Abstract
The impact of learning and long-term memory storage on synaptic connectivity is not completely understood. In this study, we examine the effects of associative learning on synaptic connectivity in adult cortical circuits by hypothesizing that these circuits function in a steady-state, in which the memory capacity of a circuit is maximal and learning must be accompanied by forgetting. Steady-state circuits should be characterized by unique connectivity features. To uncover such features we developed a biologically constrained, exactly solvable model of associative memory storage. The model is applicable to networks of multiple excitatory and inhibitory neuron classes and can account for homeostatic constraints on the number and the overall weight of functional connections received by each neuron. The results show that in spite of a large number of neuron classes, functional connections between potentially connected cells are realized with less than 50% probability if the presynaptic cell is excitatory and generally a much greater probability if it is inhibitory. We also find that constraining the overall weight of presynaptic connections leads to Gaussian connection weight distributions that are truncated at zero. In contrast, constraining the total number of functional presynaptic connections leads to non-Gaussian distributions, in which weak connections are absent. These theoretical predictions are compared with a large dataset of published experimental studies reporting amplitudes of unitary postsynaptic potentials and probabilities of connections between various classes of excitatory and inhibitory neurons in the cerebellum, neocortex, and hippocampus.
Highlights
It has long been known that learning and long-term memory formation in the brain are accompanied with changes in the patterns and weights of synaptic connections
We extend the steady-state learning model described in Chapeton et al (2012) by considering multiple classes of excitatory and inhibitory neurons and by incorporating biologically motivated homeostatic constraints
The sign of a unitary postsynaptic potential (uPSP) in a cortical neuron is dependent on the class of the presynaptic cell; it is positive if the presynaptic cell is excitatory and negative if it is inhibitory
Summary
It has long been known that learning and long-term memory formation in the brain are accompanied with changes in the patterns and weights of synaptic connections (see Bailey and Kandel, 1993; Chklovskii et al, 2004; Holtmaat and Svoboda, 2009 for review). Inspired by the ideas introduced by Gardner and Derrida (1988) and further developed by Brunel et al (2004), we hypothesized that a given local circuit of the adult cortex is functioning in a steady-state In this state the associative memory storage capacity of the circuit is maximal (critical) (Cover, 1965; Hopfield, 1982; Gardner, 1988; Gardner and Derrida, 1988), and learning new associations is accompanied with forgetting some of the old ones (Figure 1). The steady-state learning hypothesis is supported by computational studies conducted in the cerebellar (Brunel et al, 2004; Barbour et al, 2007) and cerebral (Chapeton et al, 2012) cortices It is consistent with recent experimental evidence from human subjects, showing that new learning and memory. The results were compared with a large number of published experimental studies reporting probabilities of connections and distributions of connection weights for various classes of excitatory and inhibitory neurons in the cerebellum, neocortex, and hippocampus
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