Slow manifolds within network dynamics encode working memory efficiently and robustly

Working memory is a cognitive function involving the storage and manipulation of latent information over brief intervals of time, thus making it crucial for context-dependent computation. Here, we use a top-down modeling approach to examine network-level mechanisms of working memory, an enigmatic issue and central topic of study in neuroscience. We optimize thousands of recurrent rate-based neural networks on a working memory task and then perform dynamical systems analysis on the ensuing optimized networks, wherein we find that four distinct dynamical mechanisms can emerge. In particular, we show the prevalence of a mechanism in which memories are encoded along slow stable manifolds in the network state space, leading to a phasic neuronal activation profile during memory periods. In contrast to mechanisms in which memories are directly encoded at stable attractors, these networks naturally forget stimuli over time. Despite this seeming functional disadvantage, they are more efficient in terms of how they leverage their attractor landscape and paradoxically, are considerably more robust to noise. Our results provide new hypotheses regarding how working memory function may be encoded within the dynamics of neural circuits.     

A Quantitative Theory of Neural Computation

We show how a general quantitative theory of neural computation can be used to explain two recent experimental findings in neuroscience. The first of these findings is that in human medial temporal lobe there exist neurons that correspond to identifiable concepts, such as a particular actress. Further, even when such concepts are preselected by the experimenter, such neurons can be found with paradoxical ease, after examining relatively few neurons. We offer a quantitative computational explanation of this phenomenon, where apparently none existed before. Second, for the locust olfactory system estimates of the four parameters of neuron numbers, synapse numbers, synapse strengths, and the numbers of neurons that represent an odor are now available. We show here that these numbers are related as predicted by the general theory. More generally, we identify two useful regimes for neural computation with distinct ranges of these quantitative parameters.     

Neural implementations of Bayesian inference

Bayesian inference has emerged as a general framework that captures how organisms make decisions under uncertainty. Recent experimental findings reveal disparate mechanisms for how the brain generates behaviors predicted by normative Bayesian theories. Here, we identify two broad classes of neural implementations for Bayesian inference: a modular class, where each probabilistic component of Bayesian computation is independently encoded and a transform class, where uncertain measurements are converted to Bayesian estimates through latent processes. Many recent experimental neuroscience findings studying probabilistic inference broadly fall into these classes. We identify potential avenues for synthesis across these two classes and the disparities that, at present, cannot be reconciled. We conclude that to distinguish among implementation hypotheses for Bayesian inference, we require greater engagement among theoretical and experimental neuroscientists in an effort that spans different scales of analysis, circuits, tasks, and species.     

Dynamics on the manifold: Identifying computational dynamical activity from neural population recordings

The question of how the collective activity of neural populations gives rise to complex behaviour is fundamental to neuroscience. At the core of this question lie considerations about how neural circuits can perform computations that enable sensory perception, decision making, and motor control. It is thought that such computations are implemented through the dynamical evolution of distributed activity in recurrent circuits. Thus, identifying dynamical structure in neural population activity is a key challenge towards a better understanding of neural computation. At the same time, interpreting this structure in light of the computation of interest is essential for linking the time-varying activity patterns of the neural population to ongoing computational processes. Here, we review methods that aim to quantify structure in neural population recordings through a dynamical system defined in a low-dimensional latent variable space. We discuss advantages and limitations of different modelling approaches and address future challenges for the field.     

Effect of the topology and delayed interactions in neuronal networks synchronization

As important as the intrinsic properties of an individual nervous cell stands the network of neurons in which it is embedded and by virtue of which it acquires great part of its responsiveness and functionality. In this study we have explored how the topological properties and conduction delays of several classes of neural networks affect the capacity of their constituent cells to establish well-defined temporal relations among firing of their action potentials. This ability of a population of neurons to produce and maintain a millisecond-precise coordinated firing (either evoked by external stimuli or internally generated) is central to neural codes exploiting precise spike timing for the representation and communication of information. Our results, based on extensive simulations of conductance-based type of neurons in an oscillatory regime, indicate that only certain topologies of networks allow for a coordinated firing at a local and long-range scale simultaneously. Besides network architecture, axonal conduction delays are also observed to be another important factor in the generation of coherent spiking. We report that such communication latencies not only set the phase difference between the oscillatory activity of remote neural populations but determine whether the interconnected cells can set in any coherent firing at all. In this context, we have also investigated how the balance between the network synchronizing effects and the dispersive drift caused by inhomogeneities in natural firing frequencies across neurons is resolved. Finally, we show that the observed roles of conduction delays and frequency dispersion are not particular to canonical networks but experimentally measured anatomical networks such as the macaque cortical network can display the same type of behavior.     

Motor learning with unstable neural representations

It is often assumed that learning takes place by changing an otherwise stable neural representation. To test this assumption, we studied changes in the directional tuning of primate motor cortical neurons during reaching movements performed in familiar and novel environments. During the familiar task, tuning curves exhibited slow random drift. During learning of the novel task, random drift was accompanied by systematic shifts of tuning curves. Our analysis suggests that motor learning is based on a surprisingly unstable neural representation. To explain these results, we propose that motor cortex is a redundant neural network, i.e., any single behavior can be realized by multiple configurations of synaptic strengths. We further hypothesize that synaptic modifications underlying learning contain a random component, which causes wandering among synaptic configurations with equivalent behaviors but different neural representations. We use a simple model to explore the implications of these assumptions.     

Combining Perturbations and Parameter Variation to Influence Mean First Passage Times

Perturbations are relatively large shocks to state variables that can drive transitions between stable states, while drift in parameter values gradually alters equilibrium magnitudes. This latter effect can lead to equilibrium bifurcation, the generation, or annihilation of equilibria. Equilibrium annihilations reduce the number of equilibria and so are associated with catastrophic population collapse. We study the combination of perturbations and parameter drift, using a two-species intraguild predation (IGP) model. For example, we use bifurcation analysis to understand how parameter drift affects equilibrium number, showing that both competition and predation rates in this model are bifurcating parameters. We then introduce a stochastic process to model the effects of population perturbations. We demonstrate how to evaluate the joint effects of perturbations and drift using the common currency of mean first passage time to transitions between stable states. Our methods and results are quite general, and for example, can relate to issues in both pest control and sustainable harvest. Our results show that parameter drift (1) does not importantly change the expected time to reach target points within a basin of attraction, but (2) can dramatically change the expected time to shift between basins of attraction, through its effects on equilibrium resilience.     

Current developments and future directions of bio-inspired computation and implications for ecoinformatics

Evolutionary and neural computation has been used widely in solving various problems in biological ecosystems. This paper reviews some of the recent work in evolutionary computation and neural network ensembles that could be explored further in the context of ecoinformatics. Although these bio-inspired techniques were not developed specifically for ecoinformatics, their successes in solving complex problems in other fields demonstrate how these techniques could be adapted and used for tackling difficult problems in ecoinformatics. Firstly, we will review our work in modelling and model calibration, which is an important topic in ecoinformatics. Secondly one example will be given to illustrate how coevolutionary algorithms could be used in problem-solving. Thirdly, we will describe our work on neural network ensembles, which can be used for various classification and prediction problems in ecoinformatics. Finally, we will discuss ecosystem-inspired computational models and algorithms that could be explored as directions of future research.     

A behavioral framework to guide research on central auditory development and plasticity

The auditory CNS is influenced profoundly by sounds heard during development. Auditory deprivation and?augmented sound exposure can each perturb the maturation of neural computations as well as their underlying synaptic properties. However, we have learned little about the emergence of perceptual skills in these same model systems, and especially how perception is influenced by early acoustic experience. Here, we argue that developmental studies must take greater advantage of behavioral benchmarks. We?discuss quantitative measures of perceptual development and suggest how they can play a much larger role in guiding experimental design. Most importantly, including behavioral measures will allow us to establish empirical connections among environment, neural development, and perception.     

Optimal Prediction of Moving Sound Source Direction in the Owl

Capturing nature’s statistical structure in behavioral responses is at the core of the ability to function adaptively in the environment. Bayesian statistical inference describes how sensory and prior information can be combined optimally to guide behavior. An outstanding open question of how neural coding supports Bayesian inference includes how sensory cues are optimally integrated over time. Here we address what neural response properties allow a neural system to perform Bayesian prediction, i.e., predicting where a source will be in the near future given sensory information and prior assumptions. The work here shows that the population vector decoder will perform Bayesian prediction when the receptive fields of the neurons encode the target dynamics with shifting receptive fields. We test the model using the system that underlies sound localization in barn owls. Neurons in the owl’s midbrain show shifting receptive fields for moving sources that are consistent with the predictions of the model. We predict that neural populations can be specialized to represent the statistics of dynamic stimuli to allow for a vector read-out of Bayes-optimal predictions.     

The neural encoding of self-motion

As we move through the world, information can be combined from multiple sources in order to allow us to perceive our self-motion. The vestibular system detects and encodes the motion of the head in space. In addition, extra-vestibular cues such as retinal-image motion (optic flow), proprioception, and motor efference signals, provide valuable motion cues. Here I focus on the coding strategies that are used by the brain to create neural representations of self-motion. I review recent studies comparing the thresholds of single versus populations of vestibular afferent and central neurons. I then consider recent advances in understanding the brain's strategy for combining information from the vestibular sensors with extra-vestibular cues to estimate self-motion. These studies emphasize the need to consider not only the rules by which multiple inputs are combined, but also how differences in the behavioral context govern the nature of what defines the optimal computation.     

Lessons for circuit function from large insects: towards understanding the neural basis of motor flexibility

Motor behaviors result from information processing that occurs in multiple neural networks acting at all levels from the initial selection of the behavior to its final generation. A long-standing research interest is how single neural networks can help generate different motor behaviors, that is, the origin of motor flexibility. Modern experimental techniques allow studying neural network activity during the production of multiple motor behaviors. Recent data provide strong evidence that the neural networks controlling insect legs are individually modified in task-dependent and finely tuned fashions. Understanding the mechanistic basis of these neural network modifications will be of particular interest in the upcoming years.     

Integrated mechanisms of anticipation and rate-of-change computations in cortical circuits

Local neocortical circuits are characterized by stereotypical physiological and structural features that subserve generic computational operations. These basic computations of the cortical microcircuit emerge through the interplay of neuronal connectivity, cellular intrinsic properties, and synaptic plasticity dynamics. How these interacting mechanisms generate specific computational operations in the cortical circuit remains largely unknown. Here, we identify the neurophysiological basis of both the rate of change and anticipation computations on synaptic inputs in a cortical circuit. Through biophysically realistic computer simulations and neuronal recordings, we show that the rate-of-change computation is operated robustly in cortical networks through the combination of two ubiquitous brain mechanisms: short-term synaptic depression and spike-frequency adaptation. We then show how this rate-of-change circuit can be embedded in a convergently connected network to anticipate temporally incoming synaptic inputs, in quantitative agreement with experimental findings on anticipatory responses to moving stimuli in the primary visual cortex. Given the robustness of the mechanism and the widespread nature of the physiological machinery involved, we suggest that rate-of-change computation and temporal anticipation are principal, hard-wired functions of neural information processing in the cortical microcircuit.     

Event-driven simulation of neural population synchronization facilitated by electrical coupling

Most neural communication and processing tasks are driven by spikes. This has enabled the application of the event-driven simulation schemes. However the simulation of spiking neural networks based on complex models that cannot be simplified to analytical expressions (requiring numerical calculation) is very time consuming. Here we describe briefly an event-driven simulation scheme that uses pre-calculated table-based neuron characterizations to avoid numerical calculations during a network simulation, allowing the simulation of large-scale neural systems. More concretely we explain how electrical coupling can be simulated efficiently within this computation scheme, reproducing synchronization processes observed in detailed simulations of neural populations.     

细胞类型特异的遗传学操作：揭开复杂神经环路的面纱

现代神经科学的一个重要课题足阐明复杂神经环路及其细胞组成形成行为的机制。我们希望可以通过对特定神经元群体的区分和操作在引发行为的神经计算和特定神经元群体活性之间建立一种因果联系。运用BAC重组工程技术,我们建立了超过20个“敲入”驱动品系。在这些驱动品系中,Cre或者是可诱导的CreER能够在特定类掣的GABA能细胞中表达。另外,我们还建立了一些Cre报告小鼠品系和一。个基于病毒转染的蛋白表达系统。这些病毒包含一个Cre-激活的表达元件,可以将一些荧光蛋白或分了开关在体内以很高的效率表达。这种基因操作的策略可以使我们进行如下的一些观察和操作：（1）在突触水平观察中间神经元的形态和他们之间的联系;（2）观察中间神经元的活性及其过往的活动;（3）在生理的时间分辨率上操纵特定细胞群的发放和突触传递。这将使我们对复杂神经环路功能和组织的认识进入。个全新的领域。     

Dissociable Perceptual Effects of Visual Adaptation

Neurons in the visual cortex are responsive to the presentation of oriented and curved line segments, which are thought to act as primitives for the visual processing of shapes and objects. Prolonged adaptation to such stimuli gives rise to two related perceptual effects: a slow change in the appearance of the adapting stimulus (perceptual drift), and the distortion of subsequently presented test stimuli (adaptational aftereffects). Here we used a psychophysical nulling technique to dissociate and quantify these two classical observations in order to examine their underlying mechanisms and their relationship to one another. In agreement with previous work, we found that during adaptation horizontal and vertical straight lines serve as attractors for perceived orientation and curvature. However, the rate of perceptual drift for different stimuli was not predictive of the corresponding aftereffect magnitudes, indicating that the two perceptual effects are governed by distinct neural processes. Finally, the rate of perceptual drift for curved line segments did not depend on the spatial scale of the stimulus, suggesting that its mechanisms lie outside strictly retinotopic processing stages. These findings provide new evidence that the visual system relies on statistically salient intrinsic reference stimuli for the processing of visual patterns, and point to perceptual drift as an experimental window for studying the mechanisms of visual perception.     

FPNA: interaction between FPGA and neural computation

Neural networks are usually considered as naturally parallel computing models. But the number of operators and the complex connection graph of standard neural models can not be directly handled by digital hardware devices. More particularly, several works show that programmable digital hardware is a real opportunity for flexible hardware implementations of neural networks. And yet many area and topology problems arise when standard neural models are implemented onto programmable circuits such as FPGAs, so that the fast FPGA technology improvements can not be fully exploited. Therefore neural network hardware implementations need to reconcile simple hardware topologies with complex neural architectures. The theoretical and practical framework developed, allows this combination thanks to some principles of configurable hardware that are applied to neural computation: Field Programmable Neural Arrays (FPNA) lead to powerful neural architectures that are easy to map onto FPGAs, thanks to a simplified topology and an original data exchange scheme. This paper shows how FPGAs have led to the definition of the FPNA computation paradigm. Then it shows how FPNAs contribute to current and future FPGA-based neural implementations by solving the general problems that are raised by the implementation of complex neural networks onto FPGAs.     

Nonrandom associations of maternally transmitted symbionts in insects: The roles of drift versus biased cotransmission and selection

Virtually all higher organisms form holobionts with associated microbiota. To understand the biology of holobionts we need to know how species assemble and interact. Controlled experiments are suited to study interactions between particular symbionts, but they only accommodate a tiny portion of the diversity within each species. Alternatively, interactions can be inferred by testing if associations among symbionts in the field are more or less frequent than expected under random assortment. However, random assortment may not be a valid null hypothesis for maternally transmitted symbionts since drift alone can result in associations. Here, we analyse a European field survey of endosymbionts in pea aphids (Acyrthosiphon pisum), confirming that symbiont associations are pervasive. To interpret them, we develop a model simulating the effect of drift on symbiont associations. We show that drift induces apparently nonrandom assortment, even though horizontal transmissions and maternal transmission failures tend to randomise symbiont associations. We also use this model in the approximate Bayesian computation framework to revisit the association between Spiroplasma and Wolbachia in Drosophila neotestacea. New field data reported here reveal that this association has disappeared in the investigated location, yet a significant interaction between Spiroplasma and Wolbachia can still be inferred. Our study confirms that negative and positive associations are pervasive and often induced by symbiont‐symbiont interactions. Nevertheless, some associations are also likely to be driven by drift. This possibility needs to be considered when performing such analyses, and our model is helpful for this purpose.     

Analysis of mitochondrial metabolism in situ: Combining stable isotope labeling with selective permeabilization

To date, it is well-established that mitochondrial dysfunction does not only play a vital role in cancer but also in other pathological conditions such as neurodegenerative diseases and inflammation. An important tool for the analysis of cellular metabolism is the application of stable isotope labeled substrates, which allow for the tracing of atoms throughout metabolic networks. While such analyses yield very detailed information about intracellular fluxes, the determination of compartment specific fluxes is far more challenging. Most approaches for the deconvolution of compartmented metabolism use computational models whereas experimental methods are rare. Here, we developed an experimental setup based on selective permeabilization of the cytosolic membrane that allows for the administration of stable isotope labeled substrates directly to mitochondria. We demonstrate how this approach can be used to infer metabolic changes in mitochondria induced by either chemical or genetic perturbations and give an outlook on its potential applications.