Deans' stroke musings

Changing stroke rehab and research worldwide now.Time is Brain!Just think of all the trillions and trillions of neurons that DIE each day because there are NO effective 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:

Monday, August 8, 2016

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.
  • 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.


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.

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