Neurodynamics

The objective of the study is to present a historical outline of neuromobilisation and clinical neurodynamics -in Poland and worldwide. Following the development of non-invasive methods of treatment, the knowledge of nervous system mechanics has become more and more extensive. The first record of performing a neurodynamic test goes back to 2800 BC. Many years later, in the first and second half of 20th century, Bragard, Von Lanz and Wachsmuth described three main neurodynamic tests for an upper limb. The biggest worldwide input in neuromobilisation problems development was made by the following scientists: Geoffrey Maitland, Robert Elvey, David Butler, Michael Shacklock and Alf Breig. David Butler and Michael Shacklock definitely represent the key researchers since it is them who, during their cooperation in the period of 1990-1997, made a great input in the origins of neuromobilisation treatment methods. The concept of nervous system mobilisation in Poland was initiated in April 1998 in Zgorzelec, where the first course run by David Butler himself, was held. In the same year Zbigniew Śliwiński published his first two studies in the journal entitled "Manual Medicine". At present trainings, courses and numerous research works, focused on nervous system mechanics, are conducted in Poland. The most recent and worth noting publications are these referring to neuromobilisation effectiveness and performed by the team of Dwornik, Bialoszewski and colleagues.

Neural mobilization is the most important technique used for the treatment of nervous system dysfunction. This study aimed to systematically review and evaluate the therapeutic efficacy of neural mobilization techniques in nervous system dysfunctions by assessing Randomized Controlled Clinical Trials (RCTs). Materials and Methods: We used all English papers published in five electronic databases from 2000 to 2020 using the following keywords: "neural mobilization", "nerve mobilization", "physical therapy", "nerve glide exercises", "neural stretching", "neurodynamics", and "neural physiotherapy". The full text of the articles identified was reviewed to select papers specifically discussing neural mobilization as a treatment modality. The PEDro scale was used to assess the quality of these trials. The randomized clinical trials were selected that examined the therapeutic effect of neural mobilization. Results: Twelve RCTs were identified. Five RCTs used the same median nerve tensioning technique in patients with Carpal Tunnel Syndrome (CTS). In some studies, the methods of neural mobilization were different. Fourteen papers examined different neurodynamic dysfunctions such as lateral epicondylalgia, radicular neck pain, postoperative spinal surgery, radicular low back pain, and chronic tension-type headache. There is moderate evidence (Level 2) to support distal nerve tensioning and tendon gliding techniques in CTS patients. Also, there was limited (Level 3) and insufficient (Level 4) evidence about using cervical lateral gliding away from their involved side and upper limb tension test mobilization and the use of slump stretches and combinations techniques in the treatment of neurodynamic dysfunction, respectively. Besides, all studies reported a positive effect compared to neutral effects. Conclusion: Although clinicians frequently use neuromobilization techniques for both diagnosis and treatment of nervous system dysfunctions, the quality assessment of 20 RCTs has shown insufficient evidence to support the efficacy of these techniques in the treatment of nervous system dysfunctions.

This is the readable introductory version of Global Workspace Theory, published in 1997 in the Journal of Consciousness Studies. The Oxford University Press book of that year provides an easy book-length introduction. Since 1997 our emphasis has been on incorporating more and more brain evidence. Please see our recent articles on Academia.edu

Streszczenie
Celem pracy jest przedstawienie neurodynamiki w praktyce fizjoterapeutycznej, uwzględniając dysfunkcje kończyny górnej. Badanie kliniczne jest podstawą do rozpoznania przyczyn zaburzeń neurodynamiki. Specyficzny charakter tych zaburzeń wynika z budowy układu nerwowego, dlatego znajomość sekwencji ruchowych jest w tym przypadku bezcenna.
Testy neurodynamiczne umożliwiają uzupełnienie terapii, zwiększając szanse na jej końcowy sukces. Mobilizacje układu nerwowego są skuteczną częścią terapii w dysfunkcjach kończyny górnej: entezopatiach- łokciu tenisisty i golfisty, zespole De Quervaina, zespole cieśni nadgarstka, oraz zespole kanału łokciowego.

Słowa kluczowe: neurodynamika, testy napięciowe, testy ruchomości, ból neurogenny, dysfunkcje kończyny górnej.

Summary
The goal of this article is to present neurodynamics in physiotherapy practice, consider upper limb dysfunctions. Clinical examination is essential in proper diagnosis of the neurodynamics dysfunctions. The specific character of this dysfunctions is consequence of nervous system anatomy, that’s why orders of movement knowledge is priceless in this case.
A neurodynamics tests allows therapy supplementation and increasing the chances of final success. Mobilizations of the nervous system are effectives part of therapy in upper limb dysfunctions: entesopathys- tennis and golfer’s elbow, De Quervain syndrome, carpal tunnel syndrome, Cubital Tunnel Syndrome.

Key words: neurodynamics, tension tests, movement tests, neurogenic pain, upper limb dysfunctions.

In this article, we investigate the foundations for a Gibsonian neurosci-
ence. There is an increasingly influential current in neuroscience based
on pragmatic and selectionist principles, which we think can contrib-
ute to ecological psychology. Starting from ecological psychology, we
identify three basic constraints any Gibsonian neuroscience needs to
adhere to: nonreconstructive perception, vicarious functioning, and
selectionist self-organization. We discuss two previous attempts to
integrate affordances with neuroscience: Reed’s ecological rendering of Edelman’s selectionism as well as Dreyfus’ phenomenological inter- pretation of Freeman’s neurodynamics. Reed and Dreyfus face the problem of how to account for “value.”
We then show how the free-energy principle, an increasingly dominant framework in theoretical neuroscience, is rooted  in both Freeman’s  neurodynamics and Edelman’s selectionism. The free-energy principle accounts for value in terms of selective anticipation. The selection pressures at work on the agent shape its selective sensitivity to the relevant affordances in the environment. By being responsive to the relevant affordances in the environment, an agent comes to have grip on its interactions
with the environment and can thrive in its ecological niche.

The Artificial Life approach to Evolutionary Robotics is used
as a fundamental framework for the development of a modular neural
control of autonomous mobile robots. The applied evolutionary technique
is especially designed to grow different neural structures with complex
dynamical properties. This is due to a modular neurodynamics approach
to cognitive systems, stating that cognitive processes are the result of
interacting dynamical neuro-modules. The evolutionary algorithm is de-
scribed, and a few examples for the the versatility of the procedures
are given. Besides solutions for standard tasks like exploration, obsta-
cle avoidance, and tropism, also the sequential evolution of morphology
and control of a biped is demonstrated. A further example describes the
co-evolution of different neuro-controllers cooperating to keep a gravita-
tionally driven art-robot in constant rotation.

This commentary to Fultot, Nie & Carello (2016) highlights some controversial aspects of enactivism and ecological psychology, specifically the notions of subjectivity and ecological information. I argue that, instead of choosing between them, both theories could complement each other at different levels of analysis in a single research framework for explaining cognition from a situated perspective.

Synchronization of neural signals has been proposed as a temporal cod-
ing scheme representing cooperated computation in distributed cortical
networks. Previous theoretical studies in that direction mainly focused on
the synchronization of coupled oscillatory subsystems and neglected more
complex dynamical modes, that already exist on the single-unit level. In
the present work we study the parameterized time-discrete dynamics of
two coupled recurrent networks of graded neurons. Conditions for the ex-
istence of partially synchronized dynamics of these systems are derived,
referring to a situation where only subsets of neurons in each sub-network
are synchronous. The coupled networks can have different architectures
and even a different number of neurons. Periodic as well as quasiperi-
odic and chaotic attractors constrained to a manifold M of synchronized
components are observed. Examples are discussed for coupled 3-neuron
networks having different architectures, and for coupled 2-neuron and 3-
neuron networks. Partial synchronization of different degrees is demon-
strated by numerical results for selected sets of parameters. In conclusion,
the results show that synchronization phenomena far beyond completely
synchronized oscillations can occur even in simple coupled networks. The
type of the synchronization depends in an intricate way on stimuli, history
and connectivity as well as other parameters of the network. Specific in-
puts can further switch between different operational modes in a complex
way, suggesting a similarly rich spatio-temporal behavior in real neural
systems.

Evolutionary techniques are applied to develop the neural
control of humanoid robots which were designed to act as agents in
language games in the context of the EU-project ALEAR. The basic
ingredients needed to bring forth the desired behaviours are described:
an appropriate physical simulator of the robots, an interactive evolution
environment and various analysis tools. A modular approach to neural
control is taken and supported by the corresponding evolutionary algo-
rithm, where complete neural control networks are composed of specific
functional units, the so called neuro-modules. Examples of such modules
are described and their use is demonstrated by means of two complete
developed networks for a walking and a gesture behaviour.

Conspiracy theories, or in general seriously distorted beliefs, are widespread. Large number of books and articles has been written on this subject from psychological, sociological and political science perspectives. How and why conspiracy theories transpire in the brain is still more a matter of speculation rather than of hard science. In this paper one plausible mechanisms is investigated: rapid freezing of high neu-roplasticity (RFHN). Emotional arousal temporarily increases neuroplasticity leading to creation of new associations, pathways that spread neural activation. Using the language of neurodynamics a meme is defined as quasi-stable associative memory attractor state. Depending on the temporal characteristics of the incoming information and the plasticity of the network, memory may self-organize, creating memes with large attractor basins, drawing in many accidental patterns representing concepts and memories. Creation of such memes with numerous fake associations distorts relations between stable memory states, redefining world-view. Simulations of neural network models trained with competitive Hebbian learning (CHL) on stationary and non-stationary input data show the formation of distorted memory states. Short learning with high plasticity followed by rapid decrease of plasticity leads to memes with large attraction basins, distorting input pattern representations in associative memory. Such system-level models may be used to understand formation of strong attractor states of the neurodynamics, representing distorted deeply settled beliefs.

Using neuronal plasticity in the sensorimotor loop
of embodied controllers to autonomously learn behaviors re-
mains a great challenge. The difficulty lies not only in the
development of sophisticated plasticity mechanisms, but also in
controlling when, where and how to learn, in order to achieve
the correct behavior. Borrowing from biology, we develop
a general framework that deploys intrinsic biofeedback-like
signals to assess the behavior through self-monitoring and to
control learning accordingly. The framework augments a plastic
control network (CSN) with a static neuromodulator (NM)
subnetwork (MSN), containing cells capable of producing NM
as feedback signal in response to observed network activations.
A desired behavior can then be defined solely by specifying
the conditions for NM release. As a response to NM exposure,
the CSN changes until a new stable configuration without NM
release is reached, i.e. the desired behavior is performed. We
demonstrate this approach with experiments in which simple
behaviors are learned from scratch. We show how the behaviors
can be defined with MSNs and that – in combination with a
simple stochastic plasticity method – behaviors are successfully
learned.

Artykuł, będący rozdziałem w książce Problemy Biocybernetyki i Inżynierii Biomedycznej, Tom 5 – Biomechanika, opisuje metodykę komputerowego modelowania fragmentów systemu nerwowego zaangażowanych w procesie sterowania ruchem. Do modelowania użyty został system informatyczny opracowany w Zakładzie Biocybernetyki AGH, który przeznaczony był do modelowania tak zwanych "sieci neuropodobnych". System zaimplementowano na komputerze CDC Cyber 72, a potem przeniesiono na mikrokomputery klasy IBM PC. W artykule przedstawiono działanie systemu i wykresy dynamicznego działania neuronów, w szczególności neuronów sterujących mięśnie zginacze i prostowniki tych samych stawów heterolateralnych kończyn czworonoga.

Activity cascades are found in many complex systems. In the cortex, they arise in the form of neuronal avalanches that capture ongoing and evoked neuronal activities at many spatial and temporal scales. The scale-invariant nature of avalanches suggests that the brain is in a critical state, yet predictions from critical theory on the temporal unfolding of avalanches have yet to be confirmed in vivo. Here we show in awake nonhuman primates that the temporal profile of avalanches follows a symmetrical, inverted parabola spanning up to hundreds of milliseconds. This parabola constrains how avalanches initiate locally, extend spatially and shrink as they evolve in time. Importantly, parabolas of different durations can be collapsed with a scaling exponent close to 2 supporting critical generational models of neuronal avalanches. Spontaneously emerging, transient γ–oscillations coexist with and modulate these avalanche parabolas thereby providing a temporal segmentation to inherently scale-invariant, critical dynamics. Our results identify avalanches and oscillations as dual principles in the temporal organization of brain activity.

The parametrized time-discrete dynamics of two recurrently coupled
chaotic neurons is investigated. Basic dynamical features of this system
are demonstrated for symmetric couplings of identical neurons. Periodic
as well as chaotic orbits constrained to a manifold M of synchronized
states are observed. Parameter domains for locally stable synchroniza-
tion manifolds M are determined by numerical simulations. In addition
to the synchronized dynamics there often co-exist periodic, quasiperiodic
and even chaotic attractors representing different kinds of non-synchronous
coherent dynamics. Simulation results for selected sets of parameters are
presented, and synchronization conditions for systems with non-identical
neurons are derived. Also these more general systems inherit the above
mentioned dynamical properties.

The discrete-time dynamics of small neural networks is studied empirically,
with emphasis laid on non-trivial bifurcation scenarios. For particular two- and
three-neuron networks interesting dynamical properties like periodic, quasi-
periodic and chaotic attractors are observed, many of them co-existing for one
and the same set of parameters. An appropriate equivalence class of networks
is defined, describing them as parametrized dynamical systems with identical
dynamical capacities. Combined symmetries in phase space and parameter
space are shown to generate different representatives of such a class. Moreover,
conditions on the connectivity structure are suggested, which guarantee the
existence of complex dynamics for a considered equivalence class of network
configurations.

Background: Sciatica Bothersomeness Index and Sciatica Frequency Index are used as the outcome measures for evaluation of bothersomeness and frequency of radiating symptoms in patients with low back pain with radiculopathy. So far, validated Gujarati versions of SBI and SFI have not been reported. Aim: To validate the Gujarati versions of SBI and SFI. Study Design: Cross-sectional study Materials and Methods: SBI and SFI were translated into Gujarati language using forward and back translation method. Face and content validity were examined by group consensus method by an expert committee which consisted of the authors, orthopaedic surgeon, general practitioner and physiotherapists experienced in the field of musculoskeletal rehabilitation. Concurrent validity was examined by correlating SBI and SFI with Maine Seattle Back questionnaire and subscales of SF-36 on 56 patients with lumbosacral radiculopathy aged 18 to 60 years. Test retest reliability was examined by collecting data on initial visit and after 48 hours. Results: The items were accepted with >80% consensus. Concurrent validity measured by Spearman's correlation coefficient indicated that SBI and SFI had strong to moderate correlation with MSBQ, VAS for leg pain and most subscales of SF 36 and weak correlation with vitality subscale and VAS for back pain. SBI and SFI had high internal consistency measured by Cronbach's alpha (alpha=0.94 and 0.89). Test retest reliability was measured by intraclass correlation coefficient which revealed high correlation (ICC=0.84 and 0.78). Conclusion: Gujarati versions of Sciatica bothersomeness index and sciatica frequency Index are reliable and valid measures and can be used for clinical and research purposes.

Building artificial brain-like systems is to a large extend hampered
by the difficulty to understand details of a structure-function-relationship
in biological or artificial neural networks. Based on a neurodynamics
approach to embodied cognition this paper introduces a method to
guide the development of modular neural systems in the direction of
enhanced cognitive abilities.
The method uses formally the synchronization of subnetworks to
split the dynamics of coupled systems into synchronized and asyn-
chronous components. Conditions for coupling structures allowing for
a richer dynamical spectrum of the composed system are then derived.
These conditions concern the asymptotic stability and the destabiliza-
tion of synchronization manifolds.
The concept of a synchronization core is introduced to represent
a whole family of parametrized neurodynamical systems living in a
synchronization manifold. It is used to identify those coupled sys-
tems having a rich spectrum of dynamical properties. Special coupling
structures - called generative - are identified which allow to make the
synchronized dynamics more “complex” than the dynamics of the iso-
lated parts. Furthermore, a criterion for coupling structures is given
which, in addition to the synchronized dynamics, allows also for asyn-
chronous dynamics by destabilizing the synchronization manifold. The
large class of synchronization equivalent systems contains networks
with very different coupling structures and weights all sharing the same
dynamical properties. To demonstrate the method an example is dis-
cussed in detail.

During the last two decades, analysis of 1/ƒ noise in cognitive science has led to a consider-
able progress in the way we understand the organization of our mental life. However, there
is still a lack of specific models providing explanations of how 1/ƒ noise is generated in cou-
pled brain-body-environment systems, since existing models and experiments typically tar-
get either externally observable behaviour or isolated neuronal systems but do not address
the interplay between neuronal mechanisms and sensorimotor dynamics. We present a
conceptual model of a minimal neurorobotic agent solving a behavioural task that makes it
possible to relate mechanistic (neurodynamic) and behavioural levels of description. The
model consists of a simulated robot controlled by a network of Kuramoto oscillators with ho-
meostatic plasticity and the ability to develop behavioural preferences mediated by sensori-
motor patterns. With only three oscillators, this simple model displays self-organized
criticality in the form of robust 1/ƒ noise and a wide multifractal spectrum. We show that the
emergence of self-organized criticality and 1/ƒ noise in our model is the result of three simul-
taneous conditions: a) non-linear interaction dynamics capable of generating stable collec-
tive patterns, b) internal plastic mechanisms modulating the sensorimotor flows, and c)
strong sensorimotor coupling with the environment that induces transient metastable neuro-
dynamic regimes. We carry out a number of experiments to show that both synaptic plastici-
ty and strong sensorimotor coupling play a necessary role, as constituents of self-organized
criticality, in the generation of 1/ƒ noise. The experiments also shown to be useful to test the
robustness of 1/ƒ scaling comparing the results of different techniques. We finally discuss
the role of conceptual models as mediators between nomothetic and mechanistic models
and how they can inform future experimental research where self-organized critically in-
cludes sensorimotor coupling among the essential interaction-dominant process giving rise
to 1/ƒ noise.