Biorobotics/Biomechanics

Spinal cord

Modelling

Neuron

Brain

Analytics

Intracellular

Cerebellum

For three decades our founders challenged the common consensus of one of the biggest scientific questions of our time: How does the brain work?

IntuiCell | Research

Born in the intersection
of AI and Neuroscience.

IntuiCell is a Lund University spin-off with presence in Lund and Stockholm. 

For three decades our founders challenged the common consensus 
of one of the biggest scientific questions of our time:

Today, we apply those groundbreaking neurophysiological insights 
to redesign artificial intelligence. Creating AI that is 

Explore our contrarian research on the mechanisms by which the 
brain predicts the world.

Research

CO Norrsken House
Birger Jarlsgatan 57C
SE-113 56 Stockholm

About

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IntuiCell

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 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

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

Locomotion control in mammals has been hypothesized to be governed by a central pattern generator (CPG) located in the circuitry of the spinal cord. The most common model of the CPG is the half center model, where two pools of neurons generate alternating, oscillatory activity. In this model, the pools reciprocally inhibit each other ensuring alternating activity. There is experimental support for reciprocal inhibition. However another crucial part of the half center model is a self inhibitory mechanism which prevents the neurons of each individual pool from infinite firing. Self-inhibition is hence necessary to obtain alternating activity. But critical parts of the experimental bases for the proposed mechanisms for self-inhibition were obtained 

, in preparations of juvenile animals. The commonly used adaptation of spike firing does not appear to be present in adult animals 

. We therefore modeled several possible self inhibitory mechanisms for locomotor control. Based on currently published data, previously proposed hypotheses of the self inhibitory mechanism, necessary to support the CPG hypothesis, seems to be put into question by functional evaluation tests or by 

 data. This opens for alternative explanations of how locomotion activity patterns in the adult mammal could be generated.

Locomotion control in mammals has been hypothesized to be governed by a central pattern generator (CPG) located in the circuitry of the spinal cord.

Biological data questions the support of the self inhibition required for pattern generation in the half center model

Despite decades of research, we lack a comprehensive framework to study and explain cognitive development. The emerging “paradigm” of action-based cognition implies that cognitive development is an active rather than a passive, automatic, and self-paced maturational process. Importantly, “active” refers to both sensorimotor activity (in the narrow sense) as well as to autonomous exploration (e.g., as found in active perception or active learning). How does this emphasis on action affect our understanding of cognitive development? Can an action-based approach provide a much-needed integrative theory of cognitive development? This chapter reviews key factors that influence development (including sensorimotor skills as well as genetic, social, and cultural factors) and their associated brain mechanisms. Discussion focuses on how these factors can be incorporated into a comprehensive action-based framework. Challenges are highlighted for future research (e.g., problems associated with explaining higher-level cognitive abilities and devising novel experimental methodologies). Although still in its infancy, an action-based approach to cognitive development holds promise to improve scientific understanding of cognitive development and to impact education and technology.

Despite decades of research, we lack a comprehensive framework to study and explain cognitive development. 

Acting Up: An Approach to the Study of Cognitive Development

The mechanism by which a learnt synaptic weight change can contribute to learning or adaptation of brain function is a type of credit assignment problem, which is a key issue for many parts of the brain. In the cerebellum, detailed knowledge not only of the local circuitry connectivity but also of the topography of different sources of afferent/external information makes this problem particularly tractable. In addition, multiple forms of synaptic plasticity and their general rules of induction have been identified. In this review, we will discuss the possible roles of synaptic and cellular plasticity at specific locations in contributing to behavioral changes. Focus will be on the parts of the cerebellum that are devoted to limb control, which constitute a large proportion of the cortex and where the knowledge of the external connectivity is particularly well known. From this perspective, a number of sites of synaptic plasticity appear to primarily have the function of balancing the overall level of activity in the cerebellar circuitry, whereas the locations at which synaptic plasticity leads to functional changes in terms of limb control are more limited. Specifically, the postsynaptic forms of long-term potentiation (LTP) and long-term depression (LTD) at the parallel fiber synapses made on interneurons and Purkinje cells, respectively, are the types of plasticity that mediate the widest associative capacity and the tightest link between the synaptic change and the external functions that are to be controlled.

The mechanism by which a learnt synaptic weight change can contribute to learning or adaptation of brain function is a type of credit assignment problem, which is a key issue for many parts of the brain. 

Cerebellar Synaptic Plasticity and the Credit Assignment Problem

We aimed to explore the cerebellar cortical inputs from two spinocerebellar pathways, the spinal border cell-component of the ventral spinocerebellar tract (SBC-VSCT) and the dorsal spinocerebellar tract (DSCT), respectively, in the sublobule C1 of the cerebellar posterior lobe. The two pathways were activated by electrical stimulation of the contralateral lateral funiculus (coLF) and the ipsilateral LF (iLF) at lower thoracic levels. Most granule cells in sublobule C1 did not respond at all but part of the granule cell population displayed high-intensity responses to either coLF or iLF stimulation. As a rule, Golgi cells and Purkinje cell simple spikes responded to input from both LFs, although Golgi cells could be more selective. In addition, a small population of granule cells responded to input from both the coLF and the iLF. However, in these cases, similarities in the temporal topography and magnitude of the responses suggested that the same axons were stimulated from the two LFs, i.e., that the axons of individual spinocerebellar neurons could be present in both funiculi. This was also confirmed for a population of spinal neurons located within known locations of SBC-VSCT neurons and dorsal horn (dh) DSCT neurons. We conclude that bilateral spinocerebellar responses can occur in cerebellar granule cells, but the VSCT and DSCT systems that provide the input can also be organized bilaterally. The implications for the traditional functional separation of VSCT and DSCT systems and the issue whether granule cells primarily integrate functionally similar information or not are discussed.

We aimed to explore the cerebellar cortical inputs from two spinocerebellar pathways, the spinal border cell-component of the ventral spinocerebellar tract (SBC-VSCT) and the dorsal spinocerebellar tract (DSCT), respectively, in the sublobule C1 of the cerebellar posterior lobe.

Properties of bilateral spinocerebellar activation of cerebellar cortical neurons

The spinocerebellar systems are essential for the brain in the performance of coordinated movements, but our knowledge about the spinocerebellar interactions is very limited. Recently, several crucial pieces of information have been acquired for the spinal border cell (SBC) component of the ventral spinocerebellar tract (VSCT), as well as the effects of SBC mossy fiber activation in granule cells of the cerebellar cortex. SBCs receive monosynaptic input from the reticulospinal tract (RST), which is an important driving system under locomotion, and disynaptic inhibition from Ib muscle afferents. The patterns of activity of RST neurons and Ib afferents under locomotion are known. The activity of VSCT neurons under fictive locomotion, i.e. without sensory feedback, is also known, but there is little information on how these neurons behave under actual locomotion and for cerebellar granule cells receiving SBC input this is completely unknown. But the available information makes it possible to simulate the interactions between the spinal and cerebellar neuronal circuitries with a relatively large set of biological constraints. Using a model of the various neuronal elements and the network they compose, we simulated the modulation of the SBCs and their target granule cells under locomotion and hence generated testable predictions of their general pattern of modulation under this condition. This particular system offers a unique opportunity to simulate these interactions with a limited number of assumptions, which helps making the model biologically plausible. Similar principles of information processing may be expected to apply to all spinocerebellar systems.

The spinocerebellar systems are essential for the brain in the performance of coordinated movements, but our knowledge about the spinocerebellar interactions is very limited.

Born in the intersection
of AI and Neuroscience.

IntuiCell is a Lund University spin-off with presence in Lund and Stockholm.

For three decades our founders challenged the common consensus
of one of the biggest scientific questions of our time:

How does the brain work?

Today, we apply those groundbreaking neurophysiological insights
to redesign artificial intelligence. Creating AI that is actually intelligent.

Explore our contrarian research on the mechanisms by which the
brain predicts the world.

All Publications.

Born in the intersectionof AI and Neuroscience.

IntuiCell is a Lund University spin-off with presence in Lund and Stockholm.

For three decades our founders challenged the common consensus of one of the biggest scientific questions of our time:

How does the brain work?

Today, we apply those groundbreaking neurophysiological insights to redesign artificial intelligence. Creating AI that is actually intelligent.

Explore our contrarian research on the mechanisms by which the brain predicts the world.

All Publications.

Born in the intersection
of AI and Neuroscience.

For three decades our founders challenged the common consensus
of one of the biggest scientific questions of our time:

Today, we apply those groundbreaking neurophysiological insights
to redesign artificial intelligence. Creating AI that is actually intelligent.

Explore our contrarian research on the mechanisms by which the
brain predicts the world.