Distribution Of Synaptic Strengths Research Articles

Task-Related Synaptic Changes Localized to Small Neuronal Population in Recurrent Neural Network Cortical Models.

Humans have flexible control over cognitive functions depending on the context. Several studies suggest that the prefrontal cortex (PFC) controls this cognitive flexibility, but the detailed underlying mechanisms remain unclear. Recent developments in machine learning techniques allow simple PFC models written as a recurrent neural network to perform various behavioral tasks like humans and animals. Computational modeling allows the estimation of neuronal parameters that are crucial for performing the tasks, which cannot be observed by biologic experiments. To identify salient neural-network features for flexible cognition tasks, we compared four PFC models using a context-dependent integration task. After training the neural networks with the task, we observed highly plastic synapses localized to a small neuronal population in all models. In three of the models, the neuronal units containing these highly plastic synapses contributed most to the performance. No common tendencies were observed in the distribution of synaptic strengths among the four models. These results suggest that task-dependent plastic synaptic changes are more important for accomplishing flexible cognitive tasks than the structures of the constructed synaptic networks.

Modeling maintenance of long-term potentiation in clustered synapses: long-term memory without bistability.

Memories are stored, at least partly, as patterns of strong synapses. Given molecular turnover, how can synapses maintain strong for the years that memories can persist? Some models postulate that biochemical bistability maintains strong synapses. However, bistability should give a bimodal distribution of synaptic strength or weight, whereas current data show unimodal distributions for weights and for a correlated variable, dendritic spine volume. Thus it is important for models to simulate both unimodal distributions and long-term memory persistence. Here a model is developed that connects ongoing, competing processes of synaptic growth and weakening to stochastic processes of receptor insertion and removal in dendritic spines. The model simulates long-term (>1 yr) persistence of groups of strong synapses. A unimodal weight distribution results. For stability of this distribution it proved essential to incorporate resource competition between synapses organized into small clusters. With competition, these clusters are stable for years. These simulations concur with recent data to support the “clustered plasticity hypothesis” which suggests clusters, rather than single synaptic contacts, may be a fundamental unit for storage of long-term memory. The model makes empirical predictions and may provide a framework to investigate mechanisms maintaining the balance between synaptic plasticity and stability of memory.

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.

Effects of synaptic connectivity on liquid state machine performance

The Liquid State Machine (LSM) is a biologically plausible computational neural network model for real-time computing on time-varying inputs, whose structure and function were inspired by the properties of neocortical columns in the central nervous system of mammals. The LSM uses spiking neurons connected by dynamic synapses to project inputs into a high dimensional feature space, allowing classification of inputs by linear separation, similar to the approach used in support vector machines (SVMs). The performance of a LSM neural network model on pattern recognition tasks mainly depends on its parameter settings. Two parameters are of particular interest: the distribution of synaptic strengths and synaptic connectivity. To design an efficient liquid filter that performs desired kernel functions, these parameters need to be optimized. We have studied performance as a function of these parameters for several models of synaptic connectivity. The results show that in order to achieve good performance, large synaptic weights are required to compensate for a small number of synapses in the liquid filter, and vice versa. In addition, a larger variance of the synaptic weights results in better performance for LSM benchmark problems. We also propose a genetic algorithm-based approach to evolve the liquid filter from a minimum structure with no connections, to an optimized kernel with a minimal number of synapses and high classification accuracy. This approach facilitates the design of an optimal LSM with reduced computational complexity. Results obtained using this genetic programming approach show that the synaptic weight distribution after evolution is similar in shape to that found in cortical circuitry.

The distribution of synaptic strengths and possible mechanism for synaptic plasticity arise in the model of inter-spine molecular transport of PSD-95 molecules.

The distribution of synaptic strengths and possible mechanism for synaptic plasticity arise in the model of inter-spine molecular transport of PSD-95 molecules.

Adaptive self-organization in a realistic neural network model

Information processing in complex systems is often found to be maximally efficient close to critical states associated with phase transitions. It is therefore conceivable that also neural information processing operates close to criticality. This is further supported by the observation of power-law distributions, which are a hallmark of phase transitions. An important open question is how neural networks could remain close to a critical point while undergoing a continual change in the course of development, adaptation, learning, and more. An influential contribution was made by Bornholdt and Rohlf, introducing a generic mechanism of robust self-organized criticality in adaptive networks. Here, we address the question whether this mechanism is relevant for real neural networks. We show in a realistic model that spike-time-dependent synaptic plasticity can self-organize neural networks robustly toward criticality. Our model reproduces several empirical observations and makes testable predictions on the distribution of synaptic strength, relating them to the critical state of the network. These results suggest that the interplay between dynamics and topology may be essential for neural information processing.

Simple spike activity predicts occurrence of complex spikes in cerebellar Purkinje cells.

Purkinje cells are the only output neurons in the cerebellar cortex, which plays a vital if poorly understood role in the coordination of movement. Purkinje cells show two forms of activity. Complex spikes (CS) occur at a low frequency and are driven by excitatory input from a single climbing fiber, whereas simple spikes (SS) occur at much higher frequency and are driven by the inputs from a much larger number of parallel fibers (Fig. 1). It is well established that the parallel fiber-Purkinje cell synapse can undergo long-term depression (LTD) in response to the coincident firing of both parallel and climbing fibers1. There is also some evidence that repetitive firing of parallel fibers alone can induce long-term potentiation (LTP) at the same synapses. Presumably, these opposing tendencies must be balanced in order to maintain an appropriate distribution of synaptic strengths, and the climbing fibers are likely to play an important role in controlling this balance.

Long-term enhancement of hippocampal synaptic transmission and the acquisition of spatial information

The hypothesis that memories are stored as a specific distribution of strengths in a population of modifiable synapses was examined by the bilateral induction of long-term enhancement in synapses of the main afferent fiber system to the hippocampal formation in rats. Brief, high-frequency activation of the perforant pathway in chronically prepared animals resulted in a persistent increase in the field EPSP and population spike, measured extracellularly in fascia dentata. This treatment resulted in a profound and persistent deficit in the acquisition of new spatial information in a task requiring spatial "reference" memory, and disruption of recently acquired spatial information. Well-established spatial memory was completely unaffected, however, as was the acquisition of spatial information into short-term "working" memory. These results support the hypothesis that, during the formation of "cognitive maps," spatial information must be temporarily stored at modifiable synapses at the input stage to the hippocampal formation, but that this information is not needed once the representation of the environment is well established. Spatial working memory, in a familiar environment, appears not to depend on the distribution of synaptic strengths in this system at all.