Aonan Tang - Academia.edu (original) (raw)

Papers by Aonan Tang

A few strong connections: optimizing information retention in neuronal avalanches

Journal of Neurosurgery: Pediatrics, 2008

Introduction Epileptogenicity of neuronal tissues requires both altered excitability and altered ... more Introduction Epileptogenicity of neuronal tissues requires both altered excitability and altered synchronization of neurons. However, the network-level mechanisms responsible for neuronal hyperexcitability and synchronization remain unknown, and there is much to learn regarding how even small networks of neurons interact. The present study examines local and network properties of cortical neurons from epileptogenic human and excited (“epileptic”) rat cortex. Methods Epileptogenic cortex was harvested from pediatric patients with medically refractory seizures undergoing resective surgery. Local field potential signals (LFPs) were recorded continuously for up to several hours with a 60-channel microelectrode array. We also recorded LFPs from slices and organotypic and dissociated cultures of rat cortex bathed in high K+ and low Mg++. We then compared the human and rat data, applied a second-order maximum entropy model (MEM) to the data, and explored how well the MEM predicted sequence...

Entropy, 2010

Understanding how ensembles of neurons collectively interact will be a key step in developing a m... more Understanding how ensembles of neurons collectively interact will be a key step in developing a mechanistic theory of cognitive processes. Recent progress in multineuron recording and analysis techniques has generated tremendous excitement over the physiology of living neural networks. One of the key developments driving this interest is a new class of models based on the principle of maximum entropy. Maximum entropy models have been reported to account for spatial correlation structure in ensembles of neurons recorded from several different types of data. Importantly, these models require only information about the firing rates of individual neurons and their pairwise correlations. If this approach is generally applicable, it would drastically simplify the problem of understanding how neural networks behave. Given the interest in this method, several groups now have worked to extend maximum entropy models to account for temporal correlations. Here, we review how maximum entropy models have been applied to neuronal ensemble data to account for spatial and temporal correlations. We also discuss criticisms of the maximum entropy approach that argue that it is not generally applicable to larger ensembles of neurons. We conclude that future maximum entropy models will need OPEN ACCESS Entropy 2010, 12 90 to address three issues: temporal correlations, higher-order correlations, and larger ensemble sizes. Finally, we provide a brief list of topics for future research.

BMC Neuroscience, 2010

Background How living neural networks retain information is still incompletely understood. Two pr... more Background How living neural networks retain information is still incompletely understood. Two prominent ideas on this topic have developed in parallel, but have remained somewhat unconnected. The first of these, the "synaptic hypothesis," holds that information can be retained in synaptic connection strengths, or weights, between neurons. Recent work inspired by statistical mechanics has suggested that networks will retain the most information when their weights are distributed in a skewed manner, with many weak weights and only a few strong ones. The second of these ideas is that information can be represented by stable activity patterns. Multineuron recordings have shown that sequences of neural activity distributed over many neurons are repeated above chance levels when animals perform well-learned tasks. Although these two ideas are compelling, no one to our knowledge has yet linked the predicted optimum distribution of weights to stable activity patterns actually obs...

Applied Physics Letters, 2002

The Jahn-Teller effect in the charge-ordered (CO) state for La 1-x Ca x MnO 3 (0.5≤x≤0.87) was st... more The Jahn-Teller effect in the charge-ordered (CO) state for La 1-x Ca x MnO 3 (0.5≤x≤0.87) was studied by measuring the low-temperature powder x-ray diffraction, internal friction, and shear modulus. We find that the electron-lattice interaction with the static Jahn-Teller distortion is the strongest near x ≈ 0.75 in the CO state. It was particularly observed that a crossover of the Jahn-Teller vibration mode from Q 2 to Q 3 near x=0.75 induces crossovers of the crystal structure from tetragonally compressed to tetragonally elongated orthorhombic, and of the magnetic structure from CE-type to C-type near x=0.75. The experimental results give strong evidence that the Jahn-Teller effect not only plays a key role in stabilizing the CO state, but also determines the magnetic and crystal structures in the CO state for La 1-x Ca x MnO 3 .

Bulletin of the American Physical Society, Mar 10, 2008

The average cortical neuron makes and receives about 1,000 synaptic contacts. This anatomical inf... more The average cortical neuron makes and receives about 1,000 synaptic contacts. This anatomical information suggests that local cortical networks are connected in a fairly democratic manner, with all nodes having about the same degree. But the physical connections found in the brain do not necessarily reveal how information flows through the network. We used transfer entropy (Schreiber, 2000) to assess effective connectivity in cortical slice cultures placed on a 512 electrode array system (in collaboration with Alan ...

Bulletin of the American Physical Society, Mar 18, 2009

The dynamics found in local cortical networks strongly impact the types of computations they can ... more The dynamics found in local cortical networks strongly impact the types of computations they can perform. Major classes of cortical network models assume that spatio-temporal activity evolves with either ultra-stable, chaotic or neutral dynamics. While experimental evidence has demonstrated that repeatable activity states can exist in cortical networks, it is still unclear what the spatio-temporal dynamics near these states are. To accurately address this question, the trajectories of similar, but not identical, inputs must be quantified. We use 60 ...

Bulletin of the American Physical Society, Mar 18, 2009

How does information flow through networks of neurons? The type of network topology revealed coul... more How does information flow through networks of neurons? The type of network topology revealed could have important consequences for network efficiency and robustness to damage. Several tools, including transfer entropy, Granger causality, and directed information can be applied to this question. Yet indirect connections, connections with various delays, and feedback loops can complicate the task of uncovering the information flow structure. We have applied the above methods in simple validation studies, ...

Journal of Neuroscience, 2008

Multineuron firing patterns are often observed, yet are predicted to be rare by models that assum... more Multineuron firing patterns are often observed, yet are predicted to be rare by models that assume independent firing. To explain these correlated network states, two groups recently applied a second-order maximum entropy model that used only observed firing rates and pairwise interactions as parameters (Schneidman et al., 2006; Shlens et al., 2006). Interestingly, with these minimal assumptions they predicted 90-99% of network correlations. If generally applicable, this approach could vastly simplify analyses of complex networks. However, this initial work was done largely on retinal tissue, and its applicability to cortical circuits is mostly unknown. This work also did not address the temporal evolution of correlated states. To investigate these issues, we applied the model to multielectrode data containing spontaneous spikes or local field potentials from cortical slices and cultures. The model worked slightly less well in cortex than in retina, accounting for 88 Ϯ 7% (mean Ϯ SD) of network correlations. In addition, in 8 of 13 preparations, the observed sequences of correlated states were significantly longer than predicted by concatenating states from the model. This suggested that temporal dependencies are a common feature of cortical network activity, and should be considered in future models. We found a significant relationship between strong pairwise temporal correlations and observed sequence length, suggesting that pairwise temporal correlations may allow the model to be extended into the temporal domain. We conclude that although a second-order maximum entropy model successfully predicts correlated states in cortical networks, it should be extended to account for temporal correlations observed between states.

Bulletin of the American Physical Society, Mar 10, 2008

Multi-neuron firing states are often observed, yet are predicted to be rare by models that assume... more Multi-neuron firing states are often observed, yet are predicted to be rare by models that assume independent firing. To predict these states, two groups recently applied a second-order maximum entropy model that used only observed firing rates and pairwise interactions as parameters (Schneidman et al., 2006; Shlens et al., 2006). Interestingly, these models predicted 90-99 {\%} of network correlations. If generally applicable, this approach could vastly simplify analyses of complex networks. However, this work did not address the ...

Entropy, 2010

Understanding how ensembles of neurons collectively interact will be a key step in developing a m... more Understanding how ensembles of neurons collectively interact will be a key step in developing a mechanistic theory of cognitive processes. Recent progress in multineuron recording and analysis techniques has generated tremendous excitement over the physiology of living neural networks. One of the key developments driving this interest is a new class of models based on the principle of maximum entropy. Maximum entropy models have been reported to account for spatial correlation structure in ensembles of neurons recorded from several different types of data. Importantly, these models require only information about the firing rates of individual neurons and their pairwise correlations. If this approach is generally applicable, it would drastically simplify the problem of understanding how neural networks behave. Given the interest in this method, several groups now have worked to extend maximum entropy models to account for temporal correlations. Here, we review how maximum entropy models have been applied to neuronal ensemble data to account for spatial and temporal correlations. We also discuss criticisms of the maximum entropy approach that argue that it is not generally applicable to larger ensembles of neurons. We conclude that future maximum entropy models will need OPEN ACCESS Entropy 2010, 12 90 to address three issues: temporal correlations, higher-order correlations, and larger ensemble sizes. Finally, we provide a brief list of topics for future research.

A few strong connections: optimizing information retention in neuronal avalanches

Journal of Neurosurgery: Pediatrics, 2008

Introduction Epileptogenicity of neuronal tissues requires both altered excitability and altered ... more Introduction Epileptogenicity of neuronal tissues requires both altered excitability and altered synchronization of neurons. However, the network-level mechanisms responsible for neuronal hyperexcitability and synchronization remain unknown, and there is much to learn regarding how even small networks of neurons interact. The present study examines local and network properties of cortical neurons from epileptogenic human and excited (“epileptic”) rat cortex. Methods Epileptogenic cortex was harvested from pediatric patients with medically refractory seizures undergoing resective surgery. Local field potential signals (LFPs) were recorded continuously for up to several hours with a 60-channel microelectrode array. We also recorded LFPs from slices and organotypic and dissociated cultures of rat cortex bathed in high K+ and low Mg++. We then compared the human and rat data, applied a second-order maximum entropy model (MEM) to the data, and explored how well the MEM predicted sequence...

Entropy, 2010

Understanding how ensembles of neurons collectively interact will be a key step in developing a m... more Understanding how ensembles of neurons collectively interact will be a key step in developing a mechanistic theory of cognitive processes. Recent progress in multineuron recording and analysis techniques has generated tremendous excitement over the physiology of living neural networks. One of the key developments driving this interest is a new class of models based on the principle of maximum entropy. Maximum entropy models have been reported to account for spatial correlation structure in ensembles of neurons recorded from several different types of data. Importantly, these models require only information about the firing rates of individual neurons and their pairwise correlations. If this approach is generally applicable, it would drastically simplify the problem of understanding how neural networks behave. Given the interest in this method, several groups now have worked to extend maximum entropy models to account for temporal correlations. Here, we review how maximum entropy models have been applied to neuronal ensemble data to account for spatial and temporal correlations. We also discuss criticisms of the maximum entropy approach that argue that it is not generally applicable to larger ensembles of neurons. We conclude that future maximum entropy models will need OPEN ACCESS Entropy 2010, 12 90 to address three issues: temporal correlations, higher-order correlations, and larger ensemble sizes. Finally, we provide a brief list of topics for future research.

BMC Neuroscience, 2010

Background How living neural networks retain information is still incompletely understood. Two pr... more Background How living neural networks retain information is still incompletely understood. Two prominent ideas on this topic have developed in parallel, but have remained somewhat unconnected. The first of these, the "synaptic hypothesis," holds that information can be retained in synaptic connection strengths, or weights, between neurons. Recent work inspired by statistical mechanics has suggested that networks will retain the most information when their weights are distributed in a skewed manner, with many weak weights and only a few strong ones. The second of these ideas is that information can be represented by stable activity patterns. Multineuron recordings have shown that sequences of neural activity distributed over many neurons are repeated above chance levels when animals perform well-learned tasks. Although these two ideas are compelling, no one to our knowledge has yet linked the predicted optimum distribution of weights to stable activity patterns actually obs...

Applied Physics Letters, 2002

The Jahn-Teller effect in the charge-ordered (CO) state for La 1-x Ca x MnO 3 (0.5≤x≤0.87) was st... more The Jahn-Teller effect in the charge-ordered (CO) state for La 1-x Ca x MnO 3 (0.5≤x≤0.87) was studied by measuring the low-temperature powder x-ray diffraction, internal friction, and shear modulus. We find that the electron-lattice interaction with the static Jahn-Teller distortion is the strongest near x ≈ 0.75 in the CO state. It was particularly observed that a crossover of the Jahn-Teller vibration mode from Q 2 to Q 3 near x=0.75 induces crossovers of the crystal structure from tetragonally compressed to tetragonally elongated orthorhombic, and of the magnetic structure from CE-type to C-type near x=0.75. The experimental results give strong evidence that the Jahn-Teller effect not only plays a key role in stabilizing the CO state, but also determines the magnetic and crystal structures in the CO state for La 1-x Ca x MnO 3 .

Bulletin of the American Physical Society, Mar 10, 2008

The average cortical neuron makes and receives about 1,000 synaptic contacts. This anatomical inf... more The average cortical neuron makes and receives about 1,000 synaptic contacts. This anatomical information suggests that local cortical networks are connected in a fairly democratic manner, with all nodes having about the same degree. But the physical connections found in the brain do not necessarily reveal how information flows through the network. We used transfer entropy (Schreiber, 2000) to assess effective connectivity in cortical slice cultures placed on a 512 electrode array system (in collaboration with Alan ...

Bulletin of the American Physical Society, Mar 18, 2009

The dynamics found in local cortical networks strongly impact the types of computations they can ... more The dynamics found in local cortical networks strongly impact the types of computations they can perform. Major classes of cortical network models assume that spatio-temporal activity evolves with either ultra-stable, chaotic or neutral dynamics. While experimental evidence has demonstrated that repeatable activity states can exist in cortical networks, it is still unclear what the spatio-temporal dynamics near these states are. To accurately address this question, the trajectories of similar, but not identical, inputs must be quantified. We use 60 ...

Bulletin of the American Physical Society, Mar 18, 2009

How does information flow through networks of neurons? The type of network topology revealed coul... more How does information flow through networks of neurons? The type of network topology revealed could have important consequences for network efficiency and robustness to damage. Several tools, including transfer entropy, Granger causality, and directed information can be applied to this question. Yet indirect connections, connections with various delays, and feedback loops can complicate the task of uncovering the information flow structure. We have applied the above methods in simple validation studies, ...

Journal of Neuroscience, 2008

Multineuron firing patterns are often observed, yet are predicted to be rare by models that assum... more Multineuron firing patterns are often observed, yet are predicted to be rare by models that assume independent firing. To explain these correlated network states, two groups recently applied a second-order maximum entropy model that used only observed firing rates and pairwise interactions as parameters (Schneidman et al., 2006; Shlens et al., 2006). Interestingly, with these minimal assumptions they predicted 90-99% of network correlations. If generally applicable, this approach could vastly simplify analyses of complex networks. However, this initial work was done largely on retinal tissue, and its applicability to cortical circuits is mostly unknown. This work also did not address the temporal evolution of correlated states. To investigate these issues, we applied the model to multielectrode data containing spontaneous spikes or local field potentials from cortical slices and cultures. The model worked slightly less well in cortex than in retina, accounting for 88 Ϯ 7% (mean Ϯ SD) of network correlations. In addition, in 8 of 13 preparations, the observed sequences of correlated states were significantly longer than predicted by concatenating states from the model. This suggested that temporal dependencies are a common feature of cortical network activity, and should be considered in future models. We found a significant relationship between strong pairwise temporal correlations and observed sequence length, suggesting that pairwise temporal correlations may allow the model to be extended into the temporal domain. We conclude that although a second-order maximum entropy model successfully predicts correlated states in cortical networks, it should be extended to account for temporal correlations observed between states.

Bulletin of the American Physical Society, Mar 10, 2008

Multi-neuron firing states are often observed, yet are predicted to be rare by models that assume... more Multi-neuron firing states are often observed, yet are predicted to be rare by models that assume independent firing. To predict these states, two groups recently applied a second-order maximum entropy model that used only observed firing rates and pairwise interactions as parameters (Schneidman et al., 2006; Shlens et al., 2006). Interestingly, these models predicted 90-99 {\%} of network correlations. If generally applicable, this approach could vastly simplify analyses of complex networks. However, this work did not address the ...

Entropy, 2010

Understanding how ensembles of neurons collectively interact will be a key step in developing a m... more Understanding how ensembles of neurons collectively interact will be a key step in developing a mechanistic theory of cognitive processes. Recent progress in multineuron recording and analysis techniques has generated tremendous excitement over the physiology of living neural networks. One of the key developments driving this interest is a new class of models based on the principle of maximum entropy. Maximum entropy models have been reported to account for spatial correlation structure in ensembles of neurons recorded from several different types of data. Importantly, these models require only information about the firing rates of individual neurons and their pairwise correlations. If this approach is generally applicable, it would drastically simplify the problem of understanding how neural networks behave. Given the interest in this method, several groups now have worked to extend maximum entropy models to account for temporal correlations. Here, we review how maximum entropy models have been applied to neuronal ensemble data to account for spatial and temporal correlations. We also discuss criticisms of the maximum entropy approach that argue that it is not generally applicable to larger ensembles of neurons. We conclude that future maximum entropy models will need OPEN ACCESS Entropy 2010, 12 90 to address three issues: temporal correlations, higher-order correlations, and larger ensemble sizes. Finally, we provide a brief list of topics for future research.