Biology subjects

Jorntell, H.

Publications and source records attributed to Jorntell, H..

15 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.

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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.

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bioRxiv2024

Sensory dependencies rapidly and autonomously yield generalizable representations in recurrent cortical-like networks

How biological brains become operational from development and onwards is an unresolved issue. Here we explored the emerging effects in a brain circuit model that received simulated touches through biological skin model, which featured the potentially critical aspect of sensory dependencies resulting from mechanical coupling across the tissue. Our skin model system was connected to a recurrent network a generic, primitive cortical-like subnetwork that was composed of fully connected excitatory and inhibitory neurons, where each individual synapse was subject to continuous, independent, Hebbian-like learning. We used continuous random mechanical activations of the skin model, in this regard mimicking behavioral patterns of early brain development observed in infants, and let the neuron-independent learning define the function that emerged in the network. Remarkably, we found that the network could rapidly learn to separate various naive dynamic skin inputs and solve a kinematics task it had never encountered, even when substantial parts of the sensor population or even network connections were removed post-training. We propose that autonomous learning from a sensor population with intrinsic dependencies could cause the extensively recursive cortical network to gradually adapt its intrinsic dynamics to better mirror the various dynamics of whatever body it is connected to, which result in many biologically useful features for the early acquisition of brain circuitry function.

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.

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bioRxiv2024

High-dimensional cortical signals reveal rich bimodal and working memory-like representations among S1 neuron populations

Complexity is important for flexibility of natural behavior and for the remarkably efficient learning of the brain. Here we assessed the signal complexity among neuron populations in somatosensory cortex (S1). To maximize our chances of capturing population level signal complexity, we used highly repeatable resolvable visual, tactile and visuo-tactile inputs and neuronal unit activity recorded at high temporal resolution. We found the state space of the spontaneous activity to be extremely high-dimensional in S1 populations. Their processing of tactile inputs was profoundly modulated by visual inputs and even fine nuances of visual input patterns were separated. Moreover, the dynamic activity states of the S1 neuron population signaled the preceding specific input long after the stimulation had terminated, i.e. resident information that could be a substrate for a working memory. Hence, the recorded high dimensional representations carried rich multimodal and internal working memory-like signals supporting high complexity in cortical circuitry operation.

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bioRxiv2023

Local field potential sharp waves with diversified impact on cortical neuronal encoding of haptic input

Cortical sensory processing is greatly impacted by internally generated activity. But controlling for that activity is difficult since the thalamocortical network is a high-dimensional system with rapid state changes. Therefore, to unwind the cortical computational architecture there is a need for physiological landmarks that can be used as frames of reference for computational state. Here we use a waveshape transform method to identify conspicuous local field potential sharp waves (LFP-SPWs) in the somatosensory cortex (S1). LFP-SPW events triggered short-lasting but massive neuronal activation in all recorded neurons with a subset of neurons initiating their activation up to 20 ms before the LFP-SPW onset. In contrast, LFP-SPWs differentially impacted the neuronal spike responses to ensuing tactile inputs, depressing the tactile responses in some neurons and enhancing them in others. When LFP-SPWs coactivated with more distant ECoG-SPWs, suggesting an involvement of these SPWs in global cortical signaling, the impact of the LFP-SPW on the neuronal tactile response could change substantially, including inverting its impact to the opposite. These cortical SPWs had similar overall activity patterns as reported for hippocampal SPWs and may be a biomarker for a particular type of state change that possibly involves both hippocampus and neocortex.

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bioRxiv2023

Singular superlet transform achieves markedly improved time-frequency super-resolution for separating complex neural signals

Time-frequency decomposition is a well-established method to unmix signals generated by multiple sources with unique characteristics. However, there are cases of high signal complexity where existing time-frequency decomposition tools are insufficient for localizing and representing short-bursting signals. One example is the currently highly popular extracellular low-impedance recordings from multi-electrode arrays in the brain in vivo where each neuron repeatedly generates a specific signal fingerprint (characteristic spike waveform) that can be mixed with the signals of 100s of other sources, including the spikes of nearby neurons. Here we derive the singular superlet transform (SST) method, which enables highly localized representations of fast and short bursts compared to other super-resolution spectral estimators, while also requiring orders of magnitude fewer operations. We demonstrate a substantial edge of SST over current methods in isolating specific neuronal spikes with high-fidelity in challenging, complex recording signals from neocortex in vivo. We also exemplify SSTs generic signal processing capability by achieving outstanding resolution in the decomposition of complex acoustic data.

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bioRxiv2022

Visual Input Dynamically Changes Responses to Spatiotemporal Tactile Input Patterns in S1 Neurons

To understand how sensory events are represented in and perceived by the brain, one must understand how varying internal brain states affect neuronal decoding of sensory input. Recent studies indicate global state changes in the brain impact the representation of haptic events in neurons of the primary somatosensory cortex (S1). It could be argued that the manipulations used so far to alter the cortical circuitry behavior were artificial and not reflective of normal information processing in the neocortex. In the present study we therefore wanted to explore if natural visual stimulation also could impact the interpretation of given tactile input patterns. We recorded the unitary extracellular responses to a set of spatiotemporal tactile input patterns presented either alone or together with simultaneously multicolor flashing lights from a large number of neurons in parallel in the rat primary somatosensory cortex (S1). We found that the visual input, mildly but consistently altered the temporal spike outputs to tactile input patterns in S1 neurons. We argue that the visual input change the global cortical state to an extent that it affects the cortical representation of haptic events even within the S1 and that this is an indication that the cortical network in its information processing may be far more reliant on globally distributed network dynamics than traditionally thought.

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bioRxiv2022

Profound impact of hippocampal output on the interpretation of tactile input patterns in SI neurons

Due to continuous state variations in neocortical circuits, individual SI neurons in vivo display a variety of intracellular response types to repeated presentations of the exact same tactile input pattern. The specific intracellular response obtained depends on a time-evolving combination of the specific input with the current neocortical state. To manipulate the internal cortical state, we here used brief electrical stimulation of the output region of the hippocampus, which preceded the delivery of specific tactile afferent input patterns to digit 2 of the anesthetized rat. We find that hippocampal output had a diversified and remarkably strong impact on the specific set of intracellular response types each SI neuron displays to each given tactile input pattern. The findings show that hippocampal output can profoundly impact the state-dependent interpretation of tactile inputs in SI neurons and hence influence their perception, potentially with affective and semantic components.

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bioRxiv2021

The BCM rule allows a spinal cord model to learn rhythmic movements

Animal locomotion is hypothesized to be controlled by a central pattern generator in the spinal cord. Experiments and models show that rhythm generating neurons and genetically determined network properties could sustain oscillatory output activity suitable for locomotion. However, current CPG models do not explain how a spinal cord circuitry, which has the same basic genetic plan across species, can adapt to control the different biomechanical properties and locomotion patterns existing in these species. Here we demonstrate that rhythmic and alternating movements in pendulum models can be learned by a monolayer spinal cord circuitry model using the BCM learning rule, which has been previously proposed to explain learning in the visual cortex. These results provide an alternative theory to CPG models, because rhythm generating neurons and genetically defined connectivity are not required in our model. Author summaryThe central pattern generator is the leading hypothesis of locomotor control in animals. There, rhythm generating neurons and genetically defined neural connectivity would form a circuit generating activity patterns suitable for locomotion. We provide a new hypothesis of locomotor control, where rhythmic patterns are learned by a Hebbian learning rule from a mechanical system that has an intrinsic tendency to oscillate.

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bioRxiv2021

The dynamics of touch sensing studied in a mass-spring-damper model of the skin

The mammalian skin is a densely innervated soft tissue where embedded neural mechanoreceptors report to the brain the mechanical events arising at its surface. Up to now, models of the transformations of these events into neural signals relied on quasistatic or viscoelastic mechanical models of the skin. Here, we employed a model which, in addition to elasticity and viscosity, accounted for mass to accurately reproduce the propagation of mechanical waves observed in vivo. Skin dynamics converted sensory inputs into rapidly evolving spatiotemporal patterns that magnified the information made available to a population of mechanoreceptors. Accounting for dynamics greatly enhanced the separability of tactile inputs and was efficient for a large range of mechanical parameter values. This advantage vanished when these parameters were set to approximate the quasistatic or viscoelastic cases.

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bioRxiv2021

A Model for Self-Organization of Sensorimotor Function: The Spinal Monosynaptic Loop

Recent spinal cord literature abounds with descriptions of genetic preprogramming and the molecular control of circuit formation. In this paper we explore to what extent circuit formation based on learning rather than preprogramming could explain some prominent aspects of the spinal cord connectivity patterns observed in animals. To test this we developed an artificial organism with a basic musculoskeletal system and proprioceptive sensors, connected to a neural network. We adjusted the initially randomized gains in the neural network according to a Hebbian plasticity rule while exercising the model system with spontaneous muscle activity patterns similar to those observed during early fetal development. The resulting connection matrices support functional self-organization of the mature pattern of Ia to motoneuron connectivity in the spinal circuitry. More coordinated muscle activity patterns such as observed later during neonatal locomotion impaired projection selectivity. These findings imply a generic functionality of a musculoskeletal system to imprint important aspects of its mechanical dynamics onto a neural network, without specific preprogramming other than setting a critical period for the formation and maturation of this general pattern of connectivity. Such functionality would facilitate the successful evolution of new species with altered musculoskeletal anatomy and it may help to explain patterns of connectivity and associated reflexes that appear during abnormal development.

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bioRxiv2020

Neuronal leak within recurrent neuronal networks prevents the generation of high frequency spurious signals

Recurrent circuitry components are distributed widely within the brain, including both excitatory and inhibitory synaptic connections. Recurrent neuronal networks have potential stability problems, perhaps a predisposition to epilepsy. More generally, instability risks making internal representations of information unreliable. To assess the inherent stability properties of such recurrent networks, we tested a linear summation, non-spiking neuron model with and without a dynamic leak, corresponding to the low-pass filtering of synaptic input current by the RC circuit of the biological membrane. We first show that the output of this neuron model, in either of its two forms, follows its input at a higher fidelity than a wide range of spiking neuron models across a range of input frequencies. Then we constructed fully connected recurrent networks with equal numbers of excitatory and inhibitory neurons and randomly distributed weights across all synapses. When the networks were driven by pseudorandom sensory inputs with varying frequency, the recurrent network activity tended to induce high frequency self-amplifying components, sometimes evident as distinct transients, which were not present in the input data. The addition of a dynamic leak based on known membrane properties consistently removed such spurious high frequency noise across all networks. Furthermore, we found that the neuron model with dynamic leak imparts a network stability that seamlessly scales with the size of the network, conduction delays, the input density of the sensory signal and a wide range of synaptic weight distributions. Our findings suggest that neuronal dynamic leak serves the beneficial function of protecting recurrent neuronal circuitry from the self-induction of spurious high frequency signals, thereby permitting the brain to utilize this architectural circuitry component regardless of network size or recurrency. Author SummaryIt is known that neurons of the brain are extensively interconnected, which can result in many recurrent loops within its neuronal network. Such loops are prone to instability. Here we wanted to explore the potential noise and instability that could result in recurrently connected neuronal networks across a range of conditions. To facilitate such simulations, we developed a non-spiking neuron model that captures the main characteristics of conductance-based neuron models of Hodgkin-Huxley type, but is more computationally efficient. We found that a so-called dynamic leak, which is a natural consequence of the way the membrane of the neuron is constructed and how the neuron integrates synaptic inputs, provided protection against spurious, high frequency noise that tended to arise in our recurrent networks of varying size. We propose that this linear summation model provides a stable and useful tool for exploring the computational behavior of recurrent neural networks.

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bioRxiv2019

Structured internal cortical states deduced from fixed tactile input patterns

The brain has a never-ending internal activity, whose spatiotemporal evolution interacts with external inputs to define how we perceive them. We used reproducible touch-related spatiotemporal inputs and recorded intracellularly from rat neocortical neurons to characterise this interaction. The synaptic responses, or the summed input of the networks connected to the neuron, varied greatly to repeated presentations of the same tactile input pattern delivered to the tip of digit 2. Surprisingly, however, these responses sorted into a set of specific response types, unique for each neuron. Further, using a set of eight such tactile input patterns, we found each neuron to exhibit a set of specific response types for each input provided. Response types were not determined by global cortical state, but instead likely depended on the time-varying state of the specific subnetworks connected to each neuron. The fact that some types of responses were recurrent, i.e. more likely than others, indicates that the cortical network had a non-continuous landscape of solutions for these tactile inputs. Therefore, our data suggests that sensory inputs combine with the internal dynamics of the brain networks, thereby causing them to fall into one of multiple possible perceptual attractor states. The neuron-specific instantiations of response types we observed suggest that the subnetworks connected to each neuron represent different components of those attractor states. Our results indicate that the impact of cortical internal states on external inputs is substantially more richly resolvable than previously shown. Key points summaryO_LIIt is known that the internal state of the neocortical network profoundly impacts cortical neuronal responses to sensory input. C_LIO_LILittle is known of how the internal neocortical activity combines with a given sensory input to generate the response. C_LIO_LIWe used eight reproducible patterns of skin sensor activation and made intracellular recordings in neocortical neurons to explore the response variations in the specific subnetworks connected to each recorded neuron. C_LIO_LIWe found that each neuron exhibited multiple, specific recurring response types to the exact same skin stimulation pattern and that each given stimulation pattern evoked a unique set of response types. C_LIO_LIThe findings indicate a multi-structure internal state that combines with peripheral information to define cortical responses; we suggest this mechanism is a prerequisite for the formation of perception (and illusions) and indicates that the cortical networks work according to attractor dynamics. C_LI

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