Biorobotics/Biomechanics

Spinal cord

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Intuicell | Research

Understanding the underlying principles of how the brain works

Piece by piece, our research unveils groundbreaking insights into the fundamental mechanics of how the brain operates.

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Due to continuous state variations in neocortical circuits, individual somatosensory cortex (SI) neurons in vivo display a variety of intracellular responses to the exact same spatiotemporal tactile input pattern. 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, remarkably strong impact on the intracellular response types displayed by each neuron in the primary SI to each given tactile input pattern. Qualitatively, this impact was comparable to that previously described for cortical output, which was surprising given the widely assumed specific roles of the hippocampus, such as in cortical memory formation. The findings show that hippocampal output can profoundly impact the state-dependent interpretation of tactile inputs and hence influence perception, potentially with affective and semantic components.

Due to continuous state variations in neocortical circuits, individual somatosensory cortex (SI) neurons in vivo display a variety of intracellular responses to the exact same spatiotemporal tactile input pattern.

Hippocampal output profoundly impacts the interpretation of tactile input patterns in SI cortical neurons

Currently, it is accepted that animal locomotion is 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 central pattern generator 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 Bienenstock–Cooper–Munro learning rule, which has been previously proposed to explain learning in the visual cortex. These results provide an alternative theory to central pattern generator models, because rhythm generating neurons and genetically defined connectivity are not required in our model. Though our results are not in contradiction to current models, as existing neural mechanism and structures, not used in our model, can be expected to facilitate the kind of learning demonstrated here. Therefore, our model could be used to augment existing models.

Currently, it is accepted that animal locomotion is controlled by a central pattern generator in the spinal cord.

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

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.

The mammalian skin is a densely innervated soft tissue where embedded neural mechanoreceptors report to the brain the mechanical events arising at its surface.

The Import of Skin Tissue Dynamics in Tactile Sensing

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 

 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 

. We also exemplify SST’s generic signal processing capability by achieving outstanding resolution in the decomposition of complex acoustic data.

Time-frequency decomposition is a well-established method to unmix signals generated by multiple sources with unique characteristics.

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

The dynamics of the human body can be described by the accelerations and masses of the different body parts (e.g., legs, arm, trunk). These body parts can exhibit specific coordination patterns with each other. In human walking, we found that the swing leg cooperates with the upper body and the stance leg in different ways (e.g., in-phase and out-of-phase in vertical and horizontal directions, respectively). Such patterns of self-assistance found in human locomotion could be of advantage in robotics design, in the design of any assistive device for patients with movement impairments. It can also shed light on several unexplained infrastructural features of the CNS motor control. Self-assistance means that distributed parts of the body contribute to an overlay of functions that are required to solve the underlying motor task. To draw advantage of self-assisting effects, precise and balanced spatiotemporal patterns of muscle activation are necessary. We show that the necessary neural connectivity infrastructure to achieve such muscle control exists in abundance in the spinocerebellar circuitry. We discuss how these connectivity patterns of the spinal interneurons appear to be present already perinatally but also likely are learned. We also discuss the importance of these insights into whole body locomotion for the successful design of future assistive devices and the sense of control that they could ideally confer to the user.

The dynamics of the human body can be described by the accelerations and masses of the different body parts (e.g., legs, arm, trunk).

Whole Body Coordination for Self-Assistance in Locomotion

Control of musculoskeletal systems depends on integration of voluntary commands and somatosensory feedback in the complex neural circuits of the spinal cord. It has been suggested that the various connectivity patterns that have been identified experimentally may result from the many transcriptional types that have been observed in spinal interneurons. We ask instead whether the muscle-specific details of observed connectivity patterns can arise as a consequence of Hebbian adaptation during early development, rather than being genetically ordained. We constructed an anatomically simplified model musculoskeletal system with realistic muscles and sensors and connected it to a recurrent, random neuronal network consisting of both excitatory and inhibitory neurons endowed with Hebbian learning rules. We then generated a wide set of randomized muscle twitches typical of those described during fetal development and allowed the network to learn. Multiple simulations consistently resulted in diverse and stable patterns of activity and connectivity that included subsets of the interneurons that were similar to “archetypical” interneurons described in the literature. We also found that such learning led to an increased degree of cooperativity between interneurons when performing larger limb movements on which it had not been trained. Hebbian learning gives rise to rich sets of diverse interneurons whose connectivity reflects the mechanical properties of the system. At least some of the transcriptomic diversity may reflect the effects of this process rather than the cause of the connectivity. Such a learning process seems better suited to respond to the musculoskeletal mutations that underlie the evolution of new species.

Control of musculoskeletal systems depends on integration of voluntary commands and somatosensory feedback in the complex neural circuits of the spinal cord.

A model for self-organization of sensorimotor function: spinal interneuronal integration

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.

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. 

Visual input dynamically changes responses to spatiotemporal tactile input patterns in S1 neurons

The spinal cord is engaged in all forms of motor performance but its functions are far from understood. Because network connectivity defines function, we explored the connectivity of muscular, tendon, and tactile sensory inputs among a wide population of spinal interneurons in the lower cervical segments. Using low noise intracellular whole cell recordings in the decerebrated, non-anesthetized cat in vivo, we could define mono-, di-, and trisynaptic inputs as well as the weights of each input. Whereas each neuron had a highly specific input, and each indirect input could moreover be explained by inputs in other recorded neurons, we unexpectedly also found the input connectivity of the spinal interneuron population to form a continuum. Our data hence contrasts with the currently widespread notion of distinct classes of interneurons. We argue that this suggested diversified physiological connectivity, which likely requires a major component of circuitry learning, implies a more flexible functionality.

The spinal cord is engaged in all forms of motor performance but its functions are far from understood. Because network connectivity defines function, we explored the connectivity of muscular, tendon, and tactile sensory inputs among a wide population of spinal interneurons in the lower cervical segments.

Diversified physiological sensory input connectivity questions the existence of distinct classes of spinal interneurons

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 the selective formation of the monosynaptic projections between muscle spindle primary afferents and homonymous motoneurons. 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. Normal connectivity patterns developed only when we modeled β motoneurons, which are known to innervate both intrafusal and extrafusal muscle fibers in vertebrate muscles but were not considered in previous literature regarding selective formation of these synapses in animals with paralyzed muscles. It was also helpful to correctly model the greatly reduced contractility of extrafusal muscle fibers during early development. Stronger and 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.

Recent spinal cord literature abounds with descriptions of genetic preprogramming and the molecular control of circuit formation.

A model for self-organization of sensorimotor function: the spinal monosynaptic loop

Biological cortical networks are potentially fully recurrent networks without any distinct output layer, where recognition may instead rely on the distribution of activity across its neurons. Because such biological networks can have rich dynamics, they are well-designed to cope with dynamical interactions of the types that occur in nature, while traditional machine learning networks may struggle to make sense of such data. Here we connected a simple model neuronal network (based on the 'linear summation neuron model' featuring biologically realistic dynamics (LSM), consisting of 10 of excitatory and 10 inhibitory neurons, randomly connected) to a robot finger with multiple types of force sensors when interacting with materials of different levels of compliance. Scope: to explore the performance of the network on classification accuracy. Therefore, we compared the performance of the network output with principal component analysis of statistical features of the sensory data as well as its mechanical properties. Remarkably, even though the LSM was a very small and untrained network, and merely designed to provide rich internal network dynamics while the neuron model itself was highly simplified, we found that the LSM outperformed these other statistical approaches in terms of accuracy.

Biological cortical networks are potentially fully recurrent networks without any distinct output layer, where recognition may instead rely on the distribution of activity across its neurons.

Understanding the underlying principles of how the brain works

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