Abstract
Collective rhythmic dynamics from neurons is vital for cognitive functions such as memory formation but how neurons self-organize to produce such activity is not well understood. Attractor-based computational models have been successfully implemented as a theoretical framework for memory storage in networks of neurons. Additionally, activity-dependent modification of synaptic transmission is thought to be the physiological basis of learning and memory. The goal of this study is to demonstrate that using a pharmacological treatment that has been shown to increase synaptic strength within in vitro networks of hippocampal neurons follows the dynamical postulates theorized by attractor models. We use a grid of extracellular electrodes to study changes in network activity after this perturbation and show that there is a persistent increase in overall spiking and bursting activity after treatment. This increase in activity appears to recruit more “errant” spikes into bursts. Phase plots indicate a conserved activity pattern suggesting that a synaptic potentiation perturbation to the attractor leaves it unchanged. Lastly, we construct a computational model to demonstrate that these synaptic perturbations can account for the dynamical changes seen within the network.
Highlights
A major focus in dynamical neuroscience is identifying the neural patterns of activity that characterize human behavior as well as its surroundings
Computational Model While it has been shown that the trafficking of AMPA receptors to the synapse accounts for the biological mechanism underlying LTP on a small spatial scale [28,31,32], collective neural activity is not linear and we investigated whether the manipulation of AMPA receptors might account for our observed network-wide dynamical effects
Our integrated results demonstrate that a synaptic perturbation can account for a profound change in network dynamics
Summary
A major focus in dynamical neuroscience is identifying the neural patterns of activity that characterize human behavior as well as its surroundings. An important question is how neurons selforganize into clusters or assemblies of coherent activity. These clusters of neural activity are thought to represent patterns that define different features within the external environment and the cluster constituents might change to reflect different environmental elements such as color and shading. It is thought that a stimulus-dependent persistence in neural activity underlies active, i.e., working memory [7,8,9,10] and was first postulated by Hebb [7] This persistent activity is thought to be the result of strong reciprocal or recurrent excitatory connections between co-active neurons. The self-organization of activity displayed by neurons in these types of recurrent circuits accounts for the ‘delay between stimulation and response, that seems so characteristic of thought’ [7]
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