A Theory of Consciousness:
The Design of the Neuronal Correlate of Consciousness (NCC)-circuit.

Introduction

1.1 The Neuronal Correlate of Consciousness(NCC)-circuit:

The design of a NCC-circuit is based on the assumption that such a mechanism exists in humans and animals. In 1940, the Gestalt psychologist Wolfgang Kohler wrote: “It is now almost generally acknowledged that psychological facts have “correlates” in the biological realm. These correlates, the so-called psycho-physical processes, are events in the central nervous system.” (Kohler, 1940).

Francis Crick and Christof Koch argued that neurobiology can explain consciousness (Crick & Koch, 1992). Without defining “consciousness,” they presented a rationale that “consciousness was to be found in the study of the analytic processing of the visual system”. At the turn of the century, they wrote that “The most puzzling aspect of vision and visual perception is that it gives rise to conscious ‘seeing,’ and that ‘consciousness,’ now viewed as a subjective experience or a sentient sensation, was to be found in a Neuronal Correlate of Consciousness” (NCC). They write ask the question, “To characterize the NCC we have to contrast neural activity that directly gives rise to conscious sensation, thought and action with neuronal activity that is associated with unconscious stereotypes, and on-line visuomotor behavior. Where is the difference between these forms?” (Crick and Koch, 2000, Koch & Crick, 2004).

The question has the answer embedded in it. The NCC must be “that neural activity that directly gives rise to conscious sensations.” Neuronal activity requires a circuit that generates the activity. That circuit is the correlate of consciousness since neuronal activity per se, will always give rise to more neuronal activity, not to a conscious sensation.

In this paper, it is proposed, as a working hypothesis, that a neuronal circuit, which may be defined as a NCC-circuit, is always correlated with conscious sensations. That is, consciousness itself, is an attribute of the NCC. The NCC itself is a neuronal circuit in the brain, a mechanism that may be called a Consciousness Mechanism (CM).

[moved]The design of the NCC-circuit is based on reverse-engineering the assumed functional utility of the modalities of the sensors of the various biological sensory systems. The functional utility of consciousness and the NCC is assumed to result from a Darwinian adaptation that evolved in all animals with a well developed CNS. The functional utility of various modalities is assumed to support the adaptation and survival of the organism in its environmental niche.

“Neuronal activity that directly gives rise to conscious sensation” has been discovered in the biological sensory system (Koch & Crick, 2004). Therefore, the starting point for a design of a NCC-circuit is the modalities of the various sensors of the biological sensory systems. In medical textbooks (Guyton, 1991) and most neuroscience textbooks (Kandel, Schwartz & Jessell, 1991; Gazzaniga, Ivry & Mangun, 2002; Bear, Connors, & Paradiso, 2001), the modalities of sensors are defined in terms of the conscious sensation that they evoke. The law of specific nerve energy is often used to explain the unique conscious sensation that each modality generates (Guyton, 1991; Haines, 2002). The law of specific nerve energy ensures that each type of sensor responds specifically to the appropriate form of stimulus that gives rise to a specific sensation. Furthermore, the specificity of each modality is maintained in the central connections of sensory axons, so that stimulus modality is represented by receptors, afferent axons and central pathways that it activates (Haines, 2002).

The design of the NCC-circuit is based on reverse-engineering the assumed functional utility of the modalities of the sensors of the various biological sensory systems. The functional utility of consciousness and the NCC is assumed to result from a Darwinian adaptation that evolved in all animals with a well developed CNS. The functional utility of various modalities is assumed to support the adaptation and survival of the organism in its environmental niche.

The sensory system selected for the preliminary design of the NCC is the somatosensory system rather than the visual system, as suggested by Crick & Koch (1992). The reasons are: a) The somatic body sensors evolved earlier and the gross features of the design are shared with a large number of vertebrates and invertebrates, b) the modalities of the somatic sensors are very well defined compared with the modalities of the retinal receptors (Rosen & Rosen, 2003b), and c) reverse-engineering the operation of somatic sensors is a relatively less complex task than reverse-engineering the operation of the binocular visual system.

1.2 Reverse Engineering the NCC

Steven Pinker (1997), in his book, How the Mind Works, ponders over a seemingly useless but very complex mechanical device. In a flash of light, the mysteries of the mechanism are solved when he discovers the utility of the mechanism; an “olive pitter,” perfectly engineered to remove the pit from the olive. Just as the utility explains the mechanism of the “olive pitter,” so does the utility of consciousness explain the NCC-circuit. The usual engineering approach for the design of a mechanism that has never before been designed into a machine for which there is no operational definition, is to reverse-engineer the functional utility of the mechanism. However, a operational definition of the CM is not available. Thus, the functional utility of the CM, used throughout this study, is that the CM is an evolutionary adaptation that adheres to Darwin’s law of natural selection and survival of the fittest.

Reverse-engineering an evolutionary adaptation and known connectivity of the human and animal body and brain, may be undertaken following rules published by Daniel Dennett (1995), Williams (1966), and Grafen(1997).

In the following sections a building path is specified for a reverse engineered NCC-circuit. The functional characteristics of the reverse engineered NCC are designed to be similar to the functional characteristics of the biological NCC. The proposed design adheres to Daniel Dennett’s reverse-engineering requirement, “No sound functional analysis is complete until it has confirmed that a building path has been specified.” (Dennett, 1997)

1.3 The Known Functional Characteristics of Consciousness:

The cognitive scientist, Steven Pinker (1997), refers to Ray Jackendoff (1987) and Ned Block (1995), in discussing the four characteristics of “consciousness.” The functional characteristics of the mind or brain include

1. Self Knowledge.

2. The mind builds an internal model of the world that includes the “self.”

3. Access to information or “access consciousness.” Not all the functional information of the brain and body is accessible to the mind. Crick and Koch (1995) determined a set of criteria relating to what one should look for in “access consciousness.”

4. Sentience or “qualia”: A subjective experience, feeling or sensation.

The design of the fourth quality of consciousness is an elusive goal. On the one hand, a large number of functions of the body and brain are described in terms of the CM (i.e. sensory modalities are described in terms of the subjective experiences they evoke. For example, the sentience associated with mechanoreceptors, nociceptors, or the rods and cones in the retina). On the other hand, a “subjective experience” is an attribute (such as experiencing a “force” or the passage of “time”), that can only be a correlate associated with the operation of a material machine or circuit (a spring or clock).

The fourth quality, the sentience characteristic of consciousness, related to “a subjective experience,” requires the assumption of a working hypothesis. The working hypothesis proposed in this analysis is that the “subjective experience” or sentience, the fourth characteristic of consciousness, is an attribute-byproduct present in every organism or machine that is designed to adhere to the first three characteristics enumerated above, and to the law of specific nerve evergy.

Proof of the hypothesis, and the conversion of the hypothesis into a theory will be obtained by reverse-engineering the connectivity of the first three functional characteristics of consciousness. Once an engineering design is obtained, the design may be generalized, and the predictions of the design may be observed and tested. Measuring and testing is performed to determine the extent to which the assumed biological functional utility of the mechanism is also shared by a reverse-engineered machine.

This paper consists of the reverse-engineered design of the connectivity and the neuronal pathways that form a NCC-circuit. It is based on a presentation at the Eighth International Conference on Cognitive and Neural Systems, at Boston University on May 22, 2004 (Rosen & Rosen, 2004a,b)1. The NCC-circuit, called a Relational Robotic Controller (RRC)-circuit, adheres to the first three functional characteristics of consciousness enumerated above. That NCC-circuit is a hybrid neural-network-based robotic controller that emulates the biological functions of the animal brain2.

2. Method

2.1 The Neuroscience Problems Associated with the Design of the NCC:

The design of the RRC “brain-like” controller must satisfy the first three functional characteristics of consciousness and must also adhere to Dennett’s reverse-engineering requirement “No sound functional analysis is complete until it has confirmed that a building path has been specified” (Dennett, 1997). The building path selected to adhere to Dennett’s requirement is to design and build a non-biological RRC brain-like controller that has the first three functional characteristics enumerated above. That is, the RRC controller must control a reverse-engineered mechanical robot that emulates the biological somatic motor system and sensors.

The requirements placed on the design of the RRC leads to reverse-engineering three of the four characteristics of consciousness enumerated above. These functional characteristics translate into four neuroscience problems:

Problem 1: How to build a neural network that adheres to the second “consciousness” constraint: The mind builds an internal model of the world, a “world-space”-coordinate frame that includes the “self” in the center.
The selected approach was to design a “homunculus” in the brain that is defined by the somatic body sensors, and to define the world space around the homunculus by the positions occupied by flailing limbs.

Problem 2: How to give the internal model (RRC-“world-space”-circuit) “self”-knowledge.
The selected approach was to assume that “self” knowledge would be the measured location data related to the robotic “self.” That is, the robot would be required to learn the location of each of its surface parts with respect to and related to the other surface parts. This knowledge was to be gained by learning how to move a robotic limb towards any and every other part of the robotic body. The robot would have the capability to perform an “itch-scratch” response, by moving a limb through a goal directed, obstacle avoiding trajectory aimed at an “itch”-point on the robotic body.

Problem 3: How to design the RRC so that the trajectory of motion is pre-planned and goal-directed with the option of re-planning (obstacle-avoiding) a pre-planned trajectory.

The selected approach was to divide the trajectory of motion into small transitions to adjacent nodes. During each frame period, the total goal directed trajectory is preplanned by the RRC as a sequence of small transitions. However, only the first small transition is activated by the controller and the maximum speed of operation of the robot is one transition per frame period.

Obstacle avoidance along the trajectory generates an additional neuro-scientific constraint on the RRC. The trajectory of motion must be pre-planned with the option available during subsequent frames for re-planning the pre-planned trajectory when an obstacle is detected along the pathway2 (Kandel, Schwartz & Jessell, 1991; Gazzaniga, Ivry & Mangun,1998).

Problem 4: How to train/program the RRC world-map to perform “location” and “itch-scratch” type actions.

The selected approach was to utilize state of the art techniques for training/programming a neural network.

2.1.1 Invoking the consciousness hypothesis: When the first three functional characteristics of consciousness have been successfully designed into a RRC-circuit, The “subjective experience”-hypothesis is invoked. The working hypothesis correlates a subjective experience, the sensation of tactile “feeling,” to the function of the NCC-circuit. An operational definition of tactile consciousness and a tactile theory of consciousness may then be submitted. A generalized theory of consciousness follows by the application of the tactile theory to other sensors with different sensory modalities.

2.2 A World Map-coordinate frame in the Brain:

The primary constraint imposed on the design of the world map-coordinate frame is that the topographic ordering of neural network neurons within the controller form a “model of the external world and place the homunculus of the ‘self’ in the center.” In the animal brain, the origin of the sensory signals, and the destination of the control signals is in the three dimensional space in which the robot/animal is located. In order to form a neural world-map coordinate frame in the neuronal folds in the brain, a transformation is required of the three dimensional external space into the neuronal folds space.

2.2.1. The world map in the brain is based on the observed topographic ordering of neurons in the brain: Figure 1 illustrates the transformation of the neuronal folds in the brain into the 3-dimensional external (mirror) nodal map containing the homunculus of the robot. Validation of such transformations may be obtained by reference to most text books in cognitive neural science, see for example Neuroscience by Bear, Connors & Paradiso (2001, p. 415) and Neuroscience by Dale Purves et al (1997, p. 159). These textbooks show experimental observations of the transformation of the neuronal folds of the somatosensory cortex into a three dimensional mirror nodal map space containing the homunculus of a human.

The location of neurons in the somatosensory cortex is defined by the somatic sensors that are densely located along the peripheral surface area of the body. The transformation of the neuronal folds of the motor cortex into a three dimensional space containing the homunculus of a human is defined by the distribution of muscles/motors throughout the body. Dale Purves et al (1992; 1997, p.161) refers to a variety of observations over the last 40 years of iterated somatotopic and topographical substructures within the sensory cortices. These substructures take the form of units called “brain modules,” each module involving hundreds or thousands of nerve cells in repeating patterns.

Figure 1: Transforming the cortical folds in the brain into a 3-dimensional external-mirror nodal-mapping.

It is assumed that the utility of these iterated patterns is that they may be transformed into a model of the world that includes the “self.” These itererated somatotopic and topographic substructures are reverse engineered into the RRC by neural networks called nodal map modules.

2.2.2. Definition of the “self” at the center of the world map-coordinate frame in the brain: The somatic sensory system is different from other sensory systems in that its receptors are distributed throughout the body. Thus the system is well suited for the definition of the boundary between the “self” and the external world. The mechanoreceptors, embedded in the dermis under glabrous and hairy epidermal layers, have different axonal pathways and stimulate different regions of the brain (Haines 2002, p 46). The mechanoreceptors that define the boundary of the “self” in the external world, are used to define the “self” and the space around the self, in the brain.

The design of the “self” in the RRC controller takes the form of a somatotopic and topographical substructure made up of receiving neurons of a neural network. Each receiving neuron of the somatotopic topographic organization maintains a one to one correspondence with a pressure transducer (mechanoreceptor) located on the somatotopic organization that makes up the “skin” surface. Those receiving neurons that define the “self” in the brain are shown in Figure 2. For visualization convenience the reverse-engineered input-internal world map in the RRC controller is assumed to be configured in three dimensions similar to the external nodal map distribution of tactile sensors rather than being configured similar to the folds in the brain (see figure 1). Each node of the three dimensional internal world map is the location of a receiving neuron, receiving data from a tactile body sensor. The origin of the system may be determined by sensory data from the thoracic cavity transformed into a receiving neuron at the center of the internal set of neurons defining the thoracic cavity. The motion of all body parts may then be determined relative to the internal center (origin) at the thoracic cavity.

		The near space, defined by the position of flailing limbs in the external world, may also be used to define the near space in the internal regions of the brain (see Figures 2, 8 and 9). A neuron may be assigned to a nodal position in the internal near space even though that position is unoccupied by a flailing limb. The receiving neuron at the corresponding node may have tactile data projected on it when and if the corresponding location in the external near space is occupied by a flailing limb. Regions of the external mirror nodal map unoccupied by flailing limbs are defined in the internal map by dormant receiving neurons (in the brain). Neurons in this internal region are activated only when a flailing limb occupies the corresponding position in the external mirror nodal map. The signal originating at each somatic sensor and received by the receiving neuron is designated as the input q-signal. Thus,the position-location of flailing limbs, head, and hips may be determined by the q-input signal, relative to the origin of the coordinate system. All motion of robotic parts occurs relative to the fixed location of the thoracic cavity; flailing limbs, head and neck rotational motion, and hip rotational motion. The location of neurons in the internal nodal map topographic map (located on the brain folds and shown as a three dimensional homunculus in Figure 2), bear a one to one correspondence with the mechanoreceptors that are distributed on the skin surface of the body.
Figure 2: A neuronal world-map coordinate frame within the brain showing the location of neurons that define the “self” and the near-space around the “self”.

These mechanoreceptors are reverse-engineered by pressure transducers distributed along the surface of the robotic body. Thus, a mechanoreceptor signal originating at a tactile sensor located in the external nodal map, is connected via the nervous system to a tactile receiving neuron at the corresponding nodal position in the internal nodal map in the brain. The connectivity in the internal nodal map is between indexed coordinate location and the pressure transducers that simulate the mechanoreceptors.

2.3 Measured “Self” Knowledge:

Self-knowledge in the RRC-circuit consists of data relating to the location of the various bodily parts with respect to the origin of the coordinate frame located within the controller (see Figure 2). The robotic controller is then required to learn the location of each of its surface parts with respect to and related to the other surface parts. This knowledge is gained by learning how to move, first a robotic finger and then all other moveable body parts towards any and every other part of the robotic body. In other words, the robot would have the capability to perform an “itch-scratch” response, by moving a limb or any other moveable part through a goal directed trajectory aimed at an “itch” point, defined as the q-final goal position, located at a mechanoreceptor (pressure transducer) on the robotic body. This “self knowledge” requirement generates a “known” measure of the “self"-space and also a “known” measure of the space surrounding the “self” (the near space).

2.3.1 The trajectory of the initial position, q-initial, of a robotic finger through the world map-coordinate frame:
The initial position of a flailing limb is generally determined by signals received from the muscle and joint proprioceptor receptors. The perception of limb position and movement is mediated by three main types of peripheral receptor that signal the stationary position of the limb and the speed and direction of limb movement:

1. Mechanoreceptors located in joint capsules. The joint proprioceptors respond to changes in the angle, direction, and velocity of movement of the joint.

2. Muscle spindle receptors, mechanoreceptors in muscles that are specialized for the detection of changes in muscle length (stretch).

3. Cutaneous mechanoreceptors from Golgi tendon organs monitor muscle tension, or force of contraction.

There are 2-sub-modalities of limb proprioception: the sense of stationary position of the limb (limb position sense) and the sense of limb movement (kinesthesia), (Kandel, Schwartz, & Jessell, 1991, p. 346).

In the reverse-engineered RRC the limb is replaced by a robotic arm and the proprioceptors are replaced by angle measuring transducers. In order to connect a signal originating at a q-initial position of a robotic finger, in the external (mirror) map, to its corresponding point in the near space of the internal nodal map, one must convert the signals received from the angle measuring transducers into nodal position data. The data/signals obtained from the various proprioceptors include limb position data in the form of joint movement, pressure, tension in the form of muscle-force, and dynamic sensitivity in the form of length and velocity (Haines 2002, p. 258).

The muscles of the biological arm are reverse-engineered by a robotic arm with three motors at each joint replacing each muscle set. The proprioceptors data are reverse-engineered by angle measuring transducers associated with each angular torque generated by each of the three motors. Figure 3 shows the angle measuring transducers located at the joint-gimbals locations of the shoulder and elbow that simulate the output of the proprioceptors. Figure 4 shows the position of a robotic finger that is measured by the angle measuring transducers. The internal nodal map position of q-initial is a function of the gimbals-angle location reading that is transmitted to the RRC-controller at the rate of one reading per frame period. In the design of the robotic arm, an intermediate circuit is required that converts the angle measurements to a location in the near space of the internal nodal map, and transmits the q-initial signal to a receiving neuron at that location. The simplest design of the intermediate circuit is a transformation circuit with angular inputs from the angle transducers (the proprioceptors), and N-output locations to each of the N-neurons that define the (flailing limb) near space.

	Each of the N-outputs is connected by a fiber, one to one, to each of the N-neurons that define the near space (based on the one to one correspondence between the positions in the mirror map and the corresponding positions in the near space).
Figure 3: The angle measuring transducers located at the gimbal positions of the shoulder and elbow. The transducers simulate the output of proprioceptors.		Figure 4: The position of a robotic finger that is measured by the angle measuring transducers shown in figure 3.

A complete solution to the equations of motion of an RRC- circuit for the sensory-motor control of a robotic arm has been developed by Rosen & Rosen (2003d). The RRC-controlled motion of a 29-degree of freedom robotic arm includes the motion of the elbow (qi-elbow) with respect to the shoulder, the motion of the wrist (qi-wrist) with respect to the elbow, the the motion of the knuckles with respect to the wrist (qi-knuckles), and the motion of the fingers with respect to the knuckles. An intermediate circuit converts the angle location measurement in the external nodal map, of a specific part of the robot (elbow, shoulder, hand), into a location measurement of a receiving neuron located in the internal near space. The measurement in the internal space corresponds to the q-initial location of the robotic part in the external space. Any q-initial location measurement in the external map triggers only that fiber output that is connected to the corresponding location of the receiving neuron in the internal near space. This circuit continuously monitors the position of only one part of the robot (e.g. elbow, wrist, hand) and traces a path in the mirror map corresponding to the spatial displacements of that one part (see Figure 4).
A complete and separate neural network circuit is required for each moveable part of the robot. The neural networks for the elbow (qi-elbow), wrist (qi-wrist), hand (qi-hand) etc., must operate simultaneously and hierarchically during each frame period. Thus each near space nodal location may contain multiple neurons and different neural networks for each qi-location-neuron (qi-elbow, qi-wrist, qi-hand etc.) (Rosen & Rosen, 2003d).

2.4 Control of Motion in the World Map in the Brain:

Given the input and output of the RRC-circuit, it is now possible to specify a building path that is functionally similar to the functionality of the brain. In this case the q-initial and the goal position q-final are associated with the somatosensory cortex, and the control p-signals are associated with the motor cortex.

2.4.1 The Conjunction of the Sensory Input Map with the Motor Control Output Map: The topographic distributions of sensory and motor neurons, shown in Figure 5, form a separate input neural network in the region related to the somatosensory cortex, and a separate output neural network in the region related to the motor cortex. In the reverse-engineered design of the RRC these two topographic distributions are combined into a single nodal map module. The combined sensory-input and the motor control output is formed by a conjunction of topographic distributions of sensory neurons and motor neurons. Figure 5 shows the combined sensory and motor control internal world map. In the Figure, a control p-signal originates in the motor cortex and is applied to the nodal map module at the corresponding q-initial position. The q-initial signal originates in the external nodal map, and is applied, via the simulated somatosensory cortex, to the corresponding q-initial position in the nodal map module.

The neuronal distribution of p-generating neurons in the internal world map is determined by the distribution of muscles/motors in the external map. However the point of application of each p-signal in the external map, is at the q-initial location of the robotic part controlled by the p-signal.

Figure 5: A flow diagram of the q-vector and p-vector through a RRC that simulates the functionality of the human brain. The output of the Nodal Map Module goes to the external-mirror nodal-map via the Sequence Stepper and the Control Signal-output Modules.

2.4.2 The Design of the Nodal Map Modules: The number pf p-q nodal map modules required to achieve “self” knowledge by the locomotive (itch-scratch) control of the total body, is equal to the number of joints in the animal body.

Each joint in the animal body is associated with a set of motor control neurons in the brain that may control up to three degrees of freedom per muscle joint (de Duve, 1995)3. For example, the muscles of the upper arm control the motion of the elbow with respect to the shoulder, whereas the muscle set of the lower arm controls the motion of the wrist with respect to the elbow. In each case, three motors and three angle measuring transducers are required at each joint (as shown in Figure 3). The q-initial location of a robotic part associated with each robotic joint is generated by the angle measuring transducer associated with that joint. A p-q nodal map is required for each joint in the animal body. The q-initial position of the part associated with that joint is applied to the corresponding p-q nodal map.

The reverse-engineered brain is a giant parallel processing unit that simultaneously controls all the joints present in the animal body. Any internal representation of motion must accommodate 244-different degrees of freedom that involve more than 600-different body muscles (Nourse, 1964; Saziorski, 1984). Excluding all facial muscles and joints there are approximately 65-joints in the human body (34-arm, 26-leg and 5-hip shoulder and neck (Rosen & Rosen, 2003d)). Therefore, 65-p-q nodal maps may be required in order to achieve “self”-knowledge by the locomotive “itch-scratch” control of the human body. It is assumed that the output signals of each internal nodal map consists of sets of signals that control each muscle set used to displace a particular joint-limb. The total reverse-engineered output map analogous to the topographic distribution of neurons in the motor cortex, consists of all the neurons associated with all the muscle sets of the body.

Figure 6 shows an array of RRC-circuits and the central location of the large number of self location and identification nodal maps that are configured like a world space-coordinate frame. Each nodal map module consists of a conjunction of the q-input and p-output neural networks. The internal nodal maps form a combined control and sensory signal “model of the external world and places the homunculus of the “self” in the center.” Each node of the internal nodal map has a p-signal and q-signal associated with it. The q-signal locates the node in the external nodal map whereas the p-signal causes a displacement to an adjacent node. For example in Figure 5, the displacement of the wrist takes place between a set of three adjacent nodes, from q-initial to q-final within the external nodal map

Figure 6: A hierarchical array of RRCs with the “self”-circuit at the center.

2.5 The Design of the volitional RRC circuit:

In order to have self-knowledge of the “self” that is embedded in the near space, it is necessary to have knowledge of the measure (scale size) of the near space and the "measured location" of any obstacles that may be present along any itch-scratch trajectory of motion. Obastacle avoidance along a trajectory generates an additional neuro-scientific constraint on the RRC. Thus, in addition to learning the “itch-scratch” response, the RRC must adhere to two fundamental constraints that are derived from the cognitive neuroscience literature. The first is that all motion should be pre-planned and end-point goal directed (Kandel, Schwartz & Jessell, 1991; Gazzaniga, Ivry & Mangun, 1998). The second is a volitional constraint that allows a volitional robot the option of re-planning a pre-planned trajectory whenever an obstacle is detected along the path of the pre-planned trajectory4. In order to design the RRC so that the trajectory of motion is pre-planned and goal directed with the option of re-planning a pre-planned trajectory, the trajectory of motion is decomposed into small transitions to adjacent nodes. During each frame period the total goal directed trajectory is determined by the RRC as a pre-planned sequence of small transitions. However, during any frame period, only the first small transition is activated by the controller. And the maximum speed of a robotic part along the trajectory is one nodal transition per frame period. The design of the RRC is constrained to adhere to the cognitive science constraint for goal directed and volitional action. A controller or brain organ that adheres to those two constraints, with a model of the “self” and external world incorporated within it, satisfies a volitional constraint that is consistent with the first two of the three functional characteristics of consciousness. The third constraint, “learning self-knowledge” is equivalent to programming or training a neural network to determine the scale size of each nodal transition and to locate all the “itch” points on the robotic body by performing a “scratch” type trajectory of motion.

2.6 Learning “Self” Knowledge:

The training of the Nodal Map Module proceeds with the requirement that a nodal transition between two adjacent points in the internal nodal map, is a measure of a nodal transition between two adjacent nodes in the three dimensional Euclidean space external to the robot. A trajectory of motion of a robotic part is controlled by a sequence of nodal transitions in the internal space, occurring at the rate of one nodal transition per frame period.

All the nodes of each nodal map module must be trained before it may be used to control the motor actions of the robot. The training of all the nodes of an NCC-circuit, to perform a complete set of “itch-scratch” motor control tasks is described in an article titled “The Engineering Design of a NCC-circuit for the Sensory-motor Control of a Robotic Arm” (Rosen & Rosen, 2003d).

Training the nodal map module is accomplished by use of a modified “Hebbian” learning rule (Hebbs, 1949). Figure 7 is a training flow diagram of the p-vectors and q-vectors through the RRC during one frame period. Two paths are shown in the Figure, a training path and an operational path. Training is performed on the Nodal Map Module and on the Sequence Stepper Module. The Nodal Map training consists of the tabular assignment of a correct set of p-values to each nodal location of the Nodal Map module. The correct p-value, a table-line-entry, is that control signal that causes an exact motor displacement of a robotic part, to an adjacent node, in the external map shown in Figure 5.

The Task Selector Module (TSM) generates the q-final “itch” location and motivates the robot to perform a “scratch” action aimed at the “itch”-point. For training purposes, the q-final “itch” location may be artificially generated by the TSM. The TSM-activated q-final “itch”-location becomes a Task-initiating Trigger (TT) that activates the Sequence Stepper Module to examine the region in the nodal map module, between q-initial and q-final, and select a pre-planned trajectory between q-initial and q-final.

The method of selecting the correct control signal, p-value, required to move a robotic part from q-initial to q-final, is shown in Figure 7. The correct table-line p-value at the initial node is that value that generates an exact displacement from the initial node at q-initial to the adjacent node at q-final. Correction of the table line entry p-value proceeds by noting the displacement error generated by p-initial and applying a correction factor so that p-initial (corrected) leads to an exact transition to the final node defined by q-final (see Figure 7).

The internal nodal map is said to be “trained” if each nodal table, assigned to each node, is made up of a complete set of p-signal line entries. Each p-signal line entry causes an exact motor displacement to an adjacent external nodal position. The set of table line entries assigned to each nodal table, consists of all the p-signals that lead to exact transitions to all adjacent nodal positions. At each node there are 27-p signal transitions to adjacent nodes in a three dimensional nodal map, and 8-p signal transitions to adjacent nodes in a two dimensional nodal map (see figure 5).

In order to train all the nodes in the nodal space, the q-final-TT is initially selected at nodes that are immediately adjacent to a q-initial. For each nodal map, and for each node defined in that map, the training proceeds with the q-final location placed at distances of two, three, four, and more nodal distances from q-initial. When all the nodes of all the nodal maps are fully trained, the RRC-circuit is said to exhibit “self” knowledge. The robot has now learned how to move a robotic limb towards any and every part of the robotic body.

Figure 7: Training the nodal map module: Two paths are shown in the Figure, a training path and an operational path. A p-signal is trained and assigned to a node in the Nodal Map Module when CFI is less than delta. Twenty-seven p-signals must be trained at each nodal position.

2.6.1 A Summary Description of the Design of the RRC Circuit: The electronic design of the RRC-circuit consists of a hybrid set of circuits wherein a portion of the input and output layers are neural net based and intermediate layers are based on algorithmic sequential programming. The input circuit to the RRC is a configured neural-net based world-map where the “self” is at the center of the world map-coordinate frame. Such neural networks circuits have been applied to the study of the adaptive brain by Stephen Grossberg and his associates at Boston University5 (Grossberg, 1987a,b, 1998). The neural net electronic design of the Nodal Map Module, the Sequence Stepper Module, and the Control Signal-output Module is described by Rosen & Rosen (2003c) in a paper titled “The application of the NCC-circuit for sensory-motor control of the somatic motor system.”

Figure 8: The “near”-space is defined by flailing limbs. The region of the “near”-space is an integral part of the “self”-circuit. If survival of the “self” is of prime importance to any organism, then the monitoring and protection of the near-space is second only to “self” survival.		Figure 9: The configuration of the input layer is in the form of a world-space coordinate-frame in the brain, with the “self” in the center. The input layer shows the “self”-locating neurons, the receiving neurons in the near-space surrounding the “self” and the trajectory of q-initial of a robotic finger in the near-space.

The approach of modeling the connectivity of the brain2 has blossomed with the development of powerful neural net-based computational techniques that emulate a large variety of brain functions. Some noteworthy examples are the work of Teovo Kohonen (2001) and Helge Ritter (1992), applied to self organizing maps and micro-structural connectivity in the biological brain, and the prolific work by authors at the Department of Cognitive and Neural Systems at Boston University, related to “self knowledge” and motor control5.

2.7 An example of the connectivity of the tactile Consciousness Mechanism:

The programming of the NCC-circuit, and solution to the equations of motion for the sensory-motor control of a robotic arm, is presented in “The engineering design of a NCC-circuit for the sensory-motor control of a robotic arm” (Rosen & Rosen, 2003d). In order to train the nodal map module with “self-knowledge” it is necessary to go through the training path flow diagram shown in Figures 5 and 7. In this case, given the input to the somatic feature map neural network, and the output of the motor cortex neural network, shown in Figure 5, a complete solution to the design of the nodal map module, may be obtained. The external coordinate frame that defines the near space input-layer associated with the homunculus is illustrated in the Da Vinci diagram shown in Figure 8. The associated configured input layer of the neural network is shown in Figure 9. The neural network solution of a portion of this input-layer, showing the synoptic weights between interconnections, is shown in Figure 10. A pictorial representation of the total neural network used to control the motion of a limb, about a bodily joint with 3-degrees of freedom, is shown in Figure 11 (Rosen & Rosen, 2003d).

	Figure 10: A portion of the configured input layer of the near-space world-map. The map shows the location of qi and qfx for a single trajectory of motion between q-initial and q-final TT. The synoptic weights between neurons are also shown (Rosen & Rosen, 2003d).
		Figure 11: A pictorial representation of a neural network used to control 3-degrees of freedom. The location of brain neurons in the configured input layer, the “self”, the near-space receiving neurons, and the trajectory between qix and qfx is shown in the Figure. The solution to the neural net equations represented by thresholds and the synoptic weights of interconnections is also shown in the Figure (Rosen & Rosen, 2003d).

3. Results

3.1 A Theory of “Consciousness”

The previous sections presented a well defined “building path” for a RRC-circuit that satisfies the first three functional characteristics of consciousness; the formation of a world map in the brain, self-knowledge, and access to information. The RRC-circuit is therefore the NCC-circuit. It is the Consciousness Mechanism (CM) by which a subjective experience, the modality of a sensor, is associated with each activated sensor.

3.1.1 The working hypothesis becomes a postulate: There is general concurrence (Guyton 1991; Kandel, Schwartz & Jessell, 1991; Gazzaniga, Ivry & Mangun, 1998; Haines, 2002; Bear, Connors, & Paradiso 2001; Purvis et al 1997) that the subjective experience, the 4th characteristic of consciousness, is associated with the modality of the sensor, wherein different modalities give rise to different subjective experiences. Therefore the fourth characteristic of the NCC-circuit is a subjective experience or sentience. It is an attribute-byproduct of the operation of a mechanical RRC-circuit and/or a biological NCC-circuit.

3.1.2 Postulate: The “subjective experience” or sentience, the 4th characteristic of consciousness, is an attribute or byproduct present in every organism or machine that is designed to adhere to the first three functional characteristics of “consciousness.” This Postulate is the basis for a theory of consciousness.

3.1.3 Theory of Consciousness: Consciousness is an attribute of the modality of sensory receptors that are connected to the “self identification and location”-circuit, a NCC-circuit, in a manner such that the sensory signal contributes to “self” knowledge within the world map-coordinate frame in the brain.

3.2 The Modality of the Consciousness Mechanism: The somatic sensors respond to at least four kinds of stimuli; touch, temperature, pain and body position. A single sensory receptor can encode stimulus features such as intensity, duration, position, and sometimes direction (Bears, Connors, & Paradiso, 2001, p.398). Sensory receptors produce local graded potentials whose amplitude parallels the amplitude of the stimuli. The graded potentials are produced by modulating the “open times” and hence the permeability of ion channels, shifting the membrane towards or away from the equilibrium potentials for the ions they conduct. The general organization of somatic sensation is given in most medical, physiological and neural science textbooks (for example Guyton 1991 chapter 47, and Haines 2002 Chapter 17). The receptor type (modality), the associated fiber types, the signal transmission characteristics and the type of sensation evoked by each receptor type is also presented by those textbooks.

3.2.1 Self Consciousness. Mechanoreceptors and Nociceptors: Skin performs an essential protective function and prevents the evaporation of body fluids into the dry environment. It is the largest sensory organ we have. Mechanoreceptors are embedded in the dermis under glabrous epidermal layers and hairy epidermal layers. Skin can be vibrated, pressed, pricked, and stroked, and its hairs can be bent and pulled. Thus we may define the “touch-pressure, tap, flutter, vibration“ sentient as a subjective experience mediated by cutaneous (skin) mechanoreceptor modalities. The “intensity” of the experience is determined by the intensity or value code that is present in the signal. The distribution of mechanoreceptors may be determined by a study of 2-point discrimination on the body surface. Finger tips receptors are 2.5-mm apart, lips 5-mm, Big toe 1-centimeter, forearm 3.5-centimeter, back 4.2-centimeter, and calf 4.6-centimeter (Bear, Connors, & Paradiso, 2001, p. 402).

The sensation of pain is mediated by nociceptors embedded in the skin surface and throughout the internal organs. Visceral nociceptors mediate visceral pain (heart, lung irritants, gastrointestinal tract pain, urological tract pain etc.).

3.3 Self Consciousness. Proprioceptors and the Vestibular Apparatus: Proprioceptors and the vestibular apparatus mediate the experience of “self” consciousness. The experience of “self’ consciousness (Joint movement, muscle tension, length and rate of change and velocity) is a subjective experience mediated by muscle and joint proprioceptors modalities. The intensity of the experience is determined by the intensity or value code present in the signal.

Limb proprioception is mediated primarily by muscle afferent fibers (Kandel, Schwartz & Jessell, 1991, p. 346). There are two sub-modalities of limb proprioception: the sense of stationary position of the limb (limb position sense) and the sense of limb movement (kinesthesia). Proprioceptive sensations of the limb generally occur as a consequence of voluntary (or reflexive) movement. In discussing proprioceptive sensations, Kandel, Schwartz & Jessell (1991, p. 337) show that the CM is utilized to determine knee position. For example, it is shown that limb position and kinesthesia are well developed in the absence of voluntary muscle contraction. At rest, the angle of the knee can be evaluated to within 0.5-degrees. Thus the conscious perception of limb position and movement is mediated by the three main type of muscle-joint mechanoreceptors.

The experience of balance or dizziness sensation is a subjective experience mediated by the vestibular apparatus (the organ that detects the sensation of equilibrium) in the inner ear. The orientation sensation and magnitude of the experience is determined by the value codes present in the various signals generated by the apparatus.

Different receptors respond to different mechanical energies (sub-modalities). The different receptors have different axonal pathways (primary afferent axons via the spinal chord and the trigeminal touch pathways for the face mouth and tongue) and stimulate different regions of the brain6.

4. Discussion

4.1 The Utility of the CM:

The CM, also known as the NCC-circuit, is the basis for perception of the external world, self awareness, sensory-motor control, and feedback-awareness of the internal reaction of the body to external forces.

4.1.1 Motor control: Self awareness and self knowledge of the “measure” of the near space (with the self in the center) coupled with self awareness of the internal reactions of the body to external stimuli, are powerful sensory-motor control tools for all locomotive actions performed by all body parts.

The CM mediated by the proprioceptors and the vestibular system and coupled with location “self”-knowledge, operates as a feedback loop that facilitates learning precision control of body motion in an external environment defined by the world map in the brain. “Perception” coupled with “self awareness” generates a powerful tool for designing and building a volitional humanoid robotic system (Rosen & Rosen 2003c). In most modern publications relating to sensory-motor control of the somatic motor system, the power of the CM is rarely taken into consideration and the connectivity associated with it, in the form of a neural representation for sensory-motor control, is never taken into consideration (See for example Guenther et al, 2001).

4.1.2 Perception of the external world. A recording monitor in the brain: The configured input circuit, the tactile self-circuit defined by the modalities of the mechanoreceptors distributed on the skin surface of the body, form a recording monitor in the brain. During the life of the organism, whenever a modality is activated the organism is aware and conscious of it. The constant monitoring of the subjective experience of touch-feeling or pain-pressure enhances Darwinian survival by supporting the Darwinian search engine that gives the organism knowledge of environmental contingencies; danger, food, shelter and mating opportunities. The CM and the Darwinian search engine that is used to search the environment for environmental contingencies that impact the survival of the organism, also gives rise to the subjective experience of “seeing,” “hearing,” “smelling,” and “tasting.”7

4.1.3 Definition of Perception contrasted with identification, recognition, or comprehension: Whenever a sensory receptor signal gives rise to a “conscious” subjective experience then it is said to be “perceived” by the organism. Perception is related to a sensory receptor and the subjective experience generated by that receptor (the modality of the receptor). The consciousness experience does not imply recognition, identification, or comprehension of that part of the world that is perceived. Thus animals may perceive the world with the same resolution as humans, without necessarily identifying, recognizing or comprehending the perceived world.

In order to “recognize” the pattern of excitation falling on the configured input layer, it is necessary to employ a pattern recognition circuit (Rosen & Rosen, 2003f). The configured input layer then becomes the top layer of a multilayered pattern recognition circuit. Rosen & Rosen (2003f) employ such pattern recognition circuits within the brain-controller in order to “recognize” the pattern of excitation falling on the configured input layer, and in order to design a motivational system in the brain (Rosen & Rosen, 2003f). Stephen Grossberg and his colleagues at the Boston University Center For Adaptive Systems have published many research papers related to neural networks that may serve as configured multilayered pattern recognition circuits (Carpenter & Grossberg, 1991)5.

4.1.4 Visual Perception vs. Visual Recognition: During the past half century, since Hubel & Weisel (1962) discovered that different regions of the visual cortex responded to different parts of the visual image (boundaries shapes colors), it was assumed (since the NCC-circuit for perception has not yet been discovered) that the brain was functioning to perform recognition or identification of boundaries, shapes or colors. With this presumption, since recognition or identification must be performed on the total image, a large fraction of the research of the past 40-years has been devoted to finding anatomical brain locations that reconstruct the image from its various parts (boundaries shapes colors), (see for example Grossberg, 2004; Zeki,1993). According to the theory of consciousness, such reconstruction is un-necessary for the perception of an image (Rosen & Rosen, 2003b).

Reconstruction is performed by the CM (the NCC-circuit for visual perception), wherein the different modalities of vision (receptive fields in the retina that specialize in boundary discrimination, shape discrimination, and color discrimination in the light and dark) are combined to form a conscious perception of the total image (Rosen & Rosen, 2003b). Thus, the functional and anatomical research aimed at the study of visual central pathways need not be guided by the requirement that the experimental subject identify or recognize the perceived boundaries, shapes or colors. Instead, the study of the visual central pathways may now be guided by the observation that the specificity of each visual modality is maintained in the central connections of sensory axons, so that stimulus modality is represented by receptors, afferent neurons and central pathways that it activates (Haines et al 2002, p.47).

4.1.5 The relationship of recognition, identification or comprehension to an emotional-CM. A motivational system in the brain: Emotions are subjective experiences that may also be correlated with the CM. In 1884, in an article titled What Is an Emotion William James defined the Neuronal Correlate of Emotion (NCE) as that which “upsets” the equilibrium of an autonomic organic system (James, 1984 1884). Rosen & Rosen (2003f) defined an emotion as the modality of the “upset’ autonomic subsystem. The subjective experience of emotions is a CM that may be used to design a motivational system in the brain. The emotional-CM was used by Rosen & Rosen (2003f) to reverse-engineer the design of an hedonic motivational system in the brain. Figure 12 illustrates closure of the functional loop between the “emotional” motivational system, the control of the somatic motor system,and the control of the body’s autonomic (internal organic) system. It incorporates the NCE-circuit into the operation of an hedonic motivational system in the brain-controller, and the perceptual-NCC-circuit into the operation of a conscious sensory-motor control system of the somatic motor system.

Figure 12: The reverse-engineered functionality of the human brain. The figure illustrates closure of the functional loop between the hedonic motivational system, the somatic motor system, and the autonomic (internal organic) system. It incorporates the perceptual-CM, the NCC-circuit, and the Neuronal Correlate of Emotions (NCE)-circuit into the operation of a hedonic motivational system in the brain. A closed functional loop leads to an integrated system overview of the operation of the total body and brain (Rosen & Rosen, 2003f).

Acknowledgements

The authors acknowledge the support of Machine Consciousness Inc. (MCon Inc.) in the publication of this article. All the data and figures for this article are based on patents and publications relating to the Relational Robotic Controller (RRC)-circuit that have been published in the MCon Inc. website www.mcon.org. The authors are particularly grateful for the financial support and permission to publish the MCon data and Figures, received from Machine Consciousness Inc.

Notes

1. The Rosen & Rosen (2004a) presentation at Boston University is the basis for this paper. The Boston presentation (2004a) was based on MCon publications by Rosen & Rosen (2003a,c,d). The Rosen & Rosen (2004b) presentation at Boston University was based on MCon publications by Rosen & Rosen (2003b,f).

2. The approach of modeling the connectivity of the brain rather than the mind’s symbolic representation of the world was inspired by D. O. Hebb (1949) and Frank Rosenblatt (1958, 1962 p.386). During the past few decades this approach was pursued by many research scientists. Some notable examples are the works of Stephen Grossberg, Gail Carpenter (Grossberg 1988, Carpenter 1991), Teuvo Kohonen (Kohonen 2001), William Bechtel (Bechtel & Abrahamson, 2002), Paul Churchland (Churchland & Sejnowski, 1996) and Helge Ritter (Ritter et al 1992). In the past decade the connectionist methodology has blossomed with the development of powerful neural net-based computational techniques that emulate a large variety of brain functions. For example, the work of Teovo Kohonen and Helge Ritter applied to Self Organizing Maps and micro-structural connectivity in the biological brain, and the prolific work by authors of the department of Cognitive and Neural Systems at Boston University (see note 5).

3. The simultaneous evolutionary adaptation of the organic muscle-joint and the control system (in the brain) that controls the muscle-joint, implies a one to one relationship between each separate muscle set in the body, and the internal p-q nodal map that is defined for the control of the muscle-joint. Thus, the set of equations that control the muscle set associated with one bodily joint, may also control all the muscle sets associated with all the other bodily joints.

4. This is an operational definition of “volition”. For example, if an obstacle appears along the pre-planned “scratch” trajectory of motion, the robot must have the option of re-planning the planned trajectory so as to avoid the obstacle. The robotic controller is said to be a volitional controller if the controlled trajectory of motion is goal directed and pre-planned, with the option available for re-planning the pre-planned trajectory if an environmental contingency is detected prior to reaching the pre-planned goal. Re-planning is always a function of the contingency that appears in the region of the pre-planned path. It is never functionless or random.

5. Stephen Grossberg, Boston University director of Adaptive systems and colleagues and staff at the Department of Cognitive and Neural Systems, are responsible for prolific publications in neural net-works applied to cognition, memory, motor control, speech, and pattern recognition. Stephen Grossberg is especially known for his studies of the brain by means of Pattern Recognition by Self Organizing Neural Networks (1982); Studies of Mind and Brain: Neural Principles of Learning, Perception, Development, Cognition and Motor Control (1980); How Does the Brain Build a Cognitive Code (1980); Adaptive Resonance Theory (ART) models and The Adaptive Self-organization of Serial Order in Behavior Speech Language and Motor Control (1985); The Adaptive Brain vol. I: Cognition Learning Re-enforcement and Rhythm. Vol. II: Vision Speech Language and Motor Control (1987a,b); Neural Netrworks and Natural intelligence (1988); A Cortico-Spinal Model of Reaching and Proprioception Under Multiple Task Constraints (1998); A Neural Model of Smooth Pursuit Control and Motion Perception by Cortical Area MST (Pack et al 1998); A Self Organizing Neural Network architecture for Navigation Using Optic Flow (Cameron et al 1997); Cortical Synchronization and Perceptual Framing (Grossberg and Grunewald 1998); Self Organization and Binocular Disparity Tuning by Reciprocal Corticogeniculate Interactions (Gruenwald and Grossberg, 1996); and Neural Representations for Sensorimotor Control III. Learning a Body Centered Representation of a Three Dimensional Target Position (Guenther et al 2001).

6. Does the CM shed light on the structure of the human brain? Different types of somatic sensory information are necessarily kept separate in the spinal nerves because each axon is connected to only one type of sensory receptor ending. Segregation of sensory types continues within the spinal chord and is largely maintained all the way to the cerebral cortex (Bear, Connors & Paradiso, 2001, p 434). The cortical structures of the somatosensory and motor cortex were reverse engineered in the development of the CM. And although the structure of the reverse- engineered RRC differs significantly from the functional organization and redundancies of the biological brain, the functional similarities coupled with neurological and evolutionary information, may yield significant insight into the biological structure and function of the brain (Rosen, 2003f,g)

7 . The monitoring function leads to sensory “awareness” or sensory “consciousness”. Tactile monitoring results in “touch” sensations by the “self”-circuit. Visual monitoring results in “seeing” sensation by the “self”- circuit. Auditory monitoring results in “hearing” sensation by the “self”-circuit. Etc. In each case, the characteristics and connectivity of the mechanism rests on the Darwinian assumption that “self” awareness is a crucial attribute that evolved by Darwinian natural selection in order to fulfill the Darwinian prerogative to survive and reproduce.

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