Theory Of Synaptic Plasticity Research Articles

The Role of Genes and Proteins in Neurons Functioning

Purpose: The aim of the study is to examine the role of genes and proteins in neurons functioning.
 Methodology: This study adopted a desktop methodology. This study used secondary data from which include review of existing literature from already published studies and reports that was easily accessed through online journals and libraries.
 Findings: The study revealed that genes and proteins play pivotal roles in the functioning of neurons, contributing to the complex processes involved in neural development, communication, and plasticity. Genes provide the instructions for the production of proteins, which act as the building blocks and functional molecules within cells. The interplay between genes and proteins is essential for various neuronal processes, including synaptic transmission, neural circuit formation, and the modulation of neuronal activity.
 Unique Contribution to Theory, Practice and Policy: The study was anchored on the theory of synaptic plasticity which was proposed by Donald Hebb and the theory of neurotransmission which was pioneered by Otto Loewi. The study recommends that policies should be developed to promote genetic screening and testing for neurological disorders, allowing for early interventions and improved outcomes.

2D Heterostructure for High‐Order Spatiotemporal Information Processing

AbstractThe Bienenstock, Cooper, and Munro (BCM) theory of synaptic plasticity is regarded as the most precise model of the synapse, and is more compatible with neuromorphic computing. However, the development in BCM synaptic modification is rather limited since the memristive devices used to emulate the BCM lack tunable forgetting rate. Compared with memristors, memtransistors provide another gate‐tunable freedom degree, which will help to modulate the forgetting rate. In this work, the authors demonstrate a perfect BCM learning rule based on the 2D heterostructure memtransistor through using triplet‐spike timing dependent plasticity model. Two critical characteristics of the BCM rule, sliding frequency threshold and enhanced depression effect, are perfectly presented due to their spontaneous/gate‐assistant forgetting effect. The experimental results are extremely consistent with the BCM learning rule and suggest the potential application of 2D memtransistors in high‐order spatiotemporal recognition.

Energy Consumption and Entropy Production in a Stochastic Formulation of BCM Learning.

In a series of previous studies, we provided a stochastic description of a theory of synaptic plasticity. This theory, called BCM from the names of the three authors, has been formulated in two ways: the original formulation, where the plasticity threshold is defined as the square of the time-averaged neuronal activity, and a newer formulation, where the plasticity threshold is defined as the time average of the square of the neuronal activity. The newest formulation of the BCM rule of synaptic activity has interesting statistical properties, derived from a risk (or energy) function, the minimization of which leads to seeking of interesting projections in high-dimensional space. Moreover, these two rules, if implemented by a chemical master equation approach, show another interesting difference: the original rule satisfies the detailed balance, whereas the other not. Based on this different behavior, we found a continuous parameterization between these two rules. This parameterization shows a minimum that corresponds to maximum negative eigenvalues of the Jacobian matrix. In addition, the newest rule, due to the fact that it is in a nonequilibrium steady state (NESS), shows a higher level of plasticity than the original rule. This higher level of plasticity has to be interpreted in the framework of open thermodynamical systems and we show that entropy production and energy consumption in the newest rule are both less than in the original BCM rule.

Stable reinforcement learning via temporal competition between LTP and LTD traces

Neuronal systems that are involved in reinforcement learning must solve the temporal credit assignment problem, i.e., how is a stimulus associated with a reward that is delayed in time? Theoretical studies [1-3] have postulated that neural activity underlying learning ‘tags’ synapses with an ‘eligibility trace’, and that the subsequent arrival of a reward converts the eligibility traces into actual modification of synaptic efficacies. While eligibility traces provide one simple solution to the temporal credit assignment problem, they alone do not constitute a stable learning rule because there is no other mechanism indicating when learning should cease. In order to attain stability, rules involving eligibility traces often assume that once the association is learned, further learning is prevented via an inhibition of the reward stimulus [1,3,4]. Although synaptic plasticity is responsible for reinforcement learning in the brain, theories of reinforcement learning are generally abstract and involve neither neurons nor synapses. Furthermore, biophysical theories of synaptic plasticity typically model unsupervised learning and ignore the contribution of reinforcement. Here we describe a biophysically based theory of reinforcementmodulated synaptic plasticity and postulate the existence of two eligibility traces with different temporal profiles: one corresponding to the induction of LTP, and the other to the induction of LTD. The traces have different kinetics and their difference in magnitude at the time of reward determines if synaptic modification will correspond to LTP or LTD. Due to the difference in their decay rates, the LTP and LTD traces can exhibit temporal competition at the reward time and thus provides a mechanism for stable reinforcement learning without the need to inhibit reward. We test this novel reinforcement-learning rule on an experimentally motivated model of a recurrent cortical network [5], and compare the model results to experimental results at both the cellular and circuit levels. We further suggest that these eligibility traces are implemented via kinases and phosphatases, thus accounting for results at both the cellular and system levels.

A unifying theory of synaptic long-term plasticity based on a sparse distribution of synaptic strength

Long-term synaptic plasticity is fundamental to learning and network function. It has been studied under various induction protocols and depends on firing rates, membrane voltage, and precise timing of action potentials. These protocols show different facets of a common underlying mechanism but they are mostly modeled as distinct phenomena. Here, we show that all of these different dependencies can be explained from a single computational principle. The objective is a sparse distribution of excitatory synaptic strength, which may help to reduce metabolic costs associated with synaptic transmission. Based on this objective we derive a stochastic gradient ascent learning rule which is of differential-Hebbian type. It is formulated in biophysical quantities and can be related to current mechanistic theories of synaptic plasticity. The learning rule accounts for experimental findings from all major induction protocols and explains a classic phenomenon of metaplasticity. Furthermore, our model predicts the existence of metaplasticity for spike-timing-dependent plasticity Thus, we provide a theory of long-term synaptic plasticity that unifies different induction protocols and provides a connection between functional and mechanistic levels of description.

Treatment of neurolept-induced tardive dyskinesia

Tardive dyskinesia (TDK) includes orobuccolingual movements and “piano-playing” movements of the limbs. It is a movement disorder of delayed onset that can occur in the setting of neuroleptic treatment as well as in other diseases and following treatment with other drugs. The specific pathophysiology resulting in TDK is still not completely understood but possible mechanisms include postsynaptic dopamine receptor hypersensitivity, abnormalities of striatal gamma-aminobutyric acid (GABA) neurons, and degeneration of striatal cholinergic interneurons. More recently, the theory of synaptic plasticity has been proposed. Considering these proposed mechanisms of disease, therapeutic interventions have attempted to manipulate dopamine, GABA, acetylcholine, norepinephrine and serotonin pathways and receptors. The data for the effectiveness of each class of drugs and the side effects were considered in turn.

Modeling L-LTP based on changes in concentration of pCREB transcription factor

We simulate the induction and maintenance of late long-term potentiation (L-LTP) in the hippocampal dentate gyrus by means of a new synaptic plasticity rule that is the result of combination of the spike-timing-dependent plasticity (STDP) and the moving LTD/LTP threshold θ M from the Bienenstock, Cooper and Munro (BCM) theory of synaptic plasticity. We propose the activity-dependent functional equation for θ M to be based on two processes: (1) fast process that depends on average postsynaptic spike count and (2) slow process that is a function of concentration of phosphorylated CREB (cAMP-responsive element binding) transcription factor, activation of which induces gene expression to maintain L-LTP. In the end, we propose a new, more general form of synaptic plasticity rule, which is applicable to any form of activity-dependent synaptic plasticity.

Temporal correlation based learning in neuron models

We study a learning rule based upon the temporal correlation (weighted by a learning kernel) between incoming spikes and the internal state of the postsynaptic neuron, building upon previous studies of spike timing dependent synaptic plasticity (Kempter, R., Gerstner, W., van Hemmen, J.L., Wagner, H., 1998. Extracting Oscillations: Neuronal coincidence detection with noisy periodic spike input. Neural computation 10, 1987–2017; Kempter, R., Gerstner, W., van Hemmen, J.L., 1999. Hebbian learning and spiking neurons. Physical Reviewm E59, 4498–4514; van Hemmen, J.L., 2001. Theory of synaptic plasticity. In: Moss, F., Gielen, S. (Eds.), Handbook of biological physics. vol. 4, Neuro Informatics, neural modelling, Elsevier, Amsterdam, pp. 771–823. Our learning rule for the synaptic weight w ij is w ˙ ij ( t ) = ∈ ∫ - ∞ ∞ 1 T l ∫ t - T l t ∑ μ δ ( τ + s - t j , μ ) u ( τ ) d τ Γ ( s ) d s , where the t j,μ are the arrival times of spikes from the presynaptic neuron j and the function u( t) describes the state of the postsynaptic neuron i. Thus, the spike-triggered average contained in the inner integral is weighted by a kernel Γ( s), the learning window, positive for negative, negative for positive values of the time difference s between post- and presynaptic activity. An antisymmetry assumption for the learning window enables us to derive analytical expressions for a general class of neuron models and to study the changes in input–output relationships following from synaptic weight changes. This is a genuinely non-linear effect (Song, S., Miller, K., Abbott, L., 2000. Competitive Hebbian learning through spike-timing dependent synaptic plasticity. Nature Neuroscience 3, 919–926).

Formation of receptive fields in realistic visual environments according to the Bienenstock, Cooper, and Munro (BCM) theory.

The Bienenstock, Cooper, and Munro (BCM) theory of synaptic plasticity has successfully reproduced the development of orientation selectivity and ocular dominance in kitten visual cortex in normal, as well as deprived, visual environments. To better compare the consequences of this theory with experiment, previous abstractions of the visual environment are replaced in this work by real visual images with retinal processing. The visual environment is represented by 24 gray-scale natural images that are shifted across retinal fields. In this environment, the BCM neuron develops receptive fields similar to the fields of simple cells found in kitten striate cortex. These fields display adjacent excitatory and inhibitory bands when tested with spot stimuli, orientation selectivity when tested with bar stimuli, and spatial-frequency selectivity when tested with sinusoidal gratings. In addition, their development in various deprived visual environments agrees with experimental results.

Dynamic synaptic modification threshold: computational model of experience-dependent plasticity in adult rat barrel cortex.

Previous electrophysiological experiments have documented the response of neurons in the adult rat somatic sensory ("barrel") cortex to whisker movement after normal experience and after periods of experience with all but two whiskers trimmed close to the face (whisker "pairing"). To better understand how the barrel cortex adapts to changes in the flow of sensory activity, we have developed a computational model of a single representative barrel cell based on the Bienenstock, Cooper, and Munro (BCM) theory of synaptic plasticity. The hallmark of the BCM theory is the dynamic synaptic modification threshold, theta M, which dictates whether a neuron's activity at any given instant will lead to strengthening or weakening of the synapses impinging on it. The threshold theta M is proportional to the neuron's activity averaged over some recent past. Whisker pairing was simulated by setting input activities of the cell to the noise level, except for two inputs that represented untrimmed whiskers. Initially low levels of cell activity, resulting from whisker trimming, led to low values for theta M. As certain synaptic weights potentiated, due to the activity of the paired inputs, the values of theta M increased and after some time their mean reached an asymptotic value. This saturation of theta M led to the depression of some inputs that were originally potentiated. The changes in cell response generated by the model replicated those observed in in vivo experiments. Previously, the BCM theory has explained salient features of developmental experience-dependent plasticity in kitten visual cortex. Our results suggest that the idea of a dynamic synaptic modification threshold, theta M, is general enough to explain plasticity in different species, in different sensory systems, and at different stages of brain maturity.