post-synaptic neuron. The above is the recursive way of computing the batch average:

The initial condition is as follows. The smallest in Equation (3) is 1 since after initialization. When, the initial value of on the right side of Equation (3) is used for pre-response competition to find this winner, but the initial value of does not affect the first-time updated on the left side since.

In other words, any initialization of weight vectors will only determine who win (i.e., which newly born neurons take the current role) but the initialization will not affect the distribution of weights at all. In this sense, all random initializations of synaptic weights will work equally well―all resulting in weight distributions that are computationally equivalent. Biologically, we do not care which neurons (in a small 3-D neighborhood) take the specific roles, as long as the distribution of the synaptic weights of these neurons lead to the same computational effect. This neuronal learning model leads to the following conjecture.

Conjecture 1 In a small 3-D neighborhood (e.g., of a hundred nearby neurons), neural circuits are so different across different biological brains that mapping the detailed neuron wiring of brain is not informative at the level of individual neuron.

The NIH Connectome program aims to “map the neural pathways ... about the structural and functional connectivity of the human brain. ... resulting in improved sensitivity, resolution, and utility, thereby accelerating progress in the emerging field of human connectomics”. The DN theory and the above conjecture predict that such an NIH program is not as scientifically useful as the NIH program hoped in terms of understanding how the brain works and future studies of abnormal brain circuits. For the brain, “more detailed connectomics data” seems to be not as productive as more complete and clear theories.

In this section, we present an FA as a temporal machine, although traditionally an FA is a logic machine, driven by discrete event of input.

As we need a slight deviation from the standard definition of FA, let us look at the standard definition first.

Definition 1 (Language acceptor FA) A finite automaton (FA) M is a 5-tuple, where Q is a finite set of states, consists of symbols. Σ is a finite alphabet of input symbols. is the initial state. is the set of accepting states. is the state transition function.

This classical definition is for a language acceptor, which accepts all strings x from the alphabet Σ that belongs to a language L. It has been proved [8] that given any regular language L from alphabet Σ, there is an FA that accepts L, meaning that it accepts exactly all but no other string not in L. Conversely, given any FA taking alphabet Σ, the language L that the FA accepts is a regular language. However, a language FA, just like any other automata, only deals syntax not semantics. The semantics is primary for understanding a language and the syntax is secondary.

We need to extend the definition of FA for agents that run at discrete times as follows.

Definition 2 (Agent FA) A finite automaton (FA) M for a finite symbolic world is a 4-tuple, where Σ and are the same as above and is a finite set of states, where each state is a symbol, corresponding to a set of concepts. The agent runs through discrete times, starting from state at. At each time, it reads input and transits from state

to, and outputs at time, illustrated as.

The inputs to an FA are symbolic. The input space is denoted as, which can be a discretized version of a continuous space o input. In sentence recognition, the FA reads one word at a time. The number l is equal to the number of all possible words―the size of the vocabulary. For a computer game agent, l is equal to the total number of different percepts.

The outputs (actions) from a language acceptor FA are also symbolic, which can also be a discretized version of a continuous space of output. For a sentence detector represented by an FA, when the FA reaches the last state, its action reports that the sentence has been detected.

An agent FA is an extension from the corresponding language FA, in the sense that it outputs the state, not only the acceptance property of the state. The meanings of each state, which are handcrafted by the human programmer but are not part of the formal FA definition, are only in the mind of the human programmer. Such meanings can indicate whether a state is an accepting state or not, along many other meanings associated with each state as our later example will show. However, such concepts are only in the mind of the human system designer, not something that the FA is “aware” of. This is a fundamental limitation of all symbolic models. The Developmental Network (DN) described below do not use any symbols, but instead (image) vectors from the real-world sensors and real-world effectors. As illustrated in Figure 2, a DN is grounded in the physical environment but an FA is not.

Figure 3 gives an example of the agent FA. Each state is associated with a number of cognitive states and actions, shown as text in the lower part of Figure 3, reporting action for cognition plus a motor action. The example in Figure 3 shows that an agent FA can be very general, simulating an animal in a micro, symbolic world. The meanings of each state in the lower part of Figure 3 are handcrafted by, and only in the mind of, the human designer. These meanings are not a part of the FA definition and are not accessible by the machine that simulates the FA.

Without loss of generality, we can consider that an agent FA simply outputs its current state at any time, since the state is uniquely linked to a pair of the cognition set and the action set, at least in the mind of human designer.

The FA-in-DN framework is useful for understanding how a DN works. However, FA itself is handcrafted by a human teacher, or in other words, the behaviors of an autonomously developed human teacher.

It has been proved [8] that an FA with n states partitions all the strings in Σ into n sets. Each set is called equivalence class, consisting of strings that are indistinguishable by the FA. Since these strings are indistinguishable, any string x in the same set can be used to denote the equivalent class, denoted as. Let Λ denote an empty string. Considering Figure 3, the FA partitions all possible strings into 6 equivalent classes. as the agent does not know about “calculus” although it is in Σ. All the strings in the equivalent class [Λ] end in. All strings in the equivalent class end in, etc.

From the above discussion, we can see that the key power of an FA is to lump very complex equivalent contexts into equivalent classes.

A Turing Machine (TM) [8] [31] is a 5-tuple T =, where Q is the set of states, and are the input and tape alphabets, respectively, with, q₀ is the initial state, and is the transition function:

where is the blank symbol not in, h denotes the halt state, and R, L, S denote the head motion, right, left, and stationary, respectively. Consider the following two definitions:

1) Define to include also the tape write action w and the head move action m:

Each state in is a three tuple (q, w, m) where w and m can be empty.

2) Let.

The above transition function for TM becomes the transition function of an FA:.

Therefore, the controller of any TM is an FA. A grounded DN can learn the FA perfectly. It takes input from the real word and its action can include head write and head motion. A TM is not grounded, but the DN is grounded: A TM senses from, and acts on, a tape but a DN senses from, and acts on, its real-world physical environment.

The completeness of agent FA-in-DA can be described as follows. Given a vocabulary S' representing the elements of a symbolic world, a natural language L is defined in terms of S' where the meanings of all sentences (or events) in L are defined by the set of equivalent classes, determined by Q' of FA-in-DN. When the number of states is sufficiently large, a properly learned FA-in-DN can sufficiently characterize the cognition and behaviors of an agent living in the real physical world with vocabulary S'.

This argument is based on the following observation: as long as the context state is properly learned so that it contains all the information that is necessary and sufficient for generating the following states, then with sensory input correctly selected from a cluttered scene should be sufficient to

generate the next state:.

As a simple example, an FA-in-DN can accept the context-free language, the set of all

strings that consist of n a’s followed by the same number of b’s, by simulating how a TM works on a tape to accept the language L.

The Chomsky hierarchy [31] after the work of Norm Chomsky in particular and the automana and languages theory in classical computer science [8] [31] regard only Turing Machines as general-purpose programming machine because they mainly consider only the syntax of a computer language, not the rich semantics that a symbol can represent. However, a symbolic state q and an input symbol σ can practically represent any set of meanings. Yet, the meanings of general purpose with Turing Machines and FA-in-DN are different: with a TM, it means what kind of sequence of computations the TM program can represent. With the latter FA-in-DN, it means the richness of meaning any symbol (q and σ) can represent so that the FA-in-DN can represent any emergent state-based agent that has a finite memory.

In particular, it is important to note that a state can remember very early event [2] [6] : e.g. an event needed by can be contained in, , etc.

But FA-in-DN goes beyond the symbolic AI, because it automatically develop internal representations― emergent.

Next, let us consider how a DN learns from any FA. First we consider the mapping from symbolic sets S and q to vector spaces X and Z.

Definition 3 (Symbol-to-vector mapping) A symbol-to-vector mapping m is a one-to-one mapping. We say that and are equivalent, denoted as if.

A binary vector of dimension d is such that all its components are either 0 or 1. It simulates that each neuron, among d neurons, either fires with a spike or without at each sampled discrete time. From discrete spikes, the real valued firing rate at time t can be estimated by

, where T is the temporal size for averaging. A biological neuron can fire at a maximum

rate around spikes per second, producible only under a laboratory environment. If the brain is sampled at frequency, we consider the unit time length to be second. The timing of each spike is precise up to second at the sampling rate f, not just an estimated firing rate v, which depends on the temporal size T (e.g., s). Therefore, a firing-rate neuronal model is less temporally precise than a spiking neuronal model. The latter, which DN adopts, is more precise for fast sensorimotor changes.

Let denote the d-dimensional vector space which contains all the binary vectors each of which has at most p components to be 1. Let contains all the binary vectors each of which has exactly p com- ponents to be 1.

Definition 4 (Binary-p mapping) Let. A symbol-to-vector mapping is a binary-p mapping if m is one to one, that is, if then implies.

The larger the p, the more symbols the space of Z can represent. However, through a binary-p mapping, each symbol always has a unique vector. Note that different q’s are mapped to different directions of unit z’s.

Suppose that a DN is taught by supervising binary-p codes at its exposed areas, X and Z. When the motor area Z is free, the DN performs, but the output from Z is not always exact due to (a) the DN outputs in real numbers instead of discrete symbols and (b) there are errors in any computer or biological system. The following binary conditioning can prevent error accumulation by suppressing noise and normalizing the spikes as 1, which the brain seems to use through spikes.

Definition 5 (Binary conditioning) For any vector from, the binary conditioning of z forces every real-valued component to be 1 if the pre-response of is larger than the machine zero―a small positive bound estimating computer round-off noise.

The binary conditioning must be used during autonomous performance as long as the Z representations use spikes, instead of firing rates. Machines zeros are noises from computer finite precision in representing a number. The binary conditioning suppresses the accumulation of such computer generated round-off errors. Because the Z representation is binary by definition, the binary conditioning forces the real numbers to become 0 or 1 only. However, the actual value of machine zero is computer dependent, depending on the length to represent a real number. In particular, the case of a constant Z vector of all ones will not appear incorrectly because all noises components that are meant to be 0 are set back to 0.

The output layer Z that uses binary-p mapping must use the binary conditioning, instead of top-k competition with a fixed k, as the number of firing neurons ranges from 1 to p.

Algorithm 2 (DP for GDN) A GDN is a DN that gives the following specific way of initialization. It starts from pre-specified dimensions for the X and Z areas, respectively. X represents receptors and is totally determined by the current input. But it incrementally generates neurons in Y from an empty Y (computer programming may use dynamic memory allocation). Each neuron in Z is initialized by a synaptic vector v of dimension 0, age 0. Suppose is the current synaptic vectors in Y. Whenever the network takes an input, compute the pre-resppnses in Y. If the top-1 winner in Y has a pre-response lower than 2 (i.e.,), simulate mitosis-equivalent by doing the following:

1) Increment the number of neurons,

2) Add a new Y neuron. Set the weight vector, its age to be 0, and its pre-response to be 2 since it is the perfect match based on Equation (1). There is no need to recompute the pre-responses.

The response value of each Z neuron is determined by the starting state (e.g., background class). As soon as the first Y neuron is generated, every Z neuron will add the first dimension in its synaptic vector in the following DN update. This way, the dimension of its weight vector continuously increases together with the number c of Y neurons.

In this section, we establish the most basic theorem of the three, Theorem 1. First, we give an overview. Next, we establish a lemma to facilitate the proof of Theorem 1. Then, we present Theorem 1. Finally, we discuss grounded DN.

The next theorem states how the frozen GDN generalizes for infinitely many sensory inputs.

Theorem 2 (DN generalization while frozen) Suppose that after having experienced all the transitions of the FA from time, the GDN turns into a DN that

1) freezes: It does not generate new Y neurons and does not update its adaptive part.

2) generalizes: It continues to generate responses by taking sensory inputs not restricted to the finite ones for the FA.

Then the DN generates the Maximum Likelihood (ML) action, recursively, for all integer:

where the probability density is the probability density of the new last observation

, with the parameter vector, conditioned on the last executed action, based on its experience gained from learning the FA.

Proof. Reuse the proof of the lemma. Case 1 does not apply since the DN does not generate new neurons. Only Case 2 applies.

First consider Y. Define Voronoi regions in based on now frozen, where each consisting of vectors that are closer to than to other:

Given observation, V has two sets of parameters, the X synaptic vectors and the Z synaptic vectors. They are frozen.

According to the dependence of parameters in DN, first consider c events for area Y: falls into, partitioned by the c Y vectors in V. The conditional probability density is zero if falls out of the Voronoi region of:

where is the probability density within. Note that the distribution of within is irrelevant as long as it integrates to 1.

Note that. Given, the ML estimator for the binary vector needs to maximize, which is equivalent to finding

since finding the ML estimator for Equation (15) is equivalent to finding the Voronoi region to which belongs to. This is exactly what the Y area does, supposing for top- competition.

Next, consider Z. The set of all possible binary-1 Y vectors and the set of producible binary-p Z vectors have a one-to-one correspondence: corresponds to if and only if the single firing neuron in has non-zero connections to all the firing neurons in the binary-p but not to the non-firing neurons in. Namely, given the winner Y neuron, the corresponding vector is deterministic. Furthermore, for each Y neuron, there is only unique z because of the definition of FA. Based on the definition of probability density, we have:

for every corresponding to. Thus, when the DN generates in Equation (16) for ML estimate, its Z area generates ML estimate that maximizes Equation (14).

There seems no more proper terms to describe the nature of the DN operation other than “think”. The thinking process by the current basic version of DN seems similar to, but not exactly the same as, that of the brain. At least, the richness of the mechanisms in DN that has demonstrated experimentally to be close to that of the brain.

Theorem 3 (DN generalization while updating) Suppose that after having experienced all the transitions of the FA from time, the GDN turns into a DN that

1) fixes its size: It does not generate new Y neurons.

2) adapts: It updates its adaptive part.

3) generalizes: It continues to generate responses by taking sensory inputs not restricted to the finite ones for the FA.

Then the DN “thinks” (i.e., learns and generalizes) recursively and optimally: for all integer, the DN recursively generates the Maximum Likelihood (ML) response: with

where is the probability density, conditioned on,. And the has the pre-response vector, where, , is the conditional probability for the -th Z neuron to fire:

The firing of each Z neuron has a freedom to choose a binary conditioning method to map the above the pre-response vector to the corresponding binary vector.

Proof. Again, reuse the proof of the lemma with the synaptic vectors of Y to be now adapting.

First consider Y. Equation (16) is still true as this is what DN does but V is now adapting. The probability density in Equation (15) is the currently estimated version based on past experience but V is now adapting. Then, when for top-k Y area competition, the Y response vector with j determined by Equation (16) gives Equation (17). In other words, the response vector from area is again the Maximum Likelihood (ML) estimate from the incrementally estimated probability density. The major difference between Equation (16) and Equation (17) is that in the latter, the adaptive part of the DN updates.

Next, consider Z. From the proof of the Lemma 1, the synaptic weight between the j-th Y neuron and the n-th Z neuron is

The total pre-response for the n-th neuron is

since the j-th neuron is the only firing Y neuron at this time. The above two expressions give Equation (18).

The last sentence in the theorem gives the freedom for Z to choose a binary conditioning method but a binary conditioning method is required in order to determine which Z neurons fire and all other Z neurons do not. In the brain, neural modulation (e.g., expected punishment, reward, or novelty) discourages or encourages the recalled components of z to fire.

The adaptive mode after learning the FA is autonomous inside the DN. A major novelty of this theory of thinking is that the structure inside the DN is fully emergent, regulated by the DP (i.e., nature) and indirectly shaped (i.e., nurture) by the external environment.

The neuronal resource of Y gradually re-distribute according to the new observations in. It adds new context-sensory experience and gradually weights down prior experience. Over the entire life span, more often observed experience and less often observed experience are proportionally represented as the synaptic weights.

However, an adaptive DN does not simply repeat the function of the FA it has learned. Its new thinking experience includes those that are not applicable to the FA. The following cases are all allowed in principle:

1) Thinking with a “closed eye”: A closed eye sets where u has 0.5 for all its components (all gray image). The DN runs where Y responses mainly to z as x has little “preference” in matching.

2) Thinking with an “open eye”: In the sensory input x is different from any prior input.

3) Inconsistent experience: from the same, the next may be different at different times. FA does not allow any such inconsistency. However, the inconsistencies allow occasional mistakes, update of knowledge structures, and possible discovery of new knowledge.

The neuronal resources of Y gradually re-distribute according to the new context-motor experience in. The learning rate amounts to equally weighted average for past experience by each neuron. Weng & Luciw 2009 [30] investigated amnesic average to give more weight to recent experience.

In the developmental process of a DN, there is no need for a rigid switch between FA and the real-world learning. The mitosis-equivalent of Y neurons is gradually realized by gradual mitosis and cell death, neuronal migration and connection, neuronal spine growth and death, and other neuronal adaptation. DN can also switch between neuronal initialization and adaptation smoothly. The rigid switches between neuronal initialization and neuronal adaptation and between FA learning and the real-world learning above are meant to facilitate our understanding and analysis only.

The binary conditioning is suited only when Z is supervised according to the FA to be simulated. As the “thinking” of the DN is not necessarily correct, it is not desirable to use the binary conditioning for Z neurons. For example, a dynamic threshold can be used for to pass in order for the -th neuron to fire at value 1. This threshold can be related to the punishment and reward from the environment. In general, the threshold is related to the neural modulatory system.

The thinking process by the current basic version of DN seems similar to, but not exactly the same as, that of the brain. At least, the richness of the mechanisms in an experimental DN is not yet close to that of an adult brain. For example, the DN here does not use neuromodulators so it does not prefer any signals from receptors (e.g., sweet vs bitter).

Due to the focused theoretical subject here and the space limitation, detailed experimental results of DN are not included here. The DN has had several versions of experimental embodiments, called Where-What Networks (WWNs), from WWN-1 [32] to WWN-7 [33] . Each WWN has multiple areas in the Z areas, representing the location concept (Location Motor, LM), type concept (Type Motor, TM), or scale concept (Scale Motor, SM), and so on.

A learned WWN can simultaneously detect and recognize learned 3-D objects from new unobserved cluttered natural scenes [5] [34] .

The function of this space-time machine DN differs depending on the context information in its Z area [7] . If there is no Z signal at all, the WWN is in an (emergent) free-viewing mode and it detects any learned object from the cluttered scene and tells its location from LM, type from TM, and scale from SM. If the LM area fires representing a location (intent or context), the WWN recognizes the object near that intended location from the cluttered scene and tells its type from TM and scale from SM. If the TM area fires representing an object type (intent or context) the WWN finds (i.e., detects) an intended object type from the cluttered scene and tells its location from LM and scale from SM.

A WWN can also perform autonomous attention. If the DN suppresses the firing neuron that represents an object type in TM, the WWN switches attention from one object type to another object type that barely lost in the previous Y competition―feature-based autonomous attention. If the DN suppresses the firing neuron in LM, the WWN switches attention from one object location to another object location that barely lost in the previous Y competition―location-based autonomous attention.

The WWN has also performed language acquisition for a subset of natural language and also generalized and predicted [35] . For example, predict from one person Joe to his hierarchical properties such as male and human, and predict from Penguin to its hierarchical properties such as non-flying and bird.

The WWNs have versions that are motivated, such as pain avoidance and pleasure seeking, so that its learning does not need to be supervised [9] . The learned tasks include object recognition under reinforcement learning and autonomous foraging (wandering around) in the presence of a friend and an enemy.

However, the experimental results from such DN experiments are difficult to understand and to train without a clear theoretical framework here that links DNs with the well-known automata theory and the mathematical properties presented as the three theorems that have been proved here.

Proposed first in Weng 2011 [14] , the DN framework seems to be, as far as I know, the first brain-scale computational and developmental theory for the brain and mind. By developing, we mean that the model regards brain areas should automatically emerge from activities, instead of fully specified rigidly by the genome. This view is supported by a great deal of cross-modal plasticity found in mammalian brains, from eye deprivation by Torsten N. Wiesel and David H. Hubel [36] , to the auditory cortex that processes visual information by Mriganka Sur et al. [37] , to the reassignment of modality―visual cortex is reassigned to audition and touch in the born blind as reviewed by Patrice Voss [38] .

Therefore, it appears that a valid brain model at least should not assume a static existence of―genome rigidly specified―Brodmann areas. This static existence has been prevailing in almost all existing biologically inspired models for sensorimotor systems. Instead, a brain model should explain the emergence and known plasticity of brain areas. DP enables areas to emerge in DN and adapt. The genome provides the power of cells to move and connect. The genome also plays a major role in early and coarse connections of a brain. However, fine connections in the brain seem to be primarily determined by the statistics of activities from the conception of the life all the way up to the current life time.

In conclusion, this paper provides an overarching theory of the brain and mind, although the complexity of the mind is left to the richness of the environment and the activities of DN―task nonspecific [28] . The paper also provides the proofs of the three basic theorems in an archival form. At this early time of the computational brain theory, we predict that the landscape of AI and understanding of natural intelligence would both fundamentally change in the future.

The author would like to thank Hao Ye at Fudan University who carefully proof-read the proofs presented here and raised two gaps that I have filled since then. The author would also like to thank Z. Ji, M. Luciw, K. Miyan and other members of the Embodied Intelligence Laboratory at Michigan State University; Q. Zhang, Yuekai Wang, Xiaofeng Wu and other members of the Embodied Intelligence Laboratory at Fudan University whose work has provided experimental supports for the theory presented here.