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The brain-mesh model introduces a novel three-layered architecture that integrates local and macro-regional connectivity with an underlying, mesh-inspired network layer. This foundational mesh layer, based on metallic mesh structures, spans the entire brain and generates interference patterns, noise, and resonance effects that modulate both local and global neural dynamics. The fused model goes beyond traditional connectivity frameworks by providing a unified explanation for phenomena such as brain-wide phase gradients, stable low-frequency resonance frequencies, and long-range plasticity effects, which are often difficult to explain cohesively within existing models. In addition to accounting for classical neurobiological observations, such as phase synchrony, functional connectivity fluctuations, and local Hebbian plasticity, the model offers novel insights into less understood phenomena. Specifically, it predicts connectivity-independent phase gradients across non-synaptic regions, harmonic resonance peaks consistent across individuals, and diffuse plasticity driven by global interference patterns, all of which are challenging to explain under current frameworks. These unique predictions align with partial empirical observations, such as traveling wave dynamics, consistent low-frequency oscillations, and task-induced connectivity shifts, underscoring the model's relevance. Additionally, the brain-mesh model generates testable hypotheses that distinguish it from traditional approaches. This provides a promising framework for future experimental validation and opens new avenues for understanding global brain function., Comment: This manuscript was prepared with assistance from AI tools for language refinement, drafting support, and formula verification. The preprint was originally posted on bioRxiv on 25 November 2024. Available at https://www.biorxiv.org/content/10.1101/2024.11.22.624908v1

Performing simulations with a realistic biophysical auditory nerve fiber model can be very time consuming, due to the complex nature of the calculations involved. Here, a surrogate (approximate) model of such an auditory nerve fiber model was developed using machine learning methods, to perform simulations more efficiently. Several machine learning models were compared, of which a Convolutional Neural Network showed the best performance. In fact, the Convolutional Neural Network was able to emulate the behavior of the auditory nerve fiber model with extremely high similarity ($R^2 > 0.99$), tested under a wide range of experimental conditions, whilst reducing the simulation time by five orders of magnitude. In addition, we introduce a method for randomly generating charge-balanced waveforms using hyperplane projection. In the second part of this paper, the Convolutional Neural Network surrogate model was used by an Evolutionary Algorithm to optimize the shape of the stimulus waveform in terms energy efficiency. The resulting waveforms resemble a positive Gaussian-like peak, preceded by an elongated negative phase. When comparing the energy of the waveforms generated by the Evolutionary Algorithm with the commonly used square wave, energy decreases of 8% - 45% were observed for different pulse durations. These results were validated with the original auditory nerve fiber model, which demonstrates that our proposed surrogate model can be used as its accurate and efficient replacement., Comment: 39 pages, 10 figures

The presence of diffuse Focal Axonal Swellings (FAS) is a hallmark cellular feature in many neurodegenerative diseases and traumatic brain injury. Among other things, the FAS have a significant impact on spike-train encodings that propagate through the affected neurons, leading to compromised signal processing on a neuronal network level. This work merges, for the first time, three fields of study: (i) signal processing in excitatory-inhibitory (EI) networks of neurons via population codes, (ii) decision-making theory driven by the production of evidence from stimulus, and (iii) compromised spike-train propagation through FAS. As such, we demonstrate a mathematical architecture capable of characterizing compromised decision-making driven by cellular mechanisms. The computational model also leads to several novel predictions and diagnostics for understanding injury level and cognitive deficits, including a key finding that decision-making reaction times, rather than accuracy, are indicative of network level damage. The results have a number of translational implications, including that the level of network damage can be characterized by the reaction times in simple cognitive and motor tests., Comment: 18 pages, 10 figures

The extracellular space has an ambiguous role in neuroscience. It is present in every physiologically relevant system and often used as a measurement site in experimental recordings, but it has received subordinate attention compared to the intracellular domain. In computational modeling, it is often regarded as a passive, homogeneous resistive medium with a constant conductivity, which greatly simplifies the computation of extracellular potentials. However, recent studies have shown that local ionic diffusion and capacitive effects of electrically active membranes can have a substantial impact on the extracellular potential. These effects can not be described by traditional models, and they have been subject to theoretical and experimental analyses. We strive to give an overview over recent progress in modeling the extracellular space with special regard towards the concentration and potential dynamics on different temporal and spatial scales. Three models with distinct assumptions and levels of detail are compared both theoretically and by means of numerical simulations: the classical volume conductor (VC) model, which is most frequently used in form of the line source approximation (LSA); the very detailed, but computationally intensive Poisson-Nernst-Planck model of electrodiffusion (PNP); and an intermediate one called the electroneutral model (EN). The results clearly show that there is no one model for all applications, as they show significantly different responses especially close to neuronal membranes. Finally, we list some common use cases for model simulations and give recommendations on which model to use in each situation., Comment: 12 pages (incl. references), 4 figures (color)

The evolution of spiking neurons and nervous systems in the late Ediacaran period simultaneously with the evolution of carnivores around 550 million years ago can be explained by the need for accurately timed decisions under an imminent threat of being eaten. A simple model shows that threshold triggering devices, spiking neurons, are utility-maximizing decision-makers for the timing of escape reflexes given the sensory cues available to Ediacaran animals at the onset of carnivory. Decisions are suboptimal for very weak stimuli, providing selection pressure for secondary processing of primary spike train data. A simple network can make approximately Bayes optimal decisions given stochastic spike trains. Decisions that are arbitrarily close to Bayes optimal can be obtained by enlarging this network. A subnetwork that computes the Bayesian posterior density of the critical state variable - distance between predator and prey - emerges as a core component of the decision-making mechanism. This is a neural analog of a Bayesian particle filter with cerebellar-like architecture. The model explains fundamental properties of neurons and nervous systems in modern animals and makes testable predictions.

The grid cells (GCs) of the medial entorhinal cortex (MEC) and place cells (PCs) of the hippocampus are key elements of the brain network for the metric representation of space. Currently, any of the existing theoretical models can explain all aspects of GC-specific spatially selective patterns of activity. It is also not clear how these patterns are formed during the network development. On the other hand, it was previously shown that the network that can learn to extract high principal components from the activity of the place cells could provide the basis for GC-like activity patterns development. Supporting this hypothesis is the finding that PC activity remains spatially stable after the disruption of the GC firing patterns and that grid patterns almost disappear when hippocampal cells are deactivated. Development of the early PCs before GCs formation also supports the role of PCs as the spatial information providers to GCs. Here we have developed a new theoretical model of grid fields formation based on synaptic plasticity in synapses connecting PCs in hippocampus and neurons in entorhinal cortex. Learning of hexagonally symmetric fields in the model is due to complex action of several simple biologicaly motivated synaptic plasticity rules. These rules include associative synaptic plasticity rules similar to BCM rule and homeostatic plasticity rules which restrict synaptic weigths. In contrast to previously described models, this network could learn GC patterns after a very short experience of navigation in a novel environment. In conclusion we suggest that learning on the basis of simple and biologically plausible associative synaptic plasticity rules could contribute to the formation of grid fields in early development and to maintainence of normal GCs activity patterns in familiar environments.

A recent paper in a prominent science magazine claims to show that humans can discriminate at least 1 trillion odors. The authors reached that conclusion after performing just 260 comparisons of two smells, of which about half could be discriminated. Furthermore the paper claims that the human ability to discriminate smells vastly exceeds our abilities to discriminate colors or musical tones. Here I show that all these statements are wrong by astronomical factors. A reanalysis of the authors' experiments shows they are also consistent with humans discriminating just 10 odors. The paper's extravagant claims are based on errors of mathematical logic. Further analysis highlights the importance of establishing how many dimensions the perceptual odor space has. I review some arguments on the topic and propose experimental avenues towards an answer., Comment: 14 pages, 4 figures. Revised version has same technical content, more introduction for non-experts, more thoughts in the discussion

I provide a convolution metric which takes neural membrane potential recordings as arguments and compares their subthreshold features along with the timing and number of spikes within them--summarizing differences in these with a single "distance" between the recordings. Based on van Rossum's 2001 metric for spike trains, the metric relies on a convolution operation that it performs on the input data. The kernel used for the convolution is carefully chosen such that it produces a desirable frequency space response and, unlike van Rossum's kernel, causes the metric to be first order both in differences between nearby spike times and in differences between same-time membrane potential values: an important trait. 31 pages, 4 figures., Comment: 31 pages, 4 figures

The Hodgkin-Huxley (HH) model is the currently accepted formalism of neuronal excitability. However, the HH model does not capture a number of biophysical behaviors associated with action potentials or propagating nerve impulses. Physical mechanisms underlying these processes, such as reversible heat transfer and axonal swelling have been separately investigated and compartmentally modeled to indicate the nervous system is not purely electrical or biochemical. Rather, mechanical forces and principles of thermodynamics also govern neuronal excitability and signaling. To advance our understanding of neural function and dysfunction, compartmentalized analyses of electrical, chemical, and mechanical processes need to revaluated and integrated into more comprehensive theories. The present quantitative perspective is intended to broaden the awareness of known biophysical phenomena, which are often overlooked in neuroscience. By starting to consider the collective influence of the biophysical forces influencing neural function, new paradigms can be applied to the characterization and manipulation of nervous systems., Comment: 13 pages

The cortical magnification matrix M is introduced founded on a notion similar to that of the scalar cortical magnification factor M. Unlike M, this matrix is suitable to describe anisotropy in cortical magnification, which is of particular interest in the highly gyrified human cerebral cortex. The advantage of our tensor method over other surface-based 3D methods to explore cortical morphometry is that M expresses cortical quantities in the corresponding sensory space. It allows us to investigate the spatial relation between sensory function and anatomical structure. To this end, we consider the calcarine sulcus (CS) as an anatomical landmark for the primary visual cortex (V1). We found that a stereotypically formed 3D model of V1 compared to a flat model explains an excess of cortical tissue for the representation of visual information coming from the horizon of the visual field. This suggests that the intrinsic geometry of this sulcus is adapted to encephalize a particular function along the horizon. Since visual functions are assumed to be M-scaled, cortical folding can serve as an anatomical basis for increased functionality on the horizon similar to a retinal specialization known as visual streak, which is found in animals with lower encephalization. Thus, the gain of surface area by cortical folding links anatomical structure to cortical function in a previously unrecognized way, which may guide sulci development., Comment: 22 pages, 6 figures

The experimental part of this study has shown that hunger motivation may be evoked by a long-term (10=180 s) continuous electrical stimulation of the "hunger center" at a current of 133.6{\pm}8.1 mkA. Positive emotions were caused by electrostimulation at the same current intensity but short-term duration (0.3-0.5 s). A positive feeling elicited by electrostimulation of the motivation center can be explained in terms of the adaptation (polarization) theory of motivation and emotion (Murik, 2001, 2005)., Comment: 17 pages, 4 figures; Journal article in Russian