On the emergence of cognition: from catalytic closure to neuroglial closure

Abstract

In an analogous manner as occurred during the development of a connected metabolism that at some point reached characteristics associated with what is called "life"-due mainly to a catalytic closure phenomenon when chemicals started to autocatalyze themselves forming a closed web of chemical reactions-it is here proposed that cognition and consciousness (or features associated with them) arose as a consequence of another type of closure within the nervous system, the brain especially. Proper brain function requires an efficient web of connections and once certain complexity is attained due to the number and coordinated activities of the brain cell networks, the emergent properties of cognition and consciousness take place. Seeking to identify main features of the nervous system organization for optimal function, it is here proposed that while catalytic closure yielded life, neuroglial closure produced cognition/consciousness.

Keywords: Closure; Compartmentalization; Consciousness; Equivalence relation; Life; Synchrony.

Fig. 1

Graphic representation of the concept of partition of the brain state space of cell networks (black dots), some showing a relation (e.g., synchrony, solid lines) that determines the equivalence classes separated by dotted lines. In A, there are 7 classes, whereas in B, all nets are related (one can travel form one net to any other) thus forming one class. The scenario in A occurs for n:m frequency locking and that in B for 1:1, so that in the former, specific inputs can be processed in specialized brain areas while in the latter, the processed information can be shared among all necessary brain networks. As such, the simultaneous occurrence of these frequency locking patterns in brain activity favours information processing and normal cognition

Fig. 2

Simple depiction of a partition of the brain synchrony state space using phase (or frequency) locking as a possible equivalence relation, and how it may be relevant to brain information processing. Three cell networks are shown each having neurons that can have activity at the indicated frequencies. The three nets can pass information from A to C in the case of 1:1 locking, A and B oscillating at 8 Hz and B and C at 10 Hz. Thus, for 1:1 locking, there is only one equivalence class composed of the three nets, but for n:m, only B and C can share information by synchronizing in 1:2 locking patterns

Fig. 3

The notion of neuroglial closure is here represented using the integration and segregation of sensorimotor transformations in diverse brain regions, depicted in a very schematic manner where arrows denote pathways taken by information among cell networks. The loop can start anywhere, for instance, after comprehension is reached in certain brain areas (PFC, prefrontal cortex) derived from the processing of specific sensory inputs that is becoming less and less specialized as it advances towards the PFC, an intention to commit an action starts as a relatively long-lasting high-level intention (at the psychological level) and a high-level motor command is triggered in the PMC (premotor cortex) which becomes more and more specialized running through the MC (motor cortex) until specific motor neurons innervating certain muscle cells are activated to perform the action, which in turn generates a proprioceptive input and the sensory processing stars again. Recurrence is a feature of the global processing of information and is as well present at every step, so in reality, all arrows should be double arrows

Fig. 4

Simple possible scheme of a RAF-like net in the nervous system. Seven brain cell networks (capital letters) are represented. The networks A and E are the “food” nets, where inputs (e.g., sensory) are received and enter the system. The discontinuous lines represent anatomical connectivity. The “reactions” or activations in this case (see text for explanations of the translation from the metabolic RAF to the brain) are represented by circles, and the curved arrowed lines represent the activations of the corresponding networks cause by the “reaction”. Let us assume, for simplicity, that all networks have intrinsic activity and depending on the phase synchrony of the arriving inputs from other connected networks, there is activation over a certain threshold that increases the amplitude of the signal (that is, the number of cells active increases) and thus activates other connected nets down the chain of anatomical connections, which results in the so-called functional connectivity. For example, when there is synchrony between the activity in A and in F (represented by r3), then network G becomes active and sends a signal to the connected network B; in this sense, it can be said that synchrony between A and F “catalyzes” the activation of G via r3 and possibly the synchrony between B and C via r4 and r1