Robotic Assistance for Training Finger Movement Using a Hebbian Model: A Randomized Controlled Trial

 I could use finger training but since this will never be written up in a stroke protocol I can't take these results to any therapist in the world and get the same results. I'm screwed like all stroke survivors with finger deficits.  Hope you know what Hebbian means.
http://journals.sagepub.com/doi/abs/10.1177/1545968317721975

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Justin B. Rowe, PhD, Vicky Chan, MS, Morgan L. Ingemanson, PhD, ...

First Published August 14, 2017 Research Article

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Abstract

Background. Robots that physically assist movement are increasingly used in rehabilitation therapy after stroke, yet some studies suggest robotic assistance discourages effort and reduces motor learning.  
Objective. To determine the therapeutic effects of high and low levels of robotic assistance during finger training.  
Methods. We designed a protocol that varied the amount of robotic assistance while controlling the number, amplitude, and exerted effort of training movements. Participants (n = 30) with a chronic stroke and moderate hemiparesis (average Box and Blocks Test 32 ± 18 and upper extremity Fugl-Meyer score 46 ± 12) actively moved their index and middle fingers to targets to play a musical game similar to GuitarHero 3 h/wk for 3 weeks. The participants were randomized to receive high assistance (causing 82% success at hitting targets) or low assistance (55% success). Participants performed ~8000 movements during 9 training sessions.  
Results. Both groups improved significantly at the 1-month follow-up on functional and impairment-based motor outcomes, on depression scores, and on self-efficacy of hand function, with no difference between groups in the primary endpoint (change in Box and Blocks). High assistance boosted motivation, as well as secondary motor outcomes (Fugl-Meyer and Lateral Pinch Strength)—particularly for individuals with more severe finger motor deficits. Individuals with impaired finger proprioception at baseline benefited less from the training.  
Conclusions. Robot-assisted training can promote key psychological outcomes known to modulate motor learning and retention. Furthermore, the therapeutic effectiveness of robotic assistance appears to derive at least in part from proprioceptive stimulation, consistent with a Hebbian plasticity model.

Hebbian Wiring Plasticity Generates Efficient Network Structures for Robust Inference with Synaptic Weight Plasticity

Ask your doctor and stroke association to clarify Hebbian synapses. Since plasticity is referred to, this is damned important for you to know how to construct and your doctor how to know when you are doing it right.  Don't let your doctor weasel out of answering.
http://journal.frontiersin.org/article/10.3389/fncir.2016.00041/full?

Naoki Hiratani^1,2* and Tomoki Fukai^2*

• ¹Department of Complexity Science and Engineering, The University of Tokyo, Kashiwa, Japan
• ²Laboratory for Neural Circuit Theory, RIKEN Brain Science Institute, Wako, Japan

In the adult mammalian cortex, a small fraction of spines are created and eliminated every day, and the resultant synaptic connection structure is highly nonrandom, even in local circuits. However, it remains unknown whether a particular synaptic connection structure is functionally advantageous in local circuits, and why creation and elimination of synaptic connections is necessary in addition to rich synaptic weight plasticity. To answer these questions, we studied an inference task model through theoretical and numerical analyses. We demonstrate that a robustly beneficial network structure naturally emerges by combining Hebbian-type synaptic weight plasticity and wiring plasticity. Especially in a sparsely connected network, wiring plasticity achieves reliable computation by enabling efficient information transmission. Furthermore, the proposed rule reproduces experimental observed correlation between spine dynamics and task performance.

Introduction

The amplitude of excitatory and inhibitory postsynaptic potentials (EPSPs and IPSPs), often referred to as synaptic weight, is considered a fundamental variable in neural computation (Bliss and Collingridge, 1993; Dayan and Abbott, 2005). In the mammalian cortex, excitatory synapses often show large variations in EPSP amplitudes (Song et al., 2005; Ikegaya et al., 2013; Buzsáki and Mizuseki, 2014), and the amplitude of a synapse can be stable over trials (Lefort et al., 2009) and time (Yasumatsu et al., 2008), enabling rich information capacity compared with that at binary synapses (Brunel et al., 2004; Hiratani et al., 2013). In addition, synaptic weight shows a wide variety of plasticity which depend primarily on the activity of presynaptic and postsynaptic neurons (Caporale and Dan, 2008; Feldman, 2009). Correspondingly, previous theoretical results suggest that under appropriate synaptic plasticity, a randomly connected network is computationally sufficient for various tasks (Maass et al., 2002; Ganguli and Sompolinsky, 2012).

On the other hand, it is also known that synaptic wiring plasticity and the resultant synaptic connection structure are crucial for computation in the brain (Chklovskii et al., 2004; Holtmaat and Svoboda, 2009). Elimination and creation of dendritic spines are active even in the brain of adult mammalians. In rodents, the spine turnover rate is up to 15% per day in sensory cortex (Holtmaat et al., 2005) and 5% per day in motor cortex (Zuo et al., 2005). Recent studies further revealed that spine dynamics are tightly correlated with the performance of motor-related tasks (Xu et al., 2009; Yang et al., 2009). Previous modeling studies suggest that wiring plasticity helps memory storage (Poirazi and Mel, 2001; Stepanyants et al., 2002; Knoblauch et al., 2010). However, in those studies, EPSP amplitude was often assumed to be a binary variable, and wiring plasticity was performed in a heuristic manner. Thus, it remains unknown what should be encoded by synaptic connection structure when synaptic weights have a rich capacity for representation, and how such a connection structure can be achieved through a local spine elimination and creation mechanism, which is arguably noisy and stochastic (Kasai et al., 2010).

To answer these questions, we constructed a theoretical model of an inference task. We first studied how sparse connectivity affects the performance of the network by analytic consideration and information theoretic evaluations. Then, we investigated how synaptic weights and connectivity should be organized to perform robust inference, especially under the presence of variability in the input structure. Based on these insights, we proposed a local unsupervised rule for wiring and synaptic weight plasticity. In addition, we demonstrated that connection structure and synaptic weight learn different components under a dynamic environment, enabling robust computation. Lastly, we investigated whether the model is consistent with various experimental results on spine dynamics.

More at link.

New Theory of Synapse Formation in the Brain

You will need to demand answers from your doctor how this is different from the current theories on how neuroplasticity works. Then get new stroke protocols. Be insistent, your doctor is supposed to help you recover, not just sit on the sideline. Your doctor needs to be able to predict how synapses form and what it will take to do that.  God, this is so blasted simple, does your doctor have any brains at all?
http://www.alphagalileo.org/ViewItem.aspx?ItemId=135224&CultureCode=en
The human brain keeps changing throughout a person’s lifetime. New connections are continually created while synapses that are no longer in use degenerate. To date, little is known about the mechanisms behind these processes. Jülich neuroinformatician Dr. Markus Butz has now been able to ascribe the formation of new neural networks in the visual cortex to a simple homeostatic rule that is also the basis of many other self-regulating processes in nature. With this explanation, he and his colleague Dr. Arjen van Ooyen from Amsterdam also provide a new theory on the plasticity of the brain – and a novel approach to understanding learning processes and treating brain injuries and diseases.
The brains of adult humans are by no means hard wired. Scientists have repeatedly established this fact over the last few years using different imaging techniques. This so-called neuroplasticity not only plays a key role in learning processes, it also enables the brain to recover from injuries and compensate for the loss of functions. Researchers only recently found out that even in the adult brain, not only do existing synapses adapt to new circumstances, but new connections are constantly formed and reorganized. However, it was not yet known how these natural rearrangement processes are controlled in the brain. In the open-access journal PLOS Computational Biology, Butz and van Ooyen now present a simple rule that explains how these new networks of neurons are formed (DOI: 10.1371/journal.pcbi.1003259).
“It’s very likely that the structural plasticity of the brain is the basis for long-term memory formation,” says Markus Butz, who has been working at the recently established Simulation Laboratory Neuroscience at the Jülich Supercomputing Centre for the past few months. “And it’s not just about learning. Following the amputation of extremities, brain injury, the onset of neurodegenerative diseases, and strokes, huge numbers of new synapses are formed in order to adapt the brain to the lasting changes in the patterns of incoming stimuli.”
Activity regulates synapse formation
These results show that the formation of new synapses is driven by the tendency of neurons to maintain a ‘pre-set’ electrical activity level. If the average electric activity falls below a certain threshold, the neurons begin to actively build new contact points. These are the basis for new synapses that deliver additional input – the neuron firing rate increases. This also works the other way round: as soon as the activity level exceeds an upper limit, the number of synaptic connections is reduced to prevent any overexcitation – the neuron firing rate falls. Similar forms of homeostasis frequently occur in nature, for example in the regulation of body temperature and blood sugar levels.
However, Markus Butz stresses that this does not work without a certain minimal excitation of the neurons: “A neuron that no longer receives any stimuli loses even more synapses and will die off after some time. We must take this restriction into account if we want the results of our simulations to agree with observations.” Using the visual cortex as an example, the neuroscientists have studied the principles according to which neurons form new connections and abandon existing synapses. In this region of the brain, about 10 % of the synapses are continuously regenerated. When the retina is damaged, this percentage increases even further. Using computer simulations, the authors succeeded in reconstructing the reorganization of the neurons in a way that conforms to experimental results from the visual cortex of mice and monkeys with damaged retinas.
The visual cortex is particularly suitable for demonstrating the new growth rule, because it has a property referred to as retinotopy: This means that points projected beside each other onto the retina are also arranged beside each other when they are projected onto the visual cortex, just like on a map. If areas of the retina are damaged, the cells onto which the associated images are projected receive different inputs. “In our simulations, you can see that areas which no longer receive any input from the retina start to build crosslinks, which allow them to receive more signals from their neighbouring cells,” says Markus Butz. These crosslinks are formed slowly from the edge of the damaged area towards the centre, in a process resembling the healing of a wound, until the original activity level is more or less restored.
Synaptic and structural plasticity
“The new growth rule provides structural plasticity with a principle that is almost as simple as that of synaptic plasticity,” says co-author Arjen van Ooyen, who has been working on models for the development of neural networks for decades. As early as 1949, psychology professor Donald Olding Hebb discovered that connections between neurons that are frequently activated will become stronger. Those that exchange little information will become weaker. Today, many scientists believe that this Hebbian principle plays a central role in learning and memory processes. While synaptic plasticity in involved primarily in short-term processes that take from a few milliseconds to several hours, structural plasticity extends over longer time scales, from several days to months.
Structural plasticity therefore plays a particularly important part during the (early) rehabilitation phase of patients affected by neurological diseases, which also lasts for weeks and months. The vision driving the project is that valuable ideas for the treatment of stroke patients could result from accurate predictions of synapse formation. If doctors knew how the brain structure of a patient will change and reorganize during treatment, they could determine the ideal times for phases of stimulation and rest, thus improving treatment efficiency.
New approach for numerous applications
“It was previously assumed that structural plasticity also follows the principle of Hebbian plasticity. The findings suggest that structural plasticity is governed by the homeostatic principle instead, which was not taken into consideration before,” says Prof. Abigail Morrison, head of the Simulation Laboratory Neuroscience at Jülich. Her team is already integrating the new rule into the freely accessible simulation software NEST, which is used by numerous scientists worldwide.
These findings are also of relevance for the Human Brain Project. Neuroscientists, medical scientists, computer scientists, physicists, and mathematicians in Europe are working hand in hand to simulate the entire human brain on high-performance computers of the next generation in order to better understand how it functions. “Due to the complex synaptic circuitry in the human brain, it’s not plausible that its fault tolerance and flexibility are achieved based on static connection rules. Models are therefore required for a self-organization process,” says Prof. Markus Diesmann from Jülich’s Institute of Neuroscience and Medicine, who is involved in the project. He heads Computational and Systems Neuroscience (INM-6), a subinstitute working at the interface between neuroscientific research and simulation technology.