Pengsheng Zheng

Memory consolidation is a category of processes that stabilize a memory trace after the initial acquisition. Recent studies have provided extensive experimental evidences that hippocampus plays a prominent role in the formation of episodic memory, and that occurrences of sharp wave-ripple (SPW-R) complexes and the associated network activity are critical in memory consolidation. However, the precise role of SPW-R in memory trace formation remains elusive.  Concurrent cortical/hippocampal/thalamal recordings and whole-brain activity mapping allow us to estimate the information transfer between multiple cell assembles in different brain regions. My research goal is to get a better understanding of how different oscillatory neuronal activities organize information processing within and across brain regions, especially the network activity during sharp-wave ripples and their contribution to the memory trace formation process.

Current Project Introduction: Effects of Learning on Hipp-Thal-PFC Interactions During SPW-R in Awake Rats

Memory consolidation is a category of processes that stabilize a memory trace after the initial acquisition. Recent studies have provided extensive experimental evidences that hippocampus plays a prominent role in the formation of episodic memory, and that occurrences of sharp wave-ripple (SPW-R) complexes and the associated network activity are critical in memory consolidation. However, the precise role of SPW-R in memory trace formation remains elusive.  Concurrent cortical/hippocampal/thalamal recordings and whole-brain activity mapping allow us to estimate the information transfer between multiple cell assembles in different brain regions. My research goal is to get a better understanding of how different oscillatory neuronal activities organize information processing within and across brain regions, especially the network activity during sharp-wave ripples and their contribution to the memory trace formation process.

Former Project Introduction: Mathematical modeling of different forms of synaptic plasticity.

Biological neural networks are shaped by a large number of plasticity mechanisms operating at different time scales. How these mechanisms work together to sculpt such networks into efficient information processing circuits is still poorly understood. We show that fundamental characteristics of excitatory synaptic connections in cortex and hippocampus can be explained as a consequence of self-organization in a recurrent network combining spike-timing-dependent plasticity (STDP), structural plasticity and different forms of homeostatic plasticity.

Related publications:

1. Zheng P, Dimitrakakis C and Triesch J (January-2013) Network Self-Organization Explains the Statistics and Dynamics of Synaptic Connection Strengths in Cortex, PLoS Computational Biology 9(1) 1-8.

2. Eser J, Zheng P  and Triesch J (January-2014) Nonlinear dynamics analysis of a self-organizing recurrent neural network: chaos waning, PLoS ONE 9(1): e86962. doi:10.1371/journal.pone.0086962.

3. Zheng P and Triesch J (June-2014) Robust development of synfire chains from multiple plasticity mechanisms Frontiers in Computational Neuroscience 8(66) 1-10.

Below: Complex network and synfire chain spontaneously developed by network self-organization

Former Project Introduction: Multi-stability Analysis and Design of Nonlinear Dynamical Systems

This was my PhD project which got inspirations from the traditional neural associative memory by answering the questions: Can we arbitrarily control the multistability of a dynamical system? How can we design a multistable dynamical system? Benefiting from these works, a lot of neural associative memory models now can memorize and retrieve an arbitrary set of patterns (binary, nonbinary patterns and color images with limited capacities) with almost arbitrary connection topology. Further, we successfully applied it to information retrieval, pattern recognition and data classification tasks.

Related publications:

1. Zheng P (2014) Threshold Complex-Valued Neural Associative Memory IEEE Transactions on Neural Networks and Learning Systems 25(9) 1714-1718.

2. Zheng P, Zhang J and Tang W (March-2011) Learning Associative Memories by Error Backpropagation, IEEE Transactions on Neural Networks 22(3) 347-355.

3. Zheng P, Zhang J and Tang W (October-2010) Color image associative memory on a class of Cohen–Grossberg networks,  Pattern Recognition 43(10) 3255–3260.