Use the labels in the right column to find what you want. Or you can go thru them one by one, there are only 13380 posts. Searching is done in the search box in upper left corner. I blog on anything to do with stroke.DO NOT DO ANYTHING SUGGESTED HERE AS I AM NOT MEDICALLY TRAINED, YOUR DOCTOR IS, LISTEN TO THEM. BUT I BET THEY DON'T KNOW HOW TO GET YOU 100% RECOVERED. I DON'T EITHER, BUT HAVE PLENTY OF QUESTIONS FOR YOUR DOCTOR TO ANSWER.
Deans' stroke musings
Changing stroke rehab and research worldwide now.Time is Brain!Just think of all thetrillions and trillions of neuronsthateach daybecause there areeffective hyperacute therapies besides tPA(only 12% effective). I have 493 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.
What this blog is for:
Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It's quite disgusting that this information is not available from every stroke association and doctors group. My back ground story is here:http://oc1dean.blogspot.com/2010/11/my-background-story_8.html
Monday, August 8, 2016
Hebbian Wiring Plasticity Generates Efficient Network Structures for Robust Inference with Synaptic Weight Plasticity
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
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.