Biology subjects

Szeier, S.

Publications and source records attributed to Szeier, S..

4 recordsLinked to original sources

bioRxiv2026

Brain circuitry behavioral control emerging from complexity of nested recurrent loops extending into the body

Behaviors and thoughts are driven by a multitude of nested neuronal circuitry loops. They cause complex brain activity dynamics that remain poorly understood. We show that closed-loop neuronal network operation results in an activity state space that can be best understood as a vector field with an attractor point, which controls the activity dynamics across the neuronal population. We show that brain activity in vivo, however, indicates the attractor point is continually moving along a trajectory, which requires the presence of dynamic sensory input or independent activity generation within neurons. Using a spinal network model receiving sensory feedback from a dynamical biomechanical system, we show how these two independent dynamical systems mutually drive each others activity trajectories to generate behavior. Similarly, independent self-generated activity within each thalamic neuron, in closed loop with cortical subpopulations, results in a multitude of dynamical subnetworks that shape each others activity trajectories to control cortical populations. Although the attractor trajectories reflect emergent stability, we show them to be susceptible to criticality effects where minor changes in synaptic inputs can cause the attractor trajectory to switch to cause alternative behaviors. This renders the mutual perturbations between neural and biomechanical dynamics, and between subnetworks within the CNS, an effective operational mode to achieve behavioral flexibility and to simplify learning of apparently complex behaviors. We illustrate how this mode of operation necessitates anticipatory control, thoughts, by the cortex and discuss how it can encompass also the other CNS structures involved in somatic sensorimotor control.

neuroscience↗

bioRxiv2025

Individual cortical neurons innervating large proportions of the neocortical areas in the MouseLight database

1The general notion of the extent of cortical areal interconnectivity in the neuroscience community can in part be due to results from traditional neuroanatomical studies, which later insights have shown to systematically represent underestimates of the extent of the axonal arborizations. But any underestimate of this interconnectivity could in turn be a factor in strengthening the notion of functional localization in the cortex, i.e. the idea that there are circumscribed cortical areas with specific functions that do not to a large extent depend on information being processed in other such cortical areas. Recent advances in neuroanatomical techniques have greatly improved the possibilities to follow the axonal projections of individual neurons in full, and many reconstructed neurons are made available by the MouseLight database. We sampled individual cortical neurons with the majority of their axons within the neocortex and explored the extent of their axonal connections. We found that the efferent axon of individual cortical neurons can commonly cover as much as 30-40% of all types of cortical areas, with a coverage of up to 80% for a single neuron also being demonstrated. Combining the distributions of the axonal trees of more than one cortical neuron, we found that as few as three neurons within one area could reach 100% of the other cortical areas.

neuroscience↗

bioRxiv2025

Global brain circuitry control of behavior emerging from self-governing vector field dynamics in subnetworks

Behavior ultimately depends on the spatiotemporal patterns of the neuron population activity across the brain. Here we address the issue of how the evolving patterns of population activity can be governed by the intrinsically available mechanisms within the brain. We show how the control of the evolving activity can be represented by a high-dimensional vector field, which is an emergent effect of the integrative effects afforded by the membrane capacitance of the neurons and the weights of the synaptic connections between them. For each subnetwork of the brain, its intrinsic connectivity defines the structure of a lower-dimensional vector field with a local attractor point, towards which the subnetwork activity is constantly drawn. We show that other subnetworks, defined by having a degree of independence from but an impact on the first subnetwork, will constantly move the location of the local attractor point, causing the activity in the first subnetwork to constantly chase its own tail. We show how this principle can explain how minor differences in the corticospinal control signal can produce a variety of movement patterns through the spinal interneuron circuitry. At the global level, we show how the cortical neuron population can be thought of as many concatenated subnetworks that produce diverse dynamic evolutions across the cortical neuron population globally. This operational principle can produce a dynamic population activity reminiscent of that observed across the brain in vivo and explain the foundational mechanisms underlying autonomous cortical control of its own activity evolution.

neuroscience↗

bioRxiv2024

Neuronal networks quantified as vector fields

The function of the brain function is defined by the interactions between its neurons. But these neurons exist in tremendous numbers, are continuously active and densely interconnected. Thereby they form one of the most complex dynamical systems known and there is a lack of approaches to characterize the functional properties of such biological neuronal networks. Here we introduce an approach to describe these functional properties by using its activity-defining constituents, the weights of the synaptic connections and the current activity of its neurons. We show how a high-dimensional vector field, which describes how the activity distribution across the neuron population is impacted at each instant of time, naturally emerges from these constituents. We show why a mixture of excitatory and inhibitory neurons and a diversity of synaptic weights are critical to obtain a network vector field with a structural richness. We argue that this structural richness is the foundation of activity diversity in the brain and thereby an underpinning of the behavioral flexibility and adaptability that characterizes biological creatures.

neuroscience↗