Abstract
Cellular network architecture plays a crucial role as the structural substrate for the brain functions. Therefore, it represents the main rationale for the emerging field of connectomics, defined as the comprehensive study of all aspects of central nervous system connectivity. Accordingly, in the present paper the main emphasis will be on the communication processes in the brain, namely wiring transmission (WT), i.e. the mapping of the communication channels made by cell components such as axons and synapses, and volume transmission (VT), i.e. the chemical signal diffusion along the interstitial brain fluid pathways. Considering both processes can further expand the connectomics concept, since both WT-connectomics and VT-connectomics contribute to the structure of the brain connectome. A consensus exists that such a structure follows a hierarchical or nested architecture, and macro-, meso- and microscales have been defined. In this respect, however, several lines of evidence indicate that a nanoscale (nano-connectomics) should also be considered to capture direct protein-protein allosteric interactions such as those occurring, for example, in receptor-receptor interactions at the plasma membrane level. In addition, emerging evidence points to novel mechanisms likely playing a significant role in the modulation of intercellular connectivity, increasing the plasticity of the system and adding complexity to its structure. In particular, the roamer type of VT (i.e. the intercellular transfer of RNA, proteins and receptors by extracellular vesicles) will be discussed since it allowed us to introduce a new concept of ‘transient changes of cell phenotype’, that is the transient acquisition of new signal release capabilities and/or new recognition/decoding apparatuses.
Dedicated to: This article is dedicated to Kjell Fuxe, Tomas Hökfelt and Sylvester Vizi for their pioneering findings on transmitter-identified neuronal systems and innovative views on intercellular communication processes in the brain.
Acknowledgments
This work was supported by Grant 60A06-0515/15 from the University of Padova to DG.
Appendix
Just for illustrative purposes, an abstract model of a neuron potentially implementing both WT and VT modes of intercellular communication is briefly described here. It is based on the so-called neurotransmitter field theory, originally proposed by Greer (2007).
According to this approach, the physical quantity that considered a vehicle of information is the concentration of transmitters in the ECS and the neuron is seen as a processing element transforming an input (recognized) transmitter distribution into an output (released) transmitter distribution. In order to visualize (see Figure 5) how this computation can be performed on a neurotransmitter cloud, let us proceed through two steps:
We can imagine the dendrites of the neuron as a tree with its branches inside the cloud. The surface of the tree is ‘painted’ with a shade of gray corresponding to its sensitivity to the neurotransmitter. When multiplied by the actual concentration of the neurotransmitter in the ECS and integrated over the dendritic tree surface, we shall obtain a first-order approximation of the neuron’s response. More formally, if h(x, y, z) (x, y, z ∈ H=ECS) represents the neurotransmitter cloud, μ(x, y, z) (x, y, z ∈ dendritic surface) is the sensitivity to the transmitter (e.g. the distribution of specific receptors) and σ is the usual sigmoidal activation function (Rumelhart et al., 1986), the neuron’s response (α) will be
This relationship can be further refined by introducing a ‘dendritic-membrane transfer function’ (χd (h)) accounting for the inherent nonlinear relationship between neurotransmitter concentration in the ECS and the gating of ion channels on the dendritic surface. Thus, we obtain
(1)The neuron also has an axonal tree which releases the neurotransmitter into the extracellular space. Let τ(x, y, z) be the function that quantitatively describes the output of the neuron in terms of the spatial distribution of the chemical transmitter it generates. As a first-approximation, the product of the neuron’s response α and the output function τ will provide the output transmitter cloud g(x, y, z). However, to also take into account the intrinsic nonlinear response corresponding to the release of neurotransmitter by the axon terminals as a function of the neuron firing rate, an ‘axonal-membrane transfer function’ (χa (α)) will also be introduced. Thus, we can finally write
(2)where different symbols for the spatial coordinates were used in order to differentiate the input manifold from the output one.
![Figure 5: Schematic view of the abstract model of neuron.The extracellular space (H) surrounding the dendritic tree hosts a cloud of neurotransmitter [h(x, y, z)]. The surface of the dendritic tree is represented with a gradient of gray levels to indicate its variable sensitivity [μ(x, y, z)] to the transmitter. χd , χa are the transfer functions (see the text) and σ is the activation function. The axonal tree is also represented with shades of gray to indicate the non-homogeneous spatial distribution of its release [described by the function τ(x, y, z)]. It generates in the extracellular space (G) a cloud g(x, y, z) of the released neurotransmitter.](/document/doi/10.1515/revneuro-2016-0051/asset/graphic/j_revneuro-2016-0051_fig_005.jpg)
Schematic view of the abstract model of neuron.
The extracellular space (H) surrounding the dendritic tree hosts a cloud of neurotransmitter [h(x, y, z)]. The surface of the dendritic tree is represented with a gradient of gray levels to indicate its variable sensitivity [μ(x, y, z)] to the transmitter. χd , χa are the transfer functions (see the text) and σ is the activation function. The axonal tree is also represented with shades of gray to indicate the non-homogeneous spatial distribution of its release [described by the function τ(x, y, z)]. It generates in the extracellular space (G) a cloud g(x, y, z) of the released neurotransmitter.
It was demonstrated (see Greer, 2007) that, when limited to the synaptic transmission (i.e. when the functions μ and τ are discrete), this model for neurons is computationally equivalent to the discrete classical models (Vogels et al., 2005) based on neuronal networks and synaptic weights.
This way to look at the neurotransmission, however, could quite easily accommodate features that can hardly be described in the conventional neuronal network-based models. In particular, the following extensions can likely be easily implemented:
Different types of cells: the input-output map described by (2) can be applied with no formal changes to any other cell. Each cell type, of course, will have its own transfer functions, input and output surfaces.
Different types of WT/VT: each of them can be described following the same strategy outlined before, i.e. by an input cloud h(x, y, z), a sensitivity μ(x, y, z) and a release function τ(x, y, z).
Changes in the structure of the ECS: the spatial variables (x, y, z) used in (1) and (2) refer to the ECS. Thus, changes in ECS, such as changes in the geometry of the ECS pathways or in the efficient diffusion coefficient (Syková and Nicholson, 2008), will lead to changes in the neurotransmitter clouds and in the system dynamics.
Changes in cell phenotype: the acquisition of new recognition/decoding or release capabilities (occurring, for instance, as a consequence of the roamer type of VT) can also be easily implemented in the model. In fact, from a formal point of view, it corresponds to appropriate changes of the sensitivity μ(x, y, z) and/or release τ(x, y, z) functions.
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