Plausible Network Model Research Articles

Characterization of minimal network structures modeling stochastic processes

Identifying the underlying structure of a network from observed data is an important problem across various disciplines. Given the general ill-posed nature of the problem, since in many cases, multiple plausible network models can explain the data, this article concentrates on characterizing classes of models providing possible explanations. Specifically, we explore linear models that can account for observed data in the form of wide-sense stationary processes accommodating the potential presence of feedback loops and direct feedthroughs. To achieve this, we leverage key insights from the theory of graphical models. In particular, we extensively employ Pearl-Verma Theorem in causal discovery which allows one to recover all minimal network structures compatible with the observed data. We adapt such a result to deal with stochastic processes and reinterpret it as a Gram-Schmidt orthogonalization procedure in a suitable Hilbert space. This reinterpretation allows us to characterize all minimal networks explaining a set of data, which have the property of not having any algebraic loops.

Network configurations of pain: an efficiency characterization of information transmission

Recent studies have shown that gamma-band oscillations are directly related to pain intensity. Pain can be exacerbated or diminished via deactivation or activation of inhibitory interneurons in the dorsal horn. We consider a biologically plausible network model with different proportion of inhibitory neurons to emulate gamma elicited activity during pain processes. We perform an analysis using graph theory to gain further insight in the functional state of the circuitry underlying nociceptive process, considering all the possible gamma elicited configurations of pain when changing the number of inhibitory neurons. The probability distribution of the signal associated with each node or neuron is estimated through the Bandt and Pompe approach. We evaluate the Jensen–Shannon distance between all the possible pairs of nodes/neurons, characterizing the different network configurations by calculating the closeness centrality. Thus, by building the graph properties through the node strength distributions and using an information theoretical approach, we characterize the dynamics of the network configurations of pain. This allows us to identify the nonlinear dynamical structure underlying the nociceptive process. Importantly, our findings show that a network configuration with a $$20\%$$ of inhibitory neurons boosts information transmission of the complex network circuitry associated with the pain processing.

Optimal Robust Search for Parameter Values of Qualitative Models of Gene Regulatory Networks.

Computational and mathematical models are a must for the in silico analysis or design of Gene Regulatory Networks (GRN)as they offer a theoretical context to deeply address biological regulation. We have proposed a framework where models of network dynamics are expressed through a class of nonlinear and temporal multiscale Ordinary Differential Equations (ODE). To find out models that disclose network structures underlying an observed or desired network behavior, and parameter values that enable the candidate models to reproduce such behavior, we follow a reasoning cycle that alternates procedures for model selection and parameter refinement. Plausible network models are first selected via qualitative simulation, and next their parameters are given quantitative values such that the ODE model solution reproduces the specified behavior. This paper gives algorithms to tackle the parameter refinement problem formulated as an optimization problem. We search, within the parameter space symbolically expressed, for the largest hypersphere whose points correspond to parameter values such that the ODE solution gives an instance of the given qualitative trajectory. Our approach overcomes the limitation of a previously proposed stochastic approach, namely computational load and very reduced scalability. Its applicability and effectiveness are demonstrated through two benchmark synthetic networks with different complexity.

A biologically plausible network model for pattern storage and recall inspired by Dentate Gyrus

In the race to achieve better performance, artificial intelligence has become more about the end rather than the means, which is general intelligence. This work aims to bridge the gap between the two by finding a complementary midline. The objective of this work is to project the role of Dentate Gyrus in enhancing the performance of an autoassociative network, paving the way to develop a biologically plausible neural network which, in the future, would help in simulating the network present in our brain. The proposed network imbibes biological similarities with respect to connectivity, weight updation, and activation function. Dentate Gyrus uses pre-integration lateral inhibition form of learning, and the autoassociative network is implemented using Hopfield network. The performance of the autoassociative network in the presence and absence of Dentate Gyrus is observed across multiple parameters. The results show an increase of 38% in storage capacity and a decrease of 15% in the error tolerance capability of the network in the presence of Dentate Gyrus.

Selective Prefrontal Disinhibition in a Roving Auditory Oddball Paradigm Under N-Methyl-D-Aspartate Receptor Blockade

BackgroundDisturbances in N-methyl-D-aspartate receptors (NMDARs)—as implicated in patients with schizophrenia—can cause regionally specific electrophysiological effects. Both animal models of NMDAR blockade and clinical studies in patients with schizophrenia have suggested that behavioral phenotypes are associated with reduction in inhibition within the frontal cortex. MethodsHere we investigate event-related potentials to a roving auditory oddball paradigm under ketamine in healthy human volunteers (N= 18; double-blind, placebo-controlled, crossover design). Using recent advances in Bayesian modeling of group effects in dynamic causal modeling, we fit biophysically plausible network models of the auditory processing hierarchy to whole-scalp event-related potential recordings. This allowed us to identify regionally specific effects of ketamine in a distributed network of interacting cortical sources. ResultsWe show that the effect of ketamine is best explained as a selective change in intrinsic inhibition, with a pronounced ketamine-induced reduction of inhibitory interneuron connectivity in frontal sources, compared with temporal sources. Simulations of these changes in an integrated microcircuit model shows that they are associated with a reduction in superficial pyramidal cell activity that can explain drug effects observed in the event-related potential. ConclusionsThese results are consistent with findings from invasive recordings in animal models exposed to NMDAR blockers, and provide evidence that inhibitory interneuron–specific NMDAR dysfunction may be sufficient to explain electrophysiological abnormalities induced by NMDAR blockade in human subjects.

Network interactions underlying mirror feedback in stroke: A dynamic causal modeling study.

Mirror visual feedback (MVF) is potentially a powerful tool to facilitate recovery of disordered movement and stimulate activation of under-active brain areas due to stroke. The neural mechanisms underlying MVF have therefore been a focus of recent inquiry. Although it is known that sensorimotor areas can be activated via mirror feedback, the network interactions driving this effect remain unknown. The aim of the current study was to fill this gap by using dynamic causal modeling to test the interactions between regions in the frontal and parietal lobes that may be important for modulating the activation of the ipsilesional motor cortex during mirror visual feedback of unaffected hand movement in stroke patients. Our intent was to distinguish between two theoretical neural mechanisms that might mediate ipsilateral activation in response to mirror-feedback: transfer of information between bilateral motor cortices versus recruitment of regions comprising an action observation network which in turn modulate the motor cortex. In an event-related fMRI design, fourteen chronic stroke subjects performed goal-directed finger flexion movements with their unaffected hand while observing real-time visual feedback of the corresponding (veridical) or opposite (mirror) hand in virtual reality. Among 30 plausible network models that were tested, the winning model revealed significant mirror feedback-based modulation of the ipsilesional motor cortex arising from the contralesional parietal cortex, in a region along the rostral extent of the intraparietal sulcus. No winning model was identified for the veridical feedback condition. We discuss our findings in the context of supporting the latter hypothesis, that mirror feedback-based activation of motor cortex may be attributed to engagement of a contralateral (contralesional) action observation network. These findings may have important implications for identifying putative cortical areas, which may be targeted with non-invasive brain stimulation as a means of potentiating the effects of mirror training.

Stimulus discrimination and association with Hebbian cell assemblies

Learning or the formation of memories is a key component of biological intelligence. One distinguishes between different types of memory as, for instance, the declarative memory denoting the storage of facts and concepts which are extracted from perceived stimuli. Thereby, an important question is how the brain distinguishes between stimuli that should be associated and those to be discriminated. Investigating this mechanism, in psychophysical experiments, rats were exposed to gradually morphed environments while recording activity in hippocampal place cells [1,2]. Stability of the recorded place-dependent activation patterns was dependent on the order in which the different environments were explored. In other words, stimulus discrimination or association depends on the order of presentation. Theoretical models of memory rely on the hypothesis that memorization is implemented through activity-dependent synaptic plasticity. Environmental patterns are reflected by correlated activity in subgroups of neurons leading to high recurrent synaptic efficacies among them. These groups of highly interconnected neurons are named cell assemblies (CAs). This long-standing concept with its basic ability of pattern completion is a promising approach to explain several cognitive phenomena [3]. Combining the theory of CAs with the principle of self-organizing maps (SOMs) [4], we present a biologically plausible network model able to reproduce the upper experimental findings. Basically our model consists of three populations of neurons. An input population projects through random excitatory connections on a recurrent layer. Neurons within the layer interact by excitatory synapses. Competition is introduced by an inhibitory population mutually interconnected with every layer neuron. For all neurons, we use a rate-coded neuron model with sigmoidal transfer functions. The plasticity model combines (Hebbian) synaptic plasticity and synaptic scaling (SPASS) [5]. Stimuli are inserted as activity patterns in the input population passing them on via afferent plastic synapses. Recurrent lateral connections in the layer reach a circular neighborhood of fixed range and are also plastic. The incoming excitatory synapses the inhibitory population receives as well as its outgoing connections are non-plastic. In agreement with SOM, our concept maps sufficiently differing stimuli to different representations (CAs) in the layer. Comparable to the experimental procedures described above, we define two non-overlapping activity patterns A and B. According with [1], first we transform stimulus A gradually into B with a short pause between each step of the transition sequence. After A lead to the formation of one memory representation, or rather a single CA, this CA is maintained throughout the following learning protocol. Secondly, the protocol of [2] demands a random presentation of the transition patterns. In this case, we observe the formation of multiple representations. Thus, our model reproduces the experimental findings described above. In contrast to existing theoretical studies [6], it does not require stimulus-dependent modifications of the plasticity rule itself. It further allows for investigating the influence of different variations in the learning protocol as, for instance, of the stimulus presentation time.

A meta-analysis of centrality measures for comparing and generating complex network models

Complex networks are often characterized by their statistical and topological network properties such as degree distribution, average path length, and clustering coefficient. However, many more characteristics can also be considered such as graph similarity, centrality, or flow properties. These properties have been utilized as feedback for algorithms whose goal is to ascertain plausible network models (also called generators) for a given network. However, a good set of network measures to employ that can be said to sufficiently capture network structure is not yet known. In this paper we provide an investigation into this question through a meta-analysis that quantifies the ability of a subset of measures to appropriately compare model (dis)similarity. The results are utilized as fitness measures for improving a recently proposed genetic programming (GP) framework that is capable of ascertaining a plausible network model from a single network observation. It is shown that the candidate model evaluation criteria of the GP system to automatically infer existing (man-made) network models, in addition to real-world networks, is improved.

A large scale simulation of excitation propagation in layer 2/3 of primary and secondary visual cortices of mice.

Analyzing network architecture and spatio-temporal dynamics of the visual cortical areas can facilitate understanding visual information processing in the brain. Recently, several physiological experiments utilizing the fast in-vivo imaging technique have demonstrated that the primary visual cortex (V1) and the secondary visual cortex (V2) in mice exhibit complex properties of the responses to visual and electrical stimuli. In order to provide a tool for quantitatively analyzing such a complex dynamics of the cortices at the level of neurons and circuits, here, we constructed a physiologically plausible large-scale network model of the layers 2/3 of V1 and V2, composed of 14,056 multi-compartment neuron models. The Message-Passing-Interface-based parallel simulations of our network model were able to reproduce, at least quantitatively, the neural responses experimentally observed in mouse V1 and V2 with the voltage-sensitive dye imaging.

Multistable network dynamics through lateral inhibition: an efficient mechanism for selective information routing

A remarkable feature of the brain is its ability to flexibly process information in a context-dependent manner. A hallmark example is selective visual attention. It allows to focus on relevant information, and to ignore distracting features of a visual scene, effectively routing information through the visual hierarchy [1]. A seminal hypothesis about how this routing is accomplished is 'Communication through Coherence' (CTC) [2]: A neuron with oscillating membrane potential can be driven to fire by a relatively weak input when it is close to the threshold, while if it is in a phase of low excitability, even stronger input might fail to elicit an action potential. Thus, shifting relative phases of oscillating sending and receiving populations could be utilized to gate information. Here, we investigate neural mechanisms of information routing and their effectiveness in a generic, two-layered cortical network model with lateral recurrent interactions. Building on the CTC hypothesis, we construct a biophysically plausible network model mimicking the fan-in structure of the visual processing stream. It comprises four populations A, B, C and D of recurrently coupled networks of conductance-based spiking neurons. A and B correspond to sending populations in a lower visual area and have non-overlapping receptive fields. They project to receiving populations C and D in a higher visual area with larger receptive fields. Upon activation, all populations engage in oscillatory activity. We show that introducing distance-dependent lateral inhibition brings the receiving populations in phase, and the sending populations in anti-phase, thus establishing a phase competition between the two sending populations: Only one population can be in a favorable in-phase relation with the receiving populations, since the other is automatically forced into anti-phase, thus hindering information transfer. This 'self-organization' of phase relations mediated by lateral inhibition distinguishes our approach from previous modeling efforts (e.g. [3-5]). When increasing the strength of the lateral inhibition between A and B, the system transitions from a regime of mixed representations to a bistable one, where one sending population is in a favorable in-phase relation for an extended period of time. In this regime, a brief current pulse to one sending population is sufficient to set a desired phase relation and thus 'switch' information routing in the network. This mechanism bears two interesting consequences: First, it allows neural circuits to control routing using energy-efficient, short bursts of neural activity instead of requiring sustained elevation of firing rates. Second, such pulses delivered by electrical stimulation could be employed to externally direct the focus of attention, a possible application in the field of brain prosthetics.

Exploiting temporal continuity of views to learn visual object invariance.

Exploiting temporal continuity of views to learn visual object invariance.

Mechanisms underlying cortical activity during value-guided choice

When choosing between two options, correlates of their value are represented in neural activity throughout the brain. Whether these representations reflect activity fundamental to the computational process of value comparison, as opposed to other computations covarying with value, is unknown. Here, we investigated activity in a biophysically plausible network model that transforms inputs relating to value into categorical choices. A set of characteristic time-varying signals emerged that reflect value comparison. We tested these model predictions in magnetoencephalography data recorded from human subjects performing value-guided decisions. Parietal and prefrontal signals matched closely with model predictions. These results provide a mechanistic explanation of neural signals recorded during value-guided choice, and a means of distinguishing computational roles of different cortical regions whose activity covaries with value.

New frontiers in the neuroscience of the sense of agency.

The sense that I am the author of my own actions, including the ability to distinguish my own from other people's actions, is a fundamental building block of our sense of self, on the one hand, and successful social interactions, on the other. Using cognitive neuroscience techniques, researchers have attempted to elucidate the functional basis of this intriguing phenomenon, also trying to explain pathological abnormalities of action awareness in certain psychiatric and neurological disturbances. Recent conceptual, technological, and methodological advances suggest several interesting and necessary new leads for future research on the neuroscience of agency. Here I will describe new frontiers for the field such as the need for novel and multifactorial paradigms, anatomically plausible network models for the sense of agency, investigations of the temporal dynamics during agentic processing and ecologically valid virtual reality (VR) applications.

Editorial [Hot Topic: Evolutionary Systems Biology (Guest Editor: Jianying Gu)

Systems biology has a long history as a theoretical framework in biology, positing that cellular systems possess emergent properties that can only be explained by the interactions among the components of an organism rather than by any individual component in itself. With the advent of the genomic era, data sets encompassing systems-wide measurements of gene complements, transcription, and protein expression are becoming available. Thoughtful integration of these data can lead to a systems-level understanding of the components and dynamics of cellular machineries and the development of practical methods for inferring and constructing network models of these interactions is well under way. As the creation of plausible network models of cellular systems becomes possible, comparison of systems in an evolutionary light promises to reveal the evolutionary forces that shape these systems. Systems are dynamic and selectable, that is, they respond to pressures brought to bear by changes in their environment. Therefore, the application of the methods of evolutionary biology within a systems biology framework can aid in identifying the concerted changes that drive the emergence of new phenotypes. A better understanding of systems evolution will greatly advance our knowledge about the complexity of gene regulation and help identify new therapeutic targets for effective treatment of various diseases. This special issue of Current Genomics is intended to provide a forum for researchers to share their experiences on all aspects of genome and systems evolution. This issue includes a review article discussing about the proteomics techniques that are capable of generating high quality data for systems biology research: Chen et al. reviewed recent advances in proteolysis, a key procedure prior to mass spectrometry and peptide mapping in proteomic studies of gene function. They showed that the proteolysis strategies can be accelerated by various electromagnetic waves, such as microwave-accelerated protein digestion, infrared-assisted proteolysis, ultraviolet-enhanced protein digestion, laser-assisted proteolysis, and future prospects. This issue also includes two review articles with a special emphasis on the systems biology of bacteria, ideal model organisms for evolutionary analyses of genome plasticity and adaptive phenotypes. Tang's review is focused on microbial metabolomics, which involves fast, high throughput characterization of the metabolite complements of a microorganism, and thus summarizes the global outcome of the interplay between the developmental processes and the environment. Selected topics illustrate the impact of metabolomics on the understanding of systems microbiology. Zhou et al. provide an overview of the recent advances in Streptomyces biology that have been driven by high throughput technologies. The omics based studies have revolutionized the understanding of system control and regulation dynamics of this group of bacterial of medicinal and industrial importance. Evolutionary systems biology approaches have also been applied to biomedical studies. Cai et al. provide a phylogenomic overview of the origin, distribution, diversity, and evolution of malarial proteases, a class of promising therapeutic targets for combating this devastating infectious disease that is responsible for over 1 million deaths yearly. Klemcke et al. introduce a novel physiogenomic approach to the study of hemorraghic shock, a major cause of death throughout the world. Their findings in heritable quantitative trait loci, as well as potential epigenetic mechanisms that might influence survival time after severe hemorrhage, may lead to the improvement in survival of traumatic hemorrhage and provide knowledge of genetically-informed personalized treatments. Overall, these reviews provide a fascinating survey of the combination of systems thinking and evolutionary analysis across the biological spectrum. As modeling approaches improve and as more data from more species become available, the study of systems evolution will clarify the links among the genotype, the phenotype and the selective pressures arising from the organism’s place in the environment. As an emerging field in the post genomic era, evolutionary systems biology promises to make profound contributions to our understanding of life.

How attention and reinforcers jointly optimize the associations between sensory representations, working memory and motor programs

Almost all animal behaviors can be seen as sequences of actions towards achieving certain goals. How the association cortices learn to link sensory stimuli to a correct sequence of motor responses is not well understood, especially when only a correct sequence of responses is rewarding. We present a biologically plausible neuronal network model that can be trained to perform a large variety of tasks when only stimuli and reward contingencies are varied. The model’s aim is to learn action values in a feedforward neuronal network and we present mechanisms to overcome the structural and temporal credit assignment problems. The temporal credit assignment problem is solved by a form of Q-learning [1]. The structural credit assignment problem is solved by a form of ‘attentional’ feedback from motor cortex to association cortex that delineates the units that should change connectivity to improve behavior [2]. Moreover, the model has a new mechanism to store traces of relevant sensory stimuli in working memory. During learning, the sensory stimuli, in combination with traces of previous stimuli in working memory become associated with a unique set of action values. Learning in the model is biologically realistic as model units have Hebbian plasticity that is gated by two factors [2]. Firstly, reinforcers or increases in reward expectancy cause the global release of neuromodulatory signals that inform all synapses of the network if the outcome of a trial was better or worse than expected [3]. Selective attention is the second factor that gates plasticity. Attentional feedback highlights the chain of neurons between sensory and motor cortex responsible for the selected action. Only neurons that are causally linked to the action receive attentional feedback, and change the strength of their connections. Selective attention thereby solves the structural credit assignment problem. The resulting learning rule is a form of AGREL [2], which was previously shown to be on average equivalent to error-backpropagation in classification tasks with immediate reward. The present generalization of the learning scheme is based on temporal difference learning and it can train multilayer feedforward networks to perform delayed reward tasks with multiple epochs that require multiple behavioral responses. Importantly, the generalization MQ-AGREL learns to store in working memory information that is relevant at a later stage during a task. This memory is maintained by persistent activity of units at the intermediate network layers. We show that MQ-AGREL can be trained in many tasks that are in use in neurophysiology, including (1) (delayed) saccade-antisaccade tasks; (2) categorization tasks; and (3) probabilistic classification tasks. Neurons at intermediate levels of the network acquire visual responses and memory responses as the result of training that resemble the tuning of neurons in association areas of the cerebral cortex of animals that are trained in these same tasks. We conclude that MQ-AGREL is a powerful and biologically realistic learning rule that accounts for learning in delayed reward tasks that involve non-linear mappings from sensory stimuli and working memory onto motor responses.

A large-scale model of the three first stages of the mammalian olfactory system implemented with spiking neurons

In this work we present a large-scale three stage model of the early mammalian olfactory system, including the olfactory epithelium (OE), the olfactory bulb (OB) and the olfactory (piriform) cortex (OC). All neurons in the network are modeled with a single or few compartments using the Hodgkin-Huxley formalism and are implemented in the NEURON simulator for parallel execution [1,2]. We investigate the dynamics of the network response to odorants and its performance in odor classification experiments. The OE model comprises families of olfactory receptor neurons (ORNs) with different sensitivities, each family expressing one type of olfactory receptor (OR) with a vector of affinity values for each ligand [3]. These different ORN families connect to distinct glomeruli and mitral cells (MT) according to a hypothesized wiring scheme to form a fuzzy interval code for odorant concentration in the OB, i.e. each MT cell responds within a certain range of odorant concentration and these ranges overlap for different MT cells within one glomerulus. Mitral cells in different glomeruli respond independently to an odourant forming a sparse and distributed code in the OB, i.e. only a fraction of MT cells in different glomeruli is active when an odorant is present. The OC is modeled by a modular attractor network of pyramidal cells and inhibitory interneurons [4]. The OB response patterns are used to self-organize a projection to the OC based on learning algorithms employing ideas from machine learning (mutual information between the MT responses, multi-dimensional scaling, vector quantization and Hebbian learning) [5,6]. As a result, odourant responses are represented by a sparse distributed code in the OC. Results from runs with network sizes comprising thousands of model neurons show that this biophysically plausible network model generates response patterns of cells reminding of their real counterparts (see Figure ​Figure1),1), produces attractor dynamics in the olfactory cortex, and is able to discriminate between the different trained odors. We investigate effects of the model size, backprojections from OC to OB, study the performance of the model in discriminating mixtures of odorants and compare Calcium concentration in the olfactory cortex with experimental measurements [7]. Figure 1 Schematic of the model (A) and sample membrane potentials of a pyramidal cell (B), a mitral cell (C) and an olfactory receptor neuron (D)

Cross-correlation analysis reveals circuits and mechanisms underlying direction selectivity.

Event Abstract Back to Event Cross-correlation analysis reveals circuits and mechanisms underlying direction selectivity. Pamela M. Baker1* and Wyeth Bair1 1 University of Oxford, Department of Physiology, Anatomy and Genetics, United Kingdom Cross-correlation of spike trains from pairs of neurons has proved valuable for investigating functional connectivity in the visual system. It has revealed the specific functional connections between the lateral geniculate nucleus (LGN) and simple cells in primary visual cortex (V1) [1], and exposed properties of simple cells that drive complex cells [2]. Direction selectivity is a fundamental physiological property that arises from V1 circuitry yet basic questions of how direction selective (DS) receptive fields are constructed remain unanswered. To guide experiments for uncovering cortical mechanisms of direction selectivity, we implemented a set of plausible network models of DS neurons. We demonstrate how cross-correlation analysis can reveal the circuitry and mechanisms generating DS cells, including the nature of the DS nonlinearity and the stage in the visual pathway where the time delay, inherent to motion detection, originates. Our models consisted of LGN cells, V1 simple and complex DS cells that were spiking, conductance-based integrate and fire units with physiologically-realistic intrinsic currents connected by AMPA and GABA-A synapses. We tested model architectures where DS neurons are built directly from LGN inputs or indirectly via orientation-tuned simple cells. In some models, the time delay was implemented via temporal diversity in LGN inputs, in others it was ascribed to dendritic mechanisms in the DS neuron. Both have received recent attention experimentally [3,4]. We also varied whether the DS interaction was facilitatory or suppressive. We computed shuffle-corrected cross-correlograms (CCGs) of spike trains from pairs of units that are accessible to extracellular recording in vivo. We tested the models with visual stimuli employed experimentally, including sinusoidal gratings and flashed dots. We found that cross-correlation is well-suited to determine where the DS time delay arises. In models where temporally-offset inputs synapse onto the DS unit, the peak in the CCG between any non-DS input and the DS cell occurred at the time lag typical of monosynaptically connected cells. In models where the delay was generated in dendritic subunits within the DS cell, the CCG peak shifted systematically to reflect the postsynaptic delays. Cross-correlation revealed the nature of the DS nonlinearity, with facilitatory and suppressive DS mechanisms generating distinct shapes in CCGs. We also identified key factors that determine whether connections between non-DS and DS cells are revealed in CCGs. For example, suppressive mechanisms may only become apparent in CCGs if stimuli that drive the cell in the anti-preferred direction are used. This stimulus dependence reflects the selectivity of the DS mechanism, not the input cells themselves, and is thus independent of network architecture. We explored how the magnitude of peaks and dips in the CCG change as strength and number of inputs to the DS cell vary. We address how these factors create challenges for employing this analysis in electrophysiological recording. Overall, our results suggest that CCGs from simultaneously recorded pre- and post-synaptic non-DS and DS cells, respectively, are necessary and likely sufficient for solving long-standing problems of circuitry and mechanisms underlying cortical direction selectivity. Conference: Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010. Presentation Type: Poster Presentation Topic: Poster session II Citation: Baker PM and Bair W (2010). Cross-correlation analysis reveals circuits and mechanisms underlying direction selectivity.. Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience 2010. doi: 10.3389/conf.fnins.2010.03.00332 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 08 Mar 2010; Published Online: 08 Mar 2010. * Correspondence: Pamela M Baker, University of Oxford, Department of Physiology, Anatomy and Genetics, Oxford, United Kingdom, pb2001@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Pamela M Baker Wyeth Bair Google Pamela M Baker Wyeth Bair Google Scholar Pamela M Baker Wyeth Bair PubMed Pamela M Baker Wyeth Bair Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Category learning and decision making: a cortical circuit model

Event Abstract Back to Event Category learning and decision making: a cortical circuit model How does the brain recognize 'meaning' of sensory stimuli? Through experience we learn to group stimuli into arbitrary categories (such as 'animal' or 'car') according to certain shared characteristics. Recent neurophysiological studies have begun to investigate the neural mechanisms of categorization. Freedman and Assad (2006) recorded single-cell activity in monkeys classifying 360 of visual motion directions into two discrete categories. On each trial, the monkey had to indicate whether categories of two successively presented stimuli (sample and test) were the same (match) or different (nonmatch). Firing rates of neurons in the lateral intraparietal (LIP) area were found to encode the category membership of the currently visible stimulus and did not differentiate between physical features of the stimuli. In contrast, neurons in the middle temporal (MT) area were more involved in visual feature processing and did not carry explicit information about category. A similar categorization study (Freedman et al. 2003) revealed three neural populations in the prefrontal cortex (PFC): the first encoded category information, the second encoded match/nonmatch status of the test stimulus regardless of its category, and the third showed match/nonmatch effects that were limited to one of the categories. The physiological data suggest that two main computational stages are involved in this task: (i) extract the category of the sample; and (ii) perform match vs. nonmatch comparison with the test. To identify general mechanisms underlying these basic computations, we propose a biophysically plausible network model of categorization and match vs. nonmatch comparison for motion directions. The model comprises two interconnected brain areas: a sensory (MT) and a cognitive-type area (LIP/PFC). Both areas are strongly recurrent circuits with dynamics governed by slow recurrent excitation and feedback inhibition. Importantly, heterogeneity of recurrent connections within the cognitive-type circuit leads to diversity of neural responses and a continuos spectrum of neural selectivities (e.g. mixed selectivity). We hypothesize that the bottom-up synapses from MT to LIP/PFC are plastic and implement the reward-dependent stochastic Hebbian learning rule to acquire new categories. The plastic synapses shape the direction-selective input from MT to produce category-tuned responses in LIP/PFC. Match vs. nonmatch decision in the model results from the winner-take-all competition between neural pools preferring matches and nonmatches. Along with the bottom-up input from MT, these neurons receive a weak modulatory input from the category selective neurons, which biases the competition and leads to enhancement/suppression of responses to the test stimulus, depending on whether it matches the sample category. Our model accounts for the single-cell data and provides insight into general mechanisms of categorization and comparison of perceptual stimuli. The model predicts that (i) categories can be learned by synaptic plasticity in the input from sensory to cognitive-type areas; (ii) the mechanism of the match vs. nonmatch comparison is the biased winner-take-all competition.

A general, flexible decision model, applied to visual search

To survive, an organism must pursue multiple goals and switch between them at appropriate times. Generating such behavioral flexibility is an extremely important brain function, yet little is known about how a network of neurons can pursue different goals at different times. A network's behavior can only change with an organism's goals if it receives information about those goals as input. Thus, network responses must depend jointly on both current stimuli and current goals. How are these sources of information combined to generate behavior? And, given those combinations, how can different decision criteria be implemented depending on the task at hand? Here we present a plausible network model for visual search that exhibits different decision criteria depending on the current goal. The network accurately performs several different search tasks based on the same stimulus set, and is capable of switching between tasks without retraining. The model consists of three layers, each directly inspired by cortical neurobiology: (1) A layer of input or sensory neurons whose responses to stimuli are gain-modulated depending on current goals. Such gain modulation implies that sensory and goal-dependent information are combined nonlinearly, as documented in various experiments, but the exact form of this nonlinearity is not crucial. (2) A randomly connected layer of non-linear neurons that acts as a reservoir computer. As in a liquid-state machine, this layer is capable of generating widely different response patterns. (3) A standard race-to-threshold decision model containing two competing neural populations. These are used to indicate the system's decision, whether a target is present or absent in the display, depending on which population reaches a threshold first. We illustrate the model's performance in a series of visual search tasks for which human behavioral data are available. We show that a single fixed network can perform all of these tasks correctly, qualitatively reproducing eight sets of experimental observations: 1. Variations in reaction times due to the number of displayed distractors (set size effects). 2. Differences in reaction times between conjunction and single-feature (or pop-out) searches. 3. Differences in reaction times between target-absent and target-present displays. 4. Reaction-time dependencies on the similarity between distractors and targets. 5. Search asymmetries; that is, differences in reaction-time curves when target and distractor objects are exchanged. 6. The redundant targets effect; that is, decreased reaction times as the number of targets present increases. 7. The ability to perform both singleton search (search for an object that stands out in some way) and directed-object search (search for a specific object). 8. Similar reaction-time curves for search in standard, static displays and in dynamic displays, in which the positions of all objects change randomly with a certain frequency. The last point is particularly significant because other models predict large differences in reaction times between static and dynamic conditions, whereas ours does not. We also show that the model is robust to noise, and can perform either with perfect accuracy or exhibit behaviorally realistic error rates, depending on noise and training parameters. Our model demonstrates that three key properties of cortical neurons – gain-modulation, recurrent connectivity, and race-to-threshold dynamics – can be combined to generate a powerful and flexible decision-making model. We suggest that these features, which are successful at replicating many aspects of visual search, may be combined in a similar manner to generate goal-dependent decisions in many other circumstances.

Input synchrony and spike-timing dependent plasticity

The influence of a weight-dependent spike-timing dependent plasticity (STDP) rule on the temporal evolution and equilibrium state of a certain synapse is investigated. We show that under certain conditions, a spike-induced rate-learning scheme could be achieved. Through studying the situation when a single Hodgkin-Huxley neuron is driven by a large ensemble of input neurons, we find that synchronized firing of a sub population of input neurons may be important to information processing in the nervous system. Using simulations, we show that the temporal structure of the spike trains of these synchronized input neurons can be transmitted reliably; further, synapses from these neurons will increase stably due to the STDP rule and this may provide a mechanism for learning and information storage in biologically plausible network models.