Network Plasticity as Bayesian Inference

General results from statistical learning theory suggest to understand not only brain computations, but also brain plasticity as probabilistic inference. But a model for that has been missing. We propose that inherently stochastic features of synaptic plasticity and spine motility enable cortical networks of neurons to carry out probabilistic inference by sampling from a posterior distribution of network configurations. This model provides a viable alternative to existing models that propose convergence of parameters to maximum likelihood values. It explains how priors on weight distributions and connection probabilities can be merged optimally with learned experience, how cortical networks can generalize learned information so well to novel experiences, and how they can compensate continuously for unforeseen disturbances of the network. The resulting new theory of network plasticity explains from a functional perspective a number of experimental data on stochastic aspects of synaptic plasticity that previously appeared to be quite puzzling.Comment: 33 pages, 5 figures, the supplement is available on the author's web page http://www.igi.tugraz.at/kappe

Top-down inputs enhance orientation selectivity in neurons of the primary visual cortex during perceptual learning.

Perceptual learning has been used to probe the mechanisms of cortical plasticity in the adult brain. Feedback projections are ubiquitous in the cortex, but little is known about their role in cortical plasticity. Here we explore the hypothesis that learning visual orientation discrimination involves learning-dependent plasticity of top-down feedback inputs from higher cortical areas, serving a different function from plasticity due to changes in recurrent connections within a cortical area. In a Hodgkin-Huxley-based spiking neural network model of visual cortex, we show that modulation of feedback inputs to V1 from higher cortical areas results in shunting inhibition in V1 neurons, which changes the response properties of V1 neurons. The orientation selectivity of V1 neurons is enhanced without changing orientation preference, preserving the topographic organizations in V1. These results provide new insights to the mechanisms of plasticity in the adult brain, reconciling apparently inconsistent experiments and providing a new hypothesis for a functional role of the feedback connections

Plasticity and dystonia: a hypothesis shrouded in variability.

Studying plasticity mechanisms with Professor John Rothwell was a shared highlight of our careers. In this article, we discuss non-invasive brain stimulation techniques which aim to induce and quantify plasticity, the mechanisms and nature of their inherent variability and use such observations to review the idea that excessive and abnormal plasticity is a pathophysiological substrate of dystonia. We have tried to define the tone of our review by a couple of Professor John Rothwell's many inspiring characteristics; his endless curiosity to refine knowledge and disease models by scientific exploration and his wise yet humble readiness to revise scientific doctrines when the evidence is supportive. We conclude that high variability of response to non-invasive brain stimulation plasticity protocols significantly clouds the interpretation of historical findings in dystonia research. There is an opportunity to wipe the slate clean of assumptions and armed with an informative literature in health, re-evaluate whether excessive plasticity has a causal role in the pathophysiology of dystonia

Regulation of Neuromodulator Receptor Efficacy - Implications for Whole-Neuron and Synaptic Plasticity

Membrane receptors for neuromodulators (NM) are highly regulated in their distribution and efficacy - a phenomenon which influences the individual cell's response to central signals of NM release. Even though NM receptor regulation is implicated in the pharmacological action of many drugs, and is also known to be influenced by various environmental factors, its functional consequences and modes of action are not well understood. In this paper we summarize relevant experimental evidence on NM receptor regulation (specifically dopamine D1 and D2 receptors) in order to explore its significance for neural and synaptic plasticity. We identify the relevant components of NM receptor regulation (receptor phosphorylation, receptor trafficking and sensitization of second-messenger pathways) gained from studies on cultured cells. Key principles in the regulation and control of short-term plasticity (sensitization) are identified, and a model is presented which employs direct and indirect feedback regulation of receptor efficacy. We also discuss long-term plasticity which involves shifts in receptor sensitivity and loss of responsivity to NM signals. Finally, we discuss the implications of NM receptor regulation for models of brain plasticity and memorization. We emphasize that a realistic model of brain plasticity will have to go beyond Hebbian models of long-term potentiation and depression. Plasticity in the distribution and efficacy of NM receptors may provide another important source of functional plasticity with implications for learning and memory.Comment: 35 page

Alterations in brain connectivity due to plasticity and synaptic delay

Brain plasticity refers to brain's ability to change neuronal connections, as a result of environmental stimuli, new experiences, or damage. In this work, we study the effects of the synaptic delay on both the coupling strengths and synchronisation in a neuronal network with synaptic plasticity. We build a network of Hodgkin-Huxley neurons, where the plasticity is given by the Hebbian rules. We verify that without time delay the excitatory synapses became stronger from the high frequency to low frequency neurons and the inhibitory synapses increases in the opposite way, when the delay is increased the network presents a non-trivial topology. Regarding the synchronisation, only for small values of the synaptic delay this phenomenon is observed

A geographically distributed bio-hybrid neural network with memristive plasticity

Throughout evolution the brain has mastered the art of processing real-world inputs through networks of interlinked spiking neurons. Synapses have emerged as key elements that, owing to their plasticity, are merging neuron-to-neuron signalling with memory storage and computation. Electronics has made important steps in emulating neurons through neuromorphic circuits and synapses with nanoscale memristors, yet novel applications that interlink them in heterogeneous bio-inspired and bio-hybrid architectures are just beginning to materialise. The use of memristive technologies in brain-inspired architectures for computing or for sensing spiking activity of biological neurons8 are only recent examples, however interlinking brain and electronic neurons through plasticity-driven synaptic elements has remained so far in the realm of the imagination. Here, we demonstrate a bio-hybrid neural network (bNN) where memristors work as "synaptors" between rat neural circuits and VLSI neurons. The two fundamental synaptors, from artificial-to-biological (ABsyn) and from biological-to- artificial (BAsyn), are interconnected over the Internet. The bNN extends across Europe, collapsing spatial boundaries existing in natural brain networks and laying the foundations of a new geographically distributed and evolving architecture: the Internet of Neuro-electronics (IoN).Comment: 16 pages, 10 figure