Evaluation of a shape-based model of human face discrimination using FMRI and behavioral techniques

Understanding the neural mechanisms underlying object recognition is one of the fundamental challenges of visual neuroscience. While neurophysiology experiments have provided evidence for a "simple-to-complex" processing model based on a hierarchy of increasingly complex image features, behavioral and fMRI studies of face processing have been interpreted as incompatible with this account. We present a neurophysiologically plausible, feature-based model that quantitatively accounts for face discrimination characteristics, including face inversion and "configural" effects. The model predicts that face discrimination is based on a sparse representation of units selective for face shapes, without the need to postulate additional, "face-specific" mechanisms. We derive and test predictions that quantitatively link model FFA face neuron tuning, neural adaptation measured in an fMRI rapid adaptation paradigm, and face discrimination performance. The experimental data are in excellent agreement with the model prediction that discrimination performance should asymptote as faces become dissimilar enough to activate different neuronal populations.     

Learning receptive field properties of complex cells in V1

There are two distinct classes of cells in the primary visual cortex (V1): simple cells and complex cells. One defining feature of complex cells is their spatial phase invariance; they respond strongly to oriented grating stimuli with a preferred orientation but with a wide range of spatial phases. A classical model of complete spatial phase invariance in complex cells is the energy model, in which the responses are the sum of the squared outputs of two linear spatially phase-shifted filters. However, recent experimental studies have shown that complex cells have a diverse range of spatial phase invariance and only a subset can be characterized by the energy model. While several models have been proposed to explain how complex cells could learn to be selective to orientation but invariant to spatial phase, most existing models overlook many biologically important details. We propose a biologically plausible model for complex cells that learns to pool inputs from simple cells based on the presentation of natural scene stimuli. The model is a three-layer network with rate-based neurons that describes the activities of LGN cells (layer 1), V1 simple cells (layer 2), and V1 complex cells (layer 3). The first two layers implement a recently proposed simple cell model that is biologically plausible and accounts for many experimental phenomena. The neural dynamics of the complex cells is modeled as the integration of simple cells inputs along with response normalization. Connections between LGN and simple cells are learned using Hebbian and anti-Hebbian plasticity. Connections between simple and complex cells are learned using a modified version of the Bienenstock, Cooper, and Munro (BCM) rule. Our results demonstrate that the learning rule can describe a diversity of complex cells, similar to those observed experimentally.     

Flux module decomposition for parameter estimation in a multiple-feedback loop model of biochemical networks

Computer simulation is an important technique to capture the dynamics of biochemical networks. Since few quantitative values are measured in vivo, the values for unmeasured parameters should be estimated so that the simulation agrees with the experimental data. Considering the sparsity and error rates of experimentally measured data, the first thing is not to find a numerically exact and global solution but to explore a variety of the plausible parameter solutions. To find many plausible parameter solutions without any biases, we developed the two-phase search (TPS) method. However, calculation complexity makes it hard for TPS to optimize a large-scale dynamic model. In this study divide-and-conquer methods are used to solve this problem. The flux module decomposition (FMD) is first proposed that separates a complex, large-scale dynamic model into multiple flux modules without deteriorating its basic control architectures. FMD is combined with TPS, named FMD-TPS, to find many plausible parameter solutions for a dynamic model. To demonstrate the feasibility of FMD-TPS, it is applied to the E. coli ammonia assimilation system that consists of multiple-feedback loops. The variability of the solutions is verified by measuring the space distribution of the parameter solution vectors and by defining the binary vectors checking the consistency with biological behaviors. Compared with non-decomposition methods, FMD-TPS efficiently explored a variety of plausible parameter solutions that reproduce the dynamic behaviors in vivo.     

A hierarchical neuronal model for generation and online recognition of birdsongs

The neuronal system underlying learning, generation and recognition of song in birds is one of the best-studied systems in the neurosciences. Here, we use these experimental findings to derive a neurobiologically plausible, dynamic, hierarchical model of birdsong generation and transform it into a functional model of birdsong recognition. The generation model consists of neuronal rate models and includes critical anatomical components like the premotor song-control nucleus HVC (proper name), the premotor nucleus RA (robust nucleus of the arcopallium), and a model of the syringeal and respiratory organs. We use Bayesian inference of this dynamical system to derive a possible mechanism for how birds can efficiently and robustly recognize the songs of their conspecifics in an online fashion. Our results indicate that the specific way birdsong is generated enables a listening bird to robustly and rapidly perceive embedded information at multiple time scales of a song. The resulting mechanism can be useful for investigating the functional roles of auditory recognition areas and providing predictions for future birdsong experiments.     

3D model of human arylamine N-acetyltransferase 2: structural basis of the slow acetylator phenotype of the R64Q variant and analysis of the active-site loop

The human arylamine N-acetyltransferase NAT2 is responsible for the biotransformation of numerous arylamine drugs and carcinogens. A common polymorphism of the NAT2 gene has been associated with susceptibility to drug toxicity and various malignancies. In this study, we used the crystal structure of the Salmonella typhimurium NAT (StNAT) to construct a high-quality model of a catalytic N-terminal region of NAT2 (residues 34-131). We show that this region has a similar structure in StNAT and the human isoforms NAT1 and NAT2. Comparison of the structures of these three molecules suggests that NATs have an active-site loop with a conserved structure, which is involved in substrate recognition. Our model is consistent with previous experimental data and provides the first plausible structural basis of the effects of a common genetic polymorphism (Arg(64)-->Gln) on NAT2 activity.     

A model of the relation of tonotopy and synchronization in the auditory system

Tonotopy and synchronization of spike discharges with the stimulus frequency are two modes of coding which are probably not independent. After defining an appropriate process of coding and a measure of temporal correlations between two neighbouring channels, we show that it is possible to build with these two fundamental features of the auditory system, a neurophysiologically plausible neural network which has properties of noise resistance and signal segmentation.     

Inheritance of the replication complex: a unique or common phenomenon in the control of DNA replication?

Early models of the regulation of initiation of DNA replication by protein complexes predicted that binding of a replication initiator protein to a replicator region is required for initiation of each DNA replication round, since after the initiation event the replication initiator should dissociate from DNA. It was, therefore, assumed that binding of the replication initiator is a signal for triggering DNA replication. However, more recent investigations have revealed that in many replicons this is not the case. Studies on the regulation of the replication of plasmids derived from bacteriophage lambda demonstrated that, once assembled, the replication complex can be inherited by one of the two daughter plasmid copies after each replication round and may function in subsequent replication rounds. Since this DNA-bound protein complex bears information about specific initiation of DNA replication, this phenomenon has been called "protein inheritance." A similar phenomenon has recently been reported for oriJ-based plasmids. Moreover, the current model of the initiation of DNA replication in the yeast Saccharomyces cerevisiae proposes that the origin recognition complex (ORC) remains bound to one copy of the ori sequence (the ARS region) after initiation of DNA replication. Thus, it seems plausible that protein inheritance is not unique for lambda plasmids, but may be a common phenomenon in the control of DNA replication, at least in microbes.     

A Bayesian Developmental Approach to Robotic Goal-Based Imitation Learning

A fundamental challenge in robotics today is building robots that can learn new skills by observing humans and imitating human actions. We propose a new Bayesian approach to robotic learning by imitation inspired by the developmental hypothesis that children use self-experience to bootstrap the process of intention recognition and goal-based imitation. Our approach allows an autonomous agent to: (i) learn probabilistic models of actions through self-discovery and experience, (ii) utilize these learned models for inferring the goals of human actions, and (iii) perform goal-based imitation for robotic learning and human-robot collaboration. Such an approach allows a robot to leverage its increasing repertoire of learned behaviors to interpret increasingly complex human actions and use the inferred goals for imitation, even when the robot has very different actuators from humans. We demonstrate our approach using two different scenarios: (i) a simulated robot that learns human-like gaze following behavior, and (ii) a robot that learns to imitate human actions in a tabletop organization task. In both cases, the agent learns a probabilistic model of its own actions, and uses this model for goal inference and goal-based imitation. We also show that the robotic agent can use its probabilistic model to seek human assistance when it recognizes that its inferred actions are too uncertain, risky, or impossible to perform, thereby opening the door to human-robot collaboration.     

Evidence for a substrate-binding subsite in ribonuclease T1. Crystal structure of the complex with two guanosines, and model building of the complex with the substrate guanylyl-3',5'-guanosine

The enzyme ribonuclease T1 cleaves single-stranded RNA at the 3'-side of guanosine. The structure of the complex with two guanosines has been analyzed at 1.8-A resolution and refined to a crystallographic R value of 14.0%. One guanosine occupies the guanosine recognition site as observed in previously analyzed complexes of ribonuclease T1 with guanosine phosphates. The other is bound to a base-unspecific subsite marking the binding locus of the nucleoside 3'-proximal to guanosine in a cleavable RNA chain. The positions of the guanosine bound to the recognition site and of the guanine base at the subsite were used to guide model building of the substrate guanylyl-3',5'-guanosine bound to the active site of ribonuclease T1. After energy minimization and a 7-ps stochastic dynamics simulation, a plausible model of the enzyme-substrate complex was obtained which may serve as a reference point in consideration of the mechanisms of RNA hydrolysis by ribonuclease T1.     

Two models of the recognition and detection of texture-defined letters compared

We quantified texture segregation by measuring psychophysically the percentage correct detection scores for each of a set of 10 texture-defined (TD) letters using the temporal two-alternative forced choice method, and at the same time quantified spatial discrimination of the TD form of measuring psychophysically the percentage correct letter recognition scores for the 10 letters. Ten levels of task difficulty were created by adding noise dots to the texture patterns. The resulting psychophysical data were used to test and compare models of the detection and recognition of texture-defined letters. Each model comprised a sequence of physiologically plausible stages in early visual processing. Each had the same first, second and third stages, namely linear orientation-tuned spatial filters followed by rectification and smoothing. Model 1 had only one non-linear stage. Model 2 had two non-linear stages. In model 2 the second non-linear stage was cross-orientation inhibition. This second non-linear stage enhanced the texture borders by, in effect, comparing textures at different locations in the texture pattern. In both models, the last stage modelled either letter detection or letter recognition. Letter recognition was modelled as follows. We passed a given letter stimulus through the first several stages of a model and, in 10 separate calculations, cross-correlated the output with a template of each of the 10 letters. From these 10 correlations we obtained a predicted percentage correct letter recognition score for the given letter stimulus. The predicted recognition scores closely agreed with the experimental data at all 10 levels of task difficulty for model 2, but not for model 1. We conclude that a borderenhancing algorithm is necessary to model letter recognition. The letter-detection algorithm modelled detection of part of a letter (a single letter stroke) in terms of the signal-to-noise ratio of a letter-segment detector. The predicted letter detection scores fitted the data closely for both models.     

Classification using sparse representations: a biologically plausible approach

Representing signals as linear combinations of basis vectors sparsely selected from an overcomplete dictionary has proven to be advantageous for many applications in pattern recognition, machine learning, signal processing, and computer vision. While this approach was originally inspired by insights into cortical information processing, biologically plausible approaches have been limited to exploring the functionality of early sensory processing in the brain, while more practical applications have employed non-biologically plausible sparse coding algorithms. Here, a biologically plausible algorithm is proposed that can be applied to practical problems. This algorithm is evaluated using standard benchmark tasks in the domain of pattern classification, and its performance is compared to a wide range of alternative algorithms that are widely used in signal and image processing. The results show that for the classification tasks performed here, the proposed method is competitive with the best of the alternative algorithms that have been evaluated. This demonstrates that classification using sparse representations can be performed in a neurally plausible manner, and hence, that this mechanism of classification might be exploited by the brain.     

Shear-driven rolling of DNA-adhesive microspheres

Many biologically important cell binding processes, such as the rolling of leukocytes in the vasculature, are multivalent, being mediated by large numbers of weak binding ligands. Quantitative agreement between experiments and models of rolling has been elusive and often limited by the poor understanding of the binding and unbinding kinetics of the ligands involved. Here, we present a cell-free experimental model for such rolling, consisting of polymer microspheres whose adhesion to a glass surface is mediated by ligands with well-understood force-dependent binding free energy—short complementary DNA strands. We observe robust rolling activity for certain values of the shear rate and the grafted DNA strands’ binding free energy and force sensitivity. The simulation framework developed to model leukocyte rolling, adhesive dynamics, quantitatively captures the mean rolling velocity and lateral diffusivity of the experimental particles using known values of the experimental parameters. Moreover, our model captures the velocity variations seen within the trajectories of single particles. Particle-to-particle variations can be attributed to small, plausible differences in particle characteristics. Overall, our findings confirm that state-of-the-art adhesive dynamics simulations are able to capture the complex physics of particle rolling, boding well for their extension to modeling more complex systems of rolling cells.     

A stalled retrotranslocation complex reveals physical linkage between substrate recognition and proteasomal degradation during ER-associated degradation

During endoplasmic reticulum–associated degradation (ERAD), misfolded lumenal and membrane proteins in the ER are recognized by the transmembrane Hrd1 ubiquitin ligase complex and retrotranslocated to the cytosol for ubiquitination and degradation. Although substrates are believed to be delivered to the proteasome only after the ATPase Cdc48p/p97 acts, there is limited knowledge about how the Hrd1 complex coordinates with Cdc48p/p97 and the proteasome to orchestrate substrate recognition and degradation. Here we provide evidence that inactivation of Cdc48p/p97 stalls retrotranslocation and triggers formation of a complex that contains the 26S proteasome, Cdc48p/p97, ubiquitinated substrates, select components of the Hrd1 complex, and the lumenal recognition factor, Yos9p. We propose that the actions of Cdc48p/p97 and the proteasome are tightly coupled during ERAD. Our data also support a model in which the Hrd1 complex links substrate recognition and degradation on opposite sides of the ER membrane.     

Rapid immune adherence reactivity of nascent, soluble antibody/DNA immune complexes in the circulation

Immune complex disease in humans and experimental animals can occur as a consequence of the binding of specific antibodies to exogenous or endogenous antigens. If this reaction occurs in the circulation, the fate of the resulting immune complex may depend upon many factors including the ability of the immune complex to fix complement and bind to complement receptors on circulating cells (immune adherence). We studied the in vivo formation and immune adherence of soluble antibody/dsDNA immune complexes in the circulation of both a nonprimate and a primate model. The fact that this sequence of biological recognition reactions is completed in less than 2 min suggests that the immune adherence phenomenon may play a crucial role in the clearance of nascent complement-fixing immune complexes from the circulation.     

Recognizing Sequences of Sequences

The brain''s decoding of fast sensory streams is currently impossible to emulate, even approximately, with artificial agents. For example, robust speech recognition is relatively easy for humans but exceptionally difficult for artificial speech recognition systems. In this paper, we propose that recognition can be simplified with an internal model of how sensory input is generated, when formulated in a Bayesian framework. We show that a plausible candidate for an internal or generative model is a hierarchy of ‘stable heteroclinic channels’. This model describes continuous dynamics in the environment as a hierarchy of sequences, where slower sequences cause faster sequences. Under this model, online recognition corresponds to the dynamic decoding of causal sequences, giving a representation of the environment with predictive power on several timescales. We illustrate the ensuing decoding or recognition scheme using synthetic sequences of syllables, where syllables are sequences of phonemes and phonemes are sequences of sound-wave modulations. By presenting anomalous stimuli, we find that the resulting recognition dynamics disclose inference at multiple time scales and are reminiscent of neuronal dynamics seen in the real brain.     

A Network Model of Motion Processing in Area MT of Primates

A simple and biologically plausible model is proposed to simulatethe visual motion processing taking place in the middle temporal (MT) areaof the visual cortex in the primate brain. The model is ahierarchical neural network composed of multiple competitive learninglayers. The input layer of the network simulates the neurons in the primaryvisual cortex (V1), which are sensitive to the orientation and motionvelocity of the visual stimuli, and the middle and output layers of thenetwork simulate the component MT and pattern MT neurons, which areselectively responsive to local and global motions, respectively. Thenetwork model was tested with various simulated motion patterns (random dotsof different direction correlations, transparent motion, grating and plaidpatterns, and so on). The response properties of the model closely resemblemany of the known features of the MT neurons found neurophysiologically.These results show that the sophisticated response behaviors of the MTneurons can emerge naturally from some very simple models, such as acompetitive learning network.     

Bursts shape the NMDA-R mediated spike timing dependent plasticity curve: role of burst interspike interval and GABAergic inhibition

Spike timing dependent plasticity (STDP) is a synaptic learning rule where the relative timing between the presynaptic and postsynaptic action potentials determines the sign and strength of synaptic plasticity. In its basic form STDP has an asymmetric form which incorporates both persistent increases and persistent decreases in synaptic strength. The basic form of STDP, however, is not a fixed property and depends on the dendritic location. An asymmetric curve is observed in the distal dendrites, whereas a symmetrical one is observed in the proximal ones. A recent computational study has shown that the transition from the asymmetry to symmetry is due to inhibition under certain conditions. Synapses have also been observed to be unreliable at generating plasticity when excitatory postsynaptic potentials and single spikes are paired at low frequencies. Bursts of spikes, however, are reliably signaled because transmitter release is facilitated. This article presents a two-compartment model of the CA1 pyramidal cell. The model is neurophysiologically plausible with its dynamics resulting from the interplay of many ionic and synaptic currents. Plasticity is measured by a deterministic Ca²⁺ dynamics model which measures the instantaneous calcium level and its time course in the dendrite and change the strength of the synapse accordingly. The model is validated to match the asymmetrical form of STDP from the pairing of a presynaptic (dendritic) and postsynaptic (somatic) spikes as observed experimentally. With the parameter set unchanged the model investigates how pairing of bursts with single spikes and bursts in the presence or absence of inhibition shapes the STDP curve. The model predicts that inhibition strength and frequency are not the only factors of the asymmetry-to-symmetry switch of the STDP curve. Burst interspike interval is another factor. This study is an important first step towards understanding how STDP is affected under natural firing patterns in vivo.     

Global analysis of protein-protein interactions reveals multiple CYP2E1-reductase complexes

Although a single binary functional complex between cytochrome P450 (P450 or CYP for a specific isoform) and cytochrome P450 reductase (CPR) has been generally accepted in the literature, this simple model failed to explain the experimentally observed catalytic activity of recombinant CYP2E1 in dependence on the total concentration of the added CPR-K56Q mutant. Our rejection of the simplest 1:1 binding model was based on two independent lines of experimental evidence. First, under the assumption of the 1:1 binding model, separate analyses of titration curves obtained while varying either P450 or CPR concentrations individually produced contradictory results. Second, an asymmetric Job plot suggested the existence of higher order molecular complexes. To identify the most probable complexation mechanism, we generated a comprehensive data set where the concentrations of both P450 and P450 were varied simultaneously, rather than one at a time. The resulting two-dimensional data were globally fit to 32 candidate mechanistic models, involving the formation of binary, ternary, and quaternary P450.CPR complexes, in the absence or presence or P450 and CPR homodimers. Of the 32 candidate models (mechanisms), two models were approximately equally successful in explaining our experimental data. The first plausible model involves the binary complex P450.CPR, the quaternary complex (P450)2.(CPR)2, and the homodimer (P450)2. The second plausible model additionally involves a weakly bound ternary complex (P450)2.CPR. Importantly, only the binary complex P450.CPR seems catalytically active in either of the two most probable mechanisms.     

A data-informed mean-field approach to mapping of cortical parameter landscapes

Constraining the many biological parameters that govern cortical dynamics is computationally and conceptually difficult because of the curse of dimensionality. This paper addresses these challenges by proposing (1) a novel data-informed mean-field (MF) approach to efficiently map the parameter space of network models; and (2) an organizing principle for studying parameter space that enables the extraction biologically meaningful relations from this high-dimensional data. We illustrate these ideas using a large-scale network model of the Macaque primary visual cortex. Of the 10-20 model parameters, we identify 7 that are especially poorly constrained, and use the MF algorithm in (1) to discover the firing rate contours in this 7D parameter cube. Defining a “biologically plausible” region to consist of parameters that exhibit spontaneous Excitatory and Inhibitory firing rates compatible with experimental values, we find that this region is a slightly thickened codimension-1 submanifold. An implication of this finding is that while plausible regimes depend sensitively on parameters, they are also robust and flexible provided one compensates appropriately when parameters are varied. Our organizing principle for conceptualizing parameter dependence is to focus on certain 2D parameter planes that govern lateral inhibition: Intersecting these planes with the biologically plausible region leads to very simple geometric structures which, when suitably scaled, have a universal character independent of where the intersections are taken. In addition to elucidating the geometry of the plausible region, this invariance suggests useful approximate scaling relations. Our study offers, for the first time, a complete characterization of the set of all biologically plausible parameters for a detailed cortical model, which has been out of reach due to the high dimensionality of parameter space.