Vertebrate brain theory

ISBN 978-3-00-064888-5

Monograph of Dr. rer. nat. Andreas Heinrich Malczan

8   Classification of the neuronal substructures of the vertebrate brain

After the different structures of the vertebrate brain have been dealt with in the individual chapters, a classification of the neuronal substructures is now summarized with the aim of explaining the systematics of signal processing in the vertebrate brain.

The vertebrate brain has the following basic substructures (not a complete list):

Input cores

-        Correspond in the rope ladder system to the sensory centres of the different floors.

-        Receive directly receptor input from the same floor and via class 4 neurons the receptor input from lower floors.

-        Examples: Nucleus cuneatus and nucleus gracilis, torus semicircularis (sensory part), tectum opticum (sensory part), thalamus (sensory parts), amygdala (entrance nucleus: lateral amygdala) etc.

Output cores

-        Correspond in the rope ladder system to the motor centres of the different floors.

-        Receive via class 3 neurons the receptor input of the same floor and via class 5 neurons the output of the higher output cores.

-        Output often reaches motor systems

-        Examples: Nucleus ruber, thalamus (motor part), amygdala (parvocellular part of the basal amygdala) etc.

Mean value cores

-        Correspond in the rope ladder system to the average centres of the different floors.

-        Project partly unspecifically, partly specifically upwards and downwards partly to input and partly to output cores of higher and lower levels as well as to average cores of other transmitter classes.

-        Supply mean value signals to inversion cores for the purpose of signal inversion.

-        Examples: septum, amygdala (magnocellular basal amygdala), nucleus subthalamicus, raphe nuclei, locus caeruleus, nucleus reticularis, nucleus basalis Meynert, nucleus pedunculopontines etc.

-        A disturbance of the function of these nuclei has the consequence that the mean value supply of the inversion nuclei is disturbed. This leads to a disturbance in the formation of the motor or sensory time-sensitive differential images, since the inhibitory component can no longer be generated without error. As a result, disturbances occur which show the symptoms of Parkinson's and Alzheimer's disease. Deep brain stimulation of suitable mean value nuclei or the target structures of their mean value signals can alleviate the symptoms of the disease.

Page change cores

-        Serves the signal change from one side of the body to the other in the rope ladder system

-        Example: Olivar Nucleus

Switching cores

-        Used to switch from one transmitter to another.

-        Example: Substantia nigra pars compacta - is both a mean core and a switch core with specific back projections into the regions of origin.

-        Example: Striosomes.

Inversion cores

-        For signal inversion of signals, require a mean value core as signal supplier or must generate the mean value excitation internally by tapping signals passing through.

-        Examples: Matrix of the striatum, Purkinje nucleus or the cerebellum bark, cerebellar nuclei, globus pallidus, amygdala (central amygdala, medial amygdala, basal amygdala side nucleus, accessory basal nucleus) etc.

Mixed cores with compartmentalization

-        Usually two cores that penetrate each other spatially and each of which fulfils a separate task.

-        Example: Striatum, consists of striosomes and matrix, the striosomes are a switch core from dopamine to GABA, the matrix neurons are inversion neurons that invert the dopaminergic input. To do this, they need the mean signals that they form from the cortex signals that pass through them.

Divergence nuclei or divergence layers

-        The input is transformed by signal divergence into extreme value coded signals.

-        Nucleus olivaris (after formation of the signal divergence)

-        Limbic cortex, cortical amygdala (?), sensory cortex, especially visual cortex V1 etc.

Convergence cores or convergence layers

-        The extreme value coded input is transformed into analog output signals.

-        Magnocellular part of the nucleus ruber, motor cortex

-        Purkinje cells of the early Pontocerebellum

Neural networks as neural structures

-        Cerebellum bark of the Pontocerebellum.

Differential cores

-        Recognize the existing signal differences between two body models

-        If the signals come from different halves of the body, the signals of the half of the body that is stronger prevail. The time delay of a signal component allows movement perceptions.

-        If the signals originate from the same half of the body, one component is usually time-delayed so that the difference image represents a motion image.

Transformation structures - composed of several subsystems

-        Transform the signals of one signal class into signals of another signal class

-        Example: Nucleus olivaris and Spinocerebellum - transforms analog input into maximumcidated output

-        Example: Nucleus ruber, magnocellular part: transforms maximum coded input into analog output

-        Pontocerebellum, for example.

To be precise, all nuclei represent neuron layers, some of which are single-layered, others multi-layered. Therefore, the term layer can often be used instead of nucleus.

Monograph of Dr. rer. nat. Andreas Heinrich Malczan