Hebbian Wiring Plasticity Generates Efficient Network Structures for Robust Inference with Synaptic Weight Plasticity

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

Retrieval Properties of Hopfield and Correlated Attractors in an Associative Memory Model

We examine a previouly introduced attractor neural network model that explains the persistent activities of neurons in the anterior ventral temporal cortex of the brain. In this model, the coexistence of several attractors including correlated attractors was reported in the cases of finite and infinite loading. In this paper, by means of a statistical mechanical method, we study the statics and dynamics of the model in both finite and extensive loading, mainly focusing on the retrieval properties of the Hopfield and correlated attractors. In the extensive loading case, we derive the evolution equations by the dynamical replica theory. We found several characteristic temporal behaviours, both in the finite and extensive loading cases. The theoretical results were confirmed by numerical simulations.Comment: 12 pages, 7 figure

Phase Transitions of an Oscillator Neural Network with a Standard Hebb Learning Rule

Studies have been made on the phase transition phenomena of an oscillator network model based on a standard Hebb learning rule like the Hopfield model. The relative phase informations---the in-phase and anti-phase, can be embedded in the network. By self-consistent signal-to-noise analysis (SCSNA), it was found that the storage capacity is given by $\alpha_c = 0.042$, which is better than that of Cook's model. However, the retrieval quality is worse. In addition, an investigation was made into an acceleration effect caused by asymmetry of the phase dynamics. Finally, it was numerically shown that the storage capacity can be improved by modifying the shape of the coupling function.Comment: 10 pages, 6 figure

Respon Tanaman Kacang-Kacangan yang Bersifat Determinate dan Indeterminate pada Berbagai Kondisi Ketersediaan Air

Growth and yield of soybean and blackgram, having determinate and indeterminate in flowering respectively, were compared under three water regimes. Under well-watered conditions, blackgram continuously produces flowers where under rainfed + irrigation and rainfed condition, two periods of flowering can be distinguished. While only one period of flowering was observed in soybean. The second flush of flowering of blackgram in rainfed and rainfed + irrigation areas occurred during high rain fall. Partial irrigation as much as 80 mm toward the end of pod filling in soybean did not give benefit to soybean, but irrigation stimulate flowering in blackgram and increase yield up to 25%. Thus indeterminate behavior might give higher yield under rainfed condition due to its flexibility of flowering

Detailed dendritic excitatory/inhibitory balance through heterosynaptic spike-timing-dependent plasticity

The balance between excitatory and inhibitory inputs is a key feature of cortical dynamics. Such a balance is arguably preserved in dendritic branches, yet its underlying mechanism and functional roles remain unknown. In this study, we developed computational models of heterosynaptic spike-timing-dependent plasticity (STDP) to show that the excitatory/inhibitory balance in dendritic branches is robustly achieved through heterosynaptic interactions between excitatory and inhibitory synapses. The model reproduces key features of experimental heterosynaptic STDP well, and provides analytical insights. Furthermore, heterosynaptic STDP explains how the maturation of inhibitory neurons modulates the selectivity of excitatory neurons for binocular matching in the critical period plasticity. The model also provides an alternative explanation for the potential mechanism underlying the somatic detailed balance that is commonly associated with inhibitory STDP. Our results propose heterosynaptic STDP as a critical factor in synaptic organization and the resultant dendritic computation

Redundancy in synaptic connections enables neurons to learn optimally

Recent experimental studies suggest that, in cortical microcircuits of the mammalian brain, the majority of neuron-to-neuron connections are realized by multiple synapses. However, it is not known whether such redundant synaptic connections provide any functional benefit. Here, we show that redundant synaptic connections enable near-optimal learning in cooperation with synaptic rewiring. By constructing a simple dendritic neuron model, we demonstrate that with multisynaptic connections synaptic plasticity approximates a sample-based Bayesian filtering algorithm known as particle filtering, and wiring plasticity implements its resampling process. Extending the proposed framework to a detailed single-neuron model of perceptual learning in the primary visual cortex, we show that the model accounts for many experimental observations. In particular, the proposed model reproduces the dendritic position dependence of spike-timing-dependent plasticity and the functional synaptic organization on the dendritic tree based on the stimulus selectivity of presynaptic neurons. Our study provides a conceptual framework for synaptic plasticity and rewiring