Jianfeng Feng

Computational neuroscience : a comprehensive approach

A THEORETICAL OVERVIEW Introduction Deterministic Dynamical Systems Stochastic Dynamical Systems Information Theory Optimal Control ATOMISTIC SIMULATIONS OF ION CHANNELS Introduction Simulation Methods Selected Applications Outlook MODELING NEURONAL CALCIUM DYNAMICS Introduction Basic Principles Special Calcium Signaling for Neurons Conclusions STRUCTURE BASED MODELS OF NO DIFFUSION IN THE NERVOUS SYSTEM Introduction Methods Results Exploring Functional Roles with More Abstract Models Conclusions STOCHASTIC MODELING OF SINGLE ION CHANNELS Introduction Some Basic Probability Single Channel Models Transition Probabilities, Macroscopic Currents and Noise Macroscopic Currents and Noise Behaviour of Single Channels under Equilibrium Conditions Time Interval Omission Some Miscellaneous Topics THE BIOPHYSICAL BASIS OF FIRING VARIABILITY IN CORTICAL NEURONS Introduction Typical Input is Correlated and Irregular Synaptic Unreliability Postsynaptic Ion Channel Noise Integration of a Transient Input by Cortical Neurons Noisy Spike Generation Dynamics Dynamics of NMDA Receptors Class 1 and Class 2 Neurons Show Different Noise Sensitivities Cortical Cell Dynamical Classes Implications for Synchronous Firing Conclusions Generating Models of Single Neurons Introduction The Hypothalamo-Hypophysial System Statistical Methods to Investigate The Intrinsic Mechanisms Underlying Spike Patterning Summary and Conclusions GENERATING QUANTITATIVELY ACCURATE, BUT COMPUTATIONALLY CONCISE, MODELS OF SINGLE NEURONS Introduction The Hypothalamo-hypophysial System Statistical Methods to Investigate the Intrinsic Mechanisms Underlying Spike Patterning Summary and Conclusions BURSTING ACTIVITY IN WEAKLY ELECTRIC FISH Introduction Overview of the Electrosensory System Feature Extraction by Spike Bursts Factors Shaping Burst Firing In Vivo Conditional Action Potential Back Propagation Controls Burst Firing In Vitro Comparison with Other Bursting Neurons Conclusions LIKELIHOOD METHODS FOR NEURAL SPIKE TRAIN DATA ANALYSIS Introduction Theory Applications Conclusion Appendix BIOLOGICALLY-DETAILED NETWORK MODELING Introduction Cells Synapses Connections Inputs Implementation Validation Conclusions HEBBIAN LEARNING AND SPIKE-TIMING-DEPENDENT PLASTICITY Hebbian Models of Plasticity Spike-Timing Dependent Plasticity Role of Constraints in Hebbian Learning Competitive Hebbian Learning Through STDP Temporal Aspects of STDP STDP in a Network Conclusion CORRELATED NEURONAL ACTIVITY: HIGH-AND LOW-LEVEL VIEWS Introduction: the Timing Game Functional Roles for Spike Timing Correlations Arising from Common input Correlations Arising from Local Network Interactions When Are Neurons Sensitive to Correlated Input? A Simple, Quantitative Model Correlations and Neuronal Variability Conclusion Appendix A CASE STUDY OF POPULATION CODING: STIMULUS LOCALIZATION IN THE BARREL CORTEX Introduction Series Expansion Method The Whisker System Coding in the Whisker System Discussion Conclusions MODELING FLY MOTION VISION The Fly Motion Vision System: An Overview Mechanisms of Local Motion Detection: The Correlation Detector Spatial Processing of Local Motion Signals BY Lobula Plate Tangential Cells Conclusions MEAN-FIELD THEORY OF IRREGULARLY SPIKING NEURONAL POPULATIONS AND WORKING MEMORY IN RECURRENT CORTICAL NETWORKS Introduction Firing-Rate and Variability of a Spiking Neuron with Noisy input Self-Consistent Theory of Recurrent Cortical Circuits THE OPERATION OF MEMORY SYSTEMS IN THE BRAIN Introduction Functions of the Hippocampus in Long-Term Memory Short Term Memory Systems Invariant Visual Object Recognition Visual Stimulus-Reward Association, Emotion, and Motivation Effects of Mood on Memory and Visual Processing MODELING MOTOR CONTROL PARADIGMS Introduction: The Ecological Nature of Motor Control The Robotic Perspective The Biological Perspective The Role of Cerebellum in the Coordination of Multiple Joints Controlling Unstable Plants Motor Learning Paradigms COMPUTATIONAL MODELS FOR GENERIC CORTICAL MICROCIRCUITS Introduction A Conceptual Framework for Real-Time Neural Computation The Generic Neural Microcircuit Model Towards a Non-Turing theory for Real-Time Neural Computation A Generic Neural Microcircuit on the Computational Test Stand Temporal integration and Kernel Function of Neural Microcircuit Models Software for Evaluating the Computational Capabilities of Neural Microcircuit Models Discussion MODELING PRIMATE VISUAL ATTENTION Introduction Brain Areas Bottom-Up Control Top-Down Modulation of Early Vision Top-Down Deployment of Attention Attention and Scene Understanding Discussion

Depression uncouples brain hate circuit

It is increasingly recognized that we need a better understanding of how mental disorders such as depression alter the brains functional connections to improve both early diagnosis and therapy. A new holistic approach has been used to investigate functional connectivity changes in the brains of patients suffering from major depression using resting-state functional magnetic resonance imaging (fMRI) data. A canonical template of connectivity in 90 different brain regions was constructed from healthy control subjects and this identified a six-community structure with each network corresponding to a different functional system. This template was compared with functional networks derived from fMRI scans of both first-episode and longer-term, drug resistant, patients suffering from severe depression. The greatest change in both groups of depressed patients was uncoupling of the so-called ‘hate circuit’ involving the superior frontal gyrus, insula and putamen. Other major changes occurred in circuits related to risk and action responses, reward and emotion, attention and memory processing. A voxel-based morphometry analysis was also carried out but this revealed no evidence in the depressed patients for altered gray or white matter densities in the regions showing altered functional connectivity. This is the first evidence for the involvement of the ‘hate circuit’ in depression and suggests a potential reappraisal of the key neural circuitry involved. We have hypothesized that this may reflect reduced cognitive control over negative feelings toward both self and others.

Emergent Synchronous Bursting of Oxytocin Neuronal Network

When young suckle, they are rewarded intermittently with a let-down of milk that results from reflex secretion of the hormone oxytocin; without oxytocin, newly born young will die unless they are fostered. Oxytocin is made by magnocellular hypothalamic neurons, and is secreted from their nerve endings in the pituitary in response to action potentials (spikes) that are generated in the cell bodies and which are propagated down their axons to the nerve endings. Normally, oxytocin cells discharge asynchronously at 1–3 spikes/s, but during suckling, every 5 min or so, each discharges a brief, intense burst of spikes that release a pulse of oxytocin into the circulation. This reflex was the first, and is perhaps the best, example of a physiological role for peptide-mediated communication within the brain: it is coordinated by the release of oxytocin from the dendrites of oxytocin cells; it can be facilitated by injection of tiny amounts of oxytocin into the hypothalamus, and it can be blocked by injection of tiny amounts of oxytocin antagonist. Here we show how synchronized bursting can arise in a neuronal network model that incorporates basic observations of the physiology of oxytocin cells. In our model, bursting is an emergent behaviour of a complex system, involving both positive and negative feedbacks, between many sparsely connected cells. The oxytocin cells are regulated by independent afferent inputs, but they interact by local release of oxytocin and endocannabinoids. Oxytocin released from the dendrites of these cells has a positive-feedback effect, while endocannabinoids have an inhibitory effect by suppressing the afferent input to the cells.

Granger causality vs. dynamic Bayesian network inference: a comparative study

BackgroundIn computational biology, one often faces the problem of deriving the causal relationship among different elements such as genes, proteins, metabolites, neurons and so on, based upon multi-dimensional temporal data. Currently, there are two common approaches used to explore the network structure among elements. One is the Granger causality approach, and the other is the dynamic Bayesian network inference approach. Both have at least a few thousand publications reported in the literature. A key issue is to choose which approach is used to tackle the data, in particular when they give rise to contradictory results.ResultsIn this paper, we provide an answer by focusing on a systematic and computationally intensive comparison between the two approaches on both synthesized and experimental data. For synthesized data, a critical point of the data length is found: the dynamic Bayesian network outperforms the Granger causality approach when the data length is short, and vice versa. We then test our results in experimental data of short length which is a common scenario in current biological experiments: it is again confirmed that the dynamic Bayesian network works better.ConclusionWhen the data size is short, the dynamic Bayesian network inference performs better than the Granger causality approach; otherwise the Granger causality approach is better.

Detecting time-dependent coherence between non-stationary electrophysiological signals--a combined statistical and time-frequency approach.

Various time-frequency methods have been used to study time-varying properties of non-stationary neurophysiological signals. In the present study, a time-frequency coherence estimate using continuous wavelet transform (CWT) together with its confidence intervals are proposed to evaluate the correlation between two non-stationary processes. The approach is based on averaging over repeat trials. A systematic comparison between approaches using CWT and short-time Fourier transform (STFT) is carried out. Simulated data are generated to test the performance of these methods when estimating time-frequency based coherence. In contrast to some recent studies, we find that CWT based coherence estimates do not supersede STFT based estimates. We suggest that a combination of STFT and CWT would be most suitable for analysing non-stationary neural data. Tests are presented to investigate the time and frequency discrimination capabilities of the two approaches. The methods are applied to two experimental data sets: electroencephalogram (EEG) and surface electromyogram (EMG) during wrist movements in a healthy subject, and local field potential (LFP) and surface EMG recordings during resting tremor in a Parkinsonian patient. Supporting software is available at and .

Voxel Selection in fMRI Data Analysis Based on Sparse Representation

Multivariate pattern analysis approaches toward detection of brain regions from fMRI data have been gaining attention recently. In this study, we introduce an iterative sparse-representation-based algorithm for detection of voxels in functional MRI (fMRI) data with task relevant information. In each iteration of the algorithm, a linear programming problem is solved and a sparse weight vector is subsequently obtained. The final weight vector is the mean of those obtained in all iterations. The characteristics of our algorithm are as follows: 1) the weight vector (output) is sparse; 2) the magnitude of each entry of the weight vector represents the significance of its corresponding variable or feature in a classification or regression problem; and 3) due to the convergence of this algorithm, a stable weight vector is obtained. To demonstrate the validity of our algorithm and illustrate its application, we apply the algorithm to the Pittsburgh Brain Activity Interpretation Competition 2007 functional fMRI dataset for selecting the voxels, which are the most relevant to the tasks of the subjects. Based on this dataset, the aforementioned characteristics of our algorithm are analyzed, and a comparison between our method with the univariate general-linear-model-based statistical parametric mapping is performed. Using our method, a combination of voxels are selected based on the principle of effective/sparse representation of a task. Data analysis results in this paper show that this combination of voxels is suitable for decoding tasks and demonstrate the effectiveness of our method.

Impact of Correlated Inputs on the Output of the Integrate-and-Fire Model

For the integrate-and-fire model with or without reversal potentials, we consider how correlated inputs affect the variability of cellular output. For both models, the variability of efferent spike trains measured by coefficient of variation (CV) of the interspike interval is a nondecreasing function of input correlation. When the correlation coefficient is greater than 0.09, the CV of the integrate-and-fire model without reversal potentials is always above 0.5, no matter how strong the inhibitory inputs. When the correlation coefficient is greater than 0.05, CV for the integrate- and-fire model with reversal potentials is always above 0.5, independent of the strength of the inhibitory inputs. Under a given condition on correlation coefficients, we find that correlated Poisson processes can be decomposed into independent Poisson processes. We also develop a novel method to estimate the distribution density of the first passage time of the integrate-and-fire model.

Autism : reduced connectivity between cortical areas involved in face expression, theory of mind, and the sense of self

Cheng, Rolls et al. examine whole-brain voxel-based resting-state functional connectivity in 418 people with autism. They reveal reduced connectivity between regions involved in facial expression processing and theory of mind (middle temporal gyrus), emotion processing (ventromedial prefrontal cortex), and the representation of self (precuneus and related posterior cingulate areas).

Uncovering interactions in the frequency domain

Oscillatory activity plays a critical role in regulating biological processes at levels ranging from subcellular, cellular, and network to the whole organism, and often involves a large number of interacting elements. We shed light on this issue by introducing a novel approach called partial Granger causality to reliably reveal interaction patterns in multivariate data with exogenous inputs and latent variables in the frequency domain. The method is extensively tested with toy models, and successfully applied to experimental datasets, including (1) gene microarray data of HeLa cell cycle; (2) in vivo multi-electrode array (MEA) local field potentials (LFPs) recorded from the inferotemporal cortex of a sheep; and (3) in vivo LFPs recorded from distributed sites in the right hemisphere of a macaque monkey.

Is the integrate-and-fire model good enough?—a review

We review some recent results on the behaviour of the integrate-and-fire (IF) model, the FitzHugh-Nagumo (FHN) model, a simplified version of the FHN (IF-FHN) model and the Hodgkin-Huxley (HH) model with correlated inputs. The effect of inhibitory inputs on the model behaviour is also taken into account. Here, inputs exclusively take the form of diffusion approximation and correlated inputs mean correlated synaptic inputs (Sections 2 and 3). It is found that the IF and HH models respond to correlated inputs in totally opposite ways, but the IF-FHN model shows similar behaviour to the HH model. Increasing inhibitory input to single neuronal models, such as the FHN model and the HH model can sometimes increase their firing rates, which we termed inhibition-boosted firing (IBF). Using the IF model and the IF-FHN model, we theoretically explore how and when IBF can happen. The computational complexity of the IF-FHN model is very similar to the conventional IF model, but the former captures some interesting and essential features of biophysical models and could serve as a better model for spiking neuron computation.