Structural Plasticity, Effectual Connectivity, and Memory in Cortex

Abstract

Learning and memory is commonly attributed to the modification of synaptic strengths in neuronal networks. More recent experiments have also revealed a major role of structural plasticity including elimination and regeneration of synapses, growth and retraction of dendritic spines, and remodeling of axons and dendrites. Here we work out the idea that one likely function of structural plasticity is to increase “effectual connectivity” in order to improve the capacity of sparsely connected networks to store Hebbian cell assemblies that are supposed to represent memories. For this we define effectual connectivity as the fraction of synaptically linked neuron pairs within a cell assembly representing a memory. We show by theory and numerical simulation the close links between effectual connectivity and both information storage capacity of neural networks and effective connectivity as commonly employed in functional brain imaging and connectome analysis. Then, by applying our model to a recently proposed memory model, we can give improved estimates on the number of cell assemblies that can be stored in a cortical macrocolumn assuming realistic connectivity. Finally, we derive a simplified model of structural plasticity to enable large scale simulation of memory phenomena, and apply our model to link ongoing adult structural plasticity to recent behavioral data on the spacing effect of learning.

Regionally Specific Regulation of Sensorimotor Network Connectivity Following Tactile Improvement

“…Our present results from task-free rs-fMRI show that this increased neural synchronization within the SEMO network still persists after stimulation has ceased and is strongly correlated with both the improvement and performance levels of tactile discrimination after stimulation. These episodic changes in coherence may support the early onset of (structural) changes in microcircuitry (see [ 50 ] for review). We assume that the neuronal oscillation might be the functional surrogate of behavioral improvement.…”

How Memory Conforms to Brain Development

“…Both sparseness and heterogeneity damage severely the memory retrieval ability of the neural network that, for such cases, diminishes fast with P compared with the case of highly connected and homogeneous neural networks (Stauffer et al, 2003; Castillo et al, 2004; Morelli et al, 2004; Torres et al, 2004; Oshima and Odagaki, 2007; Akam and Kullmann, 2014) However, there is experimental evidence that the configurations of neural activity related to particular memories in the animal brain involve many more silent neurons, ξiμ=0, than active ones, ξiμ=1 (Chklovskii et al, 2004; Akam and Kullmann, 2014). Notice that in this case there is a positive correlation between different patterns due to the sparseness, since a 0 ≠ 0.5, which is also known to improve the storage capacity of a neural network (Knoblauch et al, 2014; Knoblauch and Sommer, 2016), and in particular that of heterogeneous and sparse neural networks (Morelli et al, 2004). Consequently, we consider here this kind of activity patterns, and we further define them as non-overlapping regions of active neurons, each consisting of N / P neurons, so that they cover the whole network (and therefore the mean activity of the patterns is a0=P-1).…”

“…Consequently, we consider here this kind of activity patterns, and we further define them as non-overlapping regions of active neurons, each consisting of N / P neurons, so that they cover the whole network (and therefore the mean activity of the patterns is a0=P-1). This corresponds to a particular definition of sparse or biased patterns, which in other works have been considered to be randomly distributed with a given a 0 (Knoblauch et al, 2014; Knoblauch and Sommer, 2016), what allows for a good visualization of the activity of the network by means of the raster plots.…”

“…In particular, recent studies on associative memory have shown that this process could greatly improve memory retrieval under a noisy environment, such as it is the case in biological systems (Millán et al, 2018a). Moreover, ongoing structural plasticity in the adult brain has also been suggested to improve substantially the storage capacity (Chklovskii et al, 2004; Knoblauch et al, 2010), and has been related to graded amnesia, catastrophic forgetting, and the spacing effect (Knoblauch et al, 2014; Knoblauch and Sommer, 2016). These results are based on the fact that the number of potential synapses a neuron could develop, i.e., its potential connectivity, is much greater than the actual number of synapses, and structural plasticity allows the system to explore different wiring possibilities (Stepanyants et al, 2002; Fares and Stepanyants, 2009).…”

Making Memories: Why Time Matters

“…Experimental results suggest learning and memory occur in the context of continual structural synaptic plasticity such as formation and elimination of synapses (Chklovskii et al, 2004 ; Holtmaat and Svoboda, 2009 ). Understanding these processes can be enhanced through modeling potential mechanisms to explain experimental findings (Knoblauch and Sommer, 2016 ; Knoblauch, 2017 ) or apply them in a new context (Popovych et al, 2015 ; Abdou et al, 2018 ). The most significant recent discoveries are in glia biology, and the roles of astrocytes and myelin in cognition and learning (Fields et al, 2014 ).…”