Abstract
In the adult mammalian cortex, a small fraction of spines are created and eliminated every day, and the resultant synaptic connection structure is highly nonrandom, even in local circuits. However, it remains unknown whether a particular synaptic connection structure is functionally advantageous in local circuits, and why creation and elimination of synaptic connections is necessary in addition to rich synaptic weight plasticity. To answer these questions, we studied an inference task model through theoretical and numerical analyses. We demonstrate that a robustly beneficial network structure naturally emerges by combining Hebbian-type synaptic weight plasticity and wiring plasticity. Especially in a sparsely connected network, wiring plasticity achieves reliable computation by enabling efficient information transmission. Furthermore, the proposed rule reproduces experimental observed correlation between spine dynamics and task performance.
Highlights
The amplitude of excitatory and inhibitory postsynaptic potentials (EPSPs and IPSPs), often referred to as synaptic weight, is considered a fundamental variable in neural computation (Bliss and Collingridge, 1993; Dayan and Abbott, 2005)
It is known that synaptic wiring plasticity and the resultant synaptic connection structure are crucial for computation in the brain (Chklovskii et al, 2004; Holtmaat and Svoboda, 2009)
What should be represented by synaptic connections and their weights, and how are those representations acquired? To explore the answers to these questions, we studied a hidden variable estimation task (Figure 1A), which appears in various stages of neural information processing (Beck et al, 2008; Lochmann and Deneve, 2011)
Summary
The amplitude of excitatory and inhibitory postsynaptic potentials (EPSPs and IPSPs), often referred to as synaptic weight, is considered a fundamental variable in neural computation (Bliss and Collingridge, 1993; Dayan and Abbott, 2005). Previous theoretical results suggest that under appropriate synaptic plasticity, a randomly connected network is computationally sufficient for various tasks (Maass et al, 2002; Ganguli and Sompolinsky, 2012). It is known that synaptic wiring plasticity and the resultant synaptic connection structure are crucial for computation in the brain (Chklovskii et al, 2004; Holtmaat and Svoboda, 2009). Recent studies further revealed that spine dynamics are tightly correlated with the performance of motor-related tasks (Xu et al, 2009; Yang et al, 2009). Previous modeling studies suggest that wiring plasticity helps memory storage
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