Selectivity and Metaplasticity in a Unified Calcium-Dependent Model

A unified, biophysically motivated Calcium-Dependent Learning model has been shown to account for various rate-based and spike time-dependent paradigms for inducing synaptic plasticity. Here, we investigate the properties of this model for a multi-synapse neuron that receives inputs with different spike-train statistics. In addition, we present a physiological form of metaplasticity, an activity-driven regulation mechanism, that is essential for the robustness of the model. A neuron thus implemented develops stable and selective receptive fields, given various input statistic

A Neural Network Approach for Analyzing the Illusion of Movement in Static Images

The purpose of this work is to analyze the illusion of movement that appears when seeing certain static images. This analysis is accomplished by using a biologically plausible neural network that learned (in a unsupervised manner) to identify the movement direction of shifting training patterns. Some of the biological features that characterizes this neural network are: intrinsic plasticity to adapt firing probability, metaplasticity to regulate synaptic weights and firing adaptation of simulated pyramidal networks. After analyzing the results, we hypothesize that the illusion is due to cinematographic perception mechanisms in the brain due to which each visual frame is renewed approximately each 100 msec. Blurring of moving object in visual frames might be interpreted by the brain as movement, the same as if we present a static blurred object

Synaptic metaplasticity underlies tetanic potentiation in Lymnaea: a novel paradigm

We present a mathematical model which explains and interprets a novel form of short-term potentiation, which was found to be use-, but not time-dependent, in experiments done on Lymnaea neurons. The high degree of potentiation is explained using a model of synaptic metaplasticity, while the use-dependence (which is critically reliant on the presence of kinase in the experiment) is explained using a model of a stochastic and bistable biological switch.Comment: 12 pages, 7 figures, to appear in PLoS One (2013

Cortical M1 plasticity and metaplasticity in patients with multiple sclerosis

Background: Previous studies on patients with Multiple Sclerosis (MS) have reported contrasting findings on cortical plasticity of the primary motor cortex and no study has yet evaluated the regulatory mechanisms of cortical plasticity (i.e., metaplasticity) in MS patients. The aim of the present study was to investigate primary motor cortex (M1) plasticity and metaplasticity in patients with MS. Methods: Nineteen patients affected by Relapsing-–Remitting MS (RR-MS) and 16 age- and sex-matched healthy controls underwent intermittent Theta Burst Stimulation (iTBS) to evaluate cortical plasticity and iTBS preceded by repetitive index finger movements to evaluate M1 metaplasticity. Results: In healthy subjects MEP size significantly increased after iTBS whereas it significantly decreased when repetitive index finger movements preceded iTBS (metaplasticity) (factor PROTOCOL: p < 0.0001; PROTOCOL x TIME interaction: p = 0.001). Conversely, in MS patients MEP size mildly increased, albeit not significantly in both conditions (p > 0.05). In MS patients, percentage changes in MEP size induced by plasticity and metaplasticity protocol were significantly associated to EDSS (p = 0.001) and kinematics of index finger movements (p = 0.01). Conclusion: M1 plasticity and metaplasticity are both altered in MS patients. When TBS is used for therapeutic purposes, TBS protocols should be tailored according to the M1 plasticity functional reserve of each MS patient

Probabilistic versus incremental presynaptic learning in biological plausible synapses

In this paper, the presynaptic rule, a classical rule for hebbian learning, is revisited. It is shown that the presynaptic rule exhibits relevant synaptic properties like synaptic directionality, and LTP metaplasticity (long-term potentiation threshold metaplasticity). With slight modifications, the presynaptic model also exhibits metaplasticity of the long-term depression threshold, being also consistent with Artola, Brocher and Singer’s (ABS) influential model. Two asymptotically equivalent versions of the presynaptic rule were adopted for this analysis: the first one uses an incremental equation while the second, conditional probabilities. Despite their simplicity, both types of presynaptic rules exhibit sophisticated biological properties, specially the probabilistic versio

Optimal storage and recall with biologically plausible synapses

Synaptic plasticity is widely accepted to underlie learning and memory. Yet, models of associative networks with biologically plausible synapses fail to match brain performance: memories stored in such networks are quickly overwritten by ongoing plasticity (Amit & Fusi 1996, Fusi et al 2007). Metaplasticity - the process by which neural activity changes the ability of synapses to exhibit further plasticity - is believed to increase memory capacity (Fusi et al 2005). However, it remains unclear if neurons can make use of this additional information during recall. In particular, previous attempts at reading out information in metaplastic synapses using heuristic recall dynamics led to rather poor performance (Huang & Amit 2010).

Here, we developed a theoretical framework for storage and recall with finite-state synapses that allowed us to find neural and synaptic dynamics that maximize the efficiency of autoassociative recall. 
Since information storage by synaptic plasticity is lossy, we formulated the problem of recalling a previously stored pattern from a noisy cue as probabilistic inference (Lengyel et al 2005) and derived neural dynamics efficiently implementing such inferences. Our approach is general and can be applied to any synaptic plasticity model which involves stochastic transitions between a finite set of states.
We show how synaptic plasticity rules need to be matched to the statistics of stored patterns, and how recall dynamics need to be matched both to input statistics and to the plasticity rule itself in order to achieve optimal performance. In particular, for binary synapses with metastates we demonstrate for the first time that memories can be efficiently read out with biologically plausible network dynamics that we derive directly from the synaptic metaplasticity rule with virtually no free parameters

Metaplasticity A Promising Tool to Disentangle Chronic Disorders of Consciousness Differential Diagnosis

The extent of cortical reorganization after brain injury in patients with Vegetative State/Unresponsive Wakefulness Syndrome (UWS) and Minimally Conscious State (MCS) depends on the residual capability of modulating synaptic plasticity. Neuroplasticity is largely abnormal in patients with UWS, although the fragments of cortical activity may exist, while patients MCS show a better cortical organization. The aim of this study was to evaluate cortical excitability in patients with disorders of consciousness (DoC) using a transcranial direct current stimulation (TDCS) metaplasticity protocol. To this end, we tested motor-evoked potential (MEP) amplitude, short intracortical inhibition (SICI), and intracortical facilitation (ICF). These measures were correlated with the level of consciousness (by the Coma Recovery Scale-Revised, CRS-R). MEP amplitude, SICI, and ICF strength were significantly modulated following different metaplasticity TDCS protocols only in the patients with MCS. SICI modulations showed a significant correlation with the CRS-R score. Our findings demonstrate, for the first time, a partial preservation of metaplasticity properties in some patients with DoC, which correlates with the level of awareness. Thus, metaplasticity assessment may help the clinician in differentiating the patients with DoC, besides the clinical evaluation. Moreover, the responsiveness to metaplasticity protocols may identify the subjects who could benefit from neuromodulation protocols

Accumulation of Dense Core Vesicles in Hippocampal Synapses Following Chronic Inactivity.

The morphology and function of neuronal synapses are regulated by neural activity, as manifested in activity-dependent synapse maturation and various forms of synaptic plasticity. Here we employed cryo-electron tomography (cryo-ET) to visualize synaptic ultrastructure in cultured hippocampal neurons and investigated changes in subcellular features in response to chronic inactivity, a paradigm often used for the induction of homeostatic synaptic plasticity. We observed a more than 2-fold increase in the mean number of dense core vesicles (DCVs) in the presynaptic compartment of excitatory synapses and an almost 20-fold increase in the number of DCVs in the presynaptic compartment of inhibitory synapses after 2 days treatment with the voltage-gated sodium channel blocker tetrodotoxin (TTX). Short-term treatment with TTX and the N-methyl-D-aspartate receptor (NMDAR) antagonist amino-5-phosphonovaleric acid (AP5) caused a 3-fold increase in the number of DCVs within 100 nm of the active zone area in excitatory synapses but had no significant effects on the overall number of DCVs. In contrast, there were very few DCVs in the postsynaptic compartments of both synapse types under all conditions. These results are consistent with a role for presynaptic DCVs in activity-dependent synapse maturation. We speculate that these accumulated DCVs can be released upon reactivation and may contribute to homeostatic metaplasticity