Noisy Cues Research Articles

Multimodal processing of noisy cues in bumblebees

Multimodal cues can improve behavioral responses by enhancing the detection and localization of sensory cues and reducing response times. Across species, studies have shown that multisensory integration of visual and olfactory cues can improve response accuracy. However, in real-world settings, sensory cues are often noisy; visual and olfactory cues can be deteriorated, masked, or mixed, making the target cue less clear to the receiver. In this study, we use an associative learning paradigm (Free Moving Proboscis Extension Reflex, FMPER) to show that having multimodal cues may improve the accuracy of bees' responses to noisy cues. Adding a noisy visual cue improves the accuracy of response to a noisy olfactory cue, despite neither the clear nor noisy visual cue being sufficient when paired with a novel olfactory cue. This may provide insight into the neural mechanisms underlying multimodal processing and the effects of environmental change on pollination services.

Attractor and integrator networks in the brain.

In this Review, we describe the singular success of attractor neural network models in describing how the brain maintains persistent activity states for working memory, corrects errors and integrates noisy cues. We consider the mechanisms by which simple and forgetful units can organize to collectively generate dynamics on the long timescales required for such computations. We discuss the myriad potential uses of attractor dynamics for computation in the brain, and showcase notable examples of brain systems in which inherently low-dimensional continuous-attractor dynamics have been concretely and rigorously identified. Thus, it is now possible to conclusively state that the brain constructs and uses such systems for computation. Finally, we highlight recent theoretical advances in understanding how the fundamental trade-offs between robustness and capacity and between structure and flexibility can be overcome by reusing and recombining the same set of modular attractors for multiple functions, so they together produce representations that are structurally constrained and robust but exhibit high capacity and are flexible.

Colour-concept association formation for novel concepts

ABSTRACT Colour-concept associations influence fundamental processes in cognition and perception, including object recognition and visual reasoning. To understand these effects, it is necessary to understand how colour-concept associations are formed. It is assumed that colour-concept associations are learned through experiences, but questions remain concerning how association formation is influenced by properties of the input and cognitive factors. We addressed these questions by first exposing participants to colour-concept co-occurrences for novel concepts (“Filk” and “Slub” alien species) using a category learning task. We then assessed colour-concept associations using an association rating task. During alien category learning, colour was a noisy cue and shape was 100% diagnostic of category membership, so participants could ignore colour to complete the task. Nonetheless, participants learned systematic colour-concept associations for “seen” colours during alien category learning and generalized to “unseen” colours as a function of colour distance from the seen colours (Experiment 1). Association formation not only depended on colour-concept co-occurrences during alien category learning, but also on cognitive structure of colour categories (e.g., degree to which an observed red colour is typical of the colour category “red”) (Experiment 2). Thus, environmental and cognitive factors combine to influence colour-concept associations formed from experiences in the world.

Can natural selection encode Bayesian priors?

The evolutionary success of many organisms depends on their ability to make decisions based on estimates of the state of their environment (e.g., predation risk) from uncertain information. These decision problems have optimal solutions and individuals in nature are expected to evolve the behavioural mechanisms to make decisions as if using the optimal solutions. Bayesian inference is the optimal method to produce estimates from uncertain data, thus natural selection is expected to favour individuals with the behavioural mechanisms to make decisions as if they were computing Bayesian estimates in typically-experienced environments, although this does not necessarily imply that favoured decision-makers do perform Bayesian computations exactly. Each individual should evolve to behave as if updating a prior estimate of the unknown environment variable to a posterior estimate as it collects evidence. The prior estimate represents the decision-maker’s default belief regarding the environment variable, i.e., the individual’s default ‘worldview’ of the environment. This default belief has been hypothesised to be shaped by natural selection and represent the environment experienced by the individual’s ancestors.We present an evolutionary model to explore how accurately Bayesian prior estimates can be encoded genetically and shaped by natural selection when decision-makers learn from uncertain information. The model simulates the evolution of a population of individuals that are required to estimate the probability of an event. Every individual has a prior estimate of this probability and collects noisy cues from the environment in order to update its prior belief to a Bayesian posterior estimate with the evidence gained. The prior is inherited and passed on to offspring. Fitness increases with the accuracy of the posterior estimates produced. Simulations show that prior estimates become accurate over evolutionary time. In addition to these ‘Bayesian’ individuals, we also introduce ‘frequentist’ individuals that do not use a prior and instead use frequentist inference when estimating the probability. Competition between the two shows that the former tend to have an evolutionary advantage over the latter, as predicted by the literature, and that this advantage is lowest when the information available to individuals poses the least uncertainty.

Assessing the brain's memory storage capacity

Memory A hallmark property of the neuronal network in the hippocampus of the brain is its ability to retrieve patterns from partial or noisy cues. This process is called pattern completion. Despite the importance of pattern completion for memory retrieval, its underlying synaptic mechanisms are poorly understood. By simultaneously recording from up to eight neurons, Guzman et al. found sparse connections between CA3 pyramidal cells in the hippocampus and few synaptic contacts. Computational modeling revealed that such concise macroscopic and microscopic connectivity combine to ensure efficient memory storage and retrieval in this brain region. Science , this issue p. [1117][1] [1]: /lookup/doi/10.1126/science.aaf1836

Formal concept analysis approach to cognitive functionalities of bidirectional associative memory

Abstract Pattern association is one among the ways through which human brain stores and recalls information. From the literature, it is evident that cognitive abilities of human brain such as learning, memorizing, recalling and updating of information are performed via concepts and their connections. In this work we have made use of Formal Concept Analysis (FCA), a mathematical framework for data and knowledge processing, to represent memories and to perform some of the cognitive functions of human brain. In particular, we model the functionalities of bidirectional associative memories. The proposed model can learn, memorize the learnt information, bi-directionally recall the information that is associated with the presented cue with the help of object-attribute relations that exists in the scenario and update the knowledge when there is a change in the considered scenario. Also when a noisy cue is given, the model is capable of recalling the most closely associated pattern by exploiting the concept hierarchy principle of FCA. Similarly, when a new information is presented on a learnt scenario, the proposed model can update its knowledge by avoiding the need to re-learn scenario. We illustrate the proposed model with a case study and validate with experiments on few real world datasets.

On the origins of suboptimality in human probabilistic inference.

Humans have been shown to combine noisy sensory information with previous experience (priors), in qualitative and sometimes quantitative agreement with the statistically-optimal predictions of Bayesian integration. However, when the prior distribution becomes more complex than a simple Gaussian, such as skewed or bimodal, training takes much longer and performance appears suboptimal. It is unclear whether such suboptimality arises from an imprecise internal representation of the complex prior, or from additional constraints in performing probabilistic computations on complex distributions, even when accurately represented. Here we probe the sources of suboptimality in probabilistic inference using a novel estimation task in which subjects are exposed to an explicitly provided distribution, thereby removing the need to remember the prior. Subjects had to estimate the location of a target given a noisy cue and a visual representation of the prior probability density over locations, which changed on each trial. Different classes of priors were examined (Gaussian, unimodal, bimodal). Subjects' performance was in qualitative agreement with the predictions of Bayesian Decision Theory although generally suboptimal. The degree of suboptimality was modulated by statistical features of the priors but was largely independent of the class of the prior and level of noise in the cue, suggesting that suboptimality in dealing with complex statistical features, such as bimodality, may be due to a problem of acquiring the priors rather than computing with them. We performed a factorial model comparison across a large set of Bayesian observer models to identify additional sources of noise and suboptimality. Our analysis rejects several models of stochastic behavior, including probability matching and sample-averaging strategies. Instead we show that subjects' response variability was mainly driven by a combination of a noisy estimation of the parameters of the priors, and by variability in the decision process, which we represent as a noisy or stochastic posterior.

Neural modeling of episodic memory: encoding, retrieval, and forgetting.

This paper presents a neural model that learns episodic traces in response to a continuous stream of sensory input and feedback received from the environment. The proposed model, based on fusion adaptive resonance theory (ART) network, extracts key events and encodes spatio-temporal relations between events by creating cognitive nodes dynamically. The model further incorporates a novel memory search procedure, which performs a continuous parallel search of stored episodic traces. Combined with a mechanism of gradual forgetting, the model is able to achieve a high level of memory performance and robustness, while controlling memory consumption over time. We present experimental studies, where the proposed episodic memory model is evaluated based on the memory consumption for encoding events and episodes as well as recall accuracy using partial and erroneous cues. Our experimental results show that: 1) the model produces highly robust performance in encoding and recalling events and episodes even with incomplete and noisy cues; 2) the model provides enhanced performance in a noisy environment due to the process of forgetting; and 3) compared with prior models of spatio-temporal memory, our model shows a higher tolerance toward noise and errors in the retrieval cues.

Continuous Evolution of Statistical Estimators for Optimal Decision-Making

In many everyday situations, humans must make precise decisions in the presence of uncertain sensory information. For example, when asked to combine information from multiple sources we often assign greater weight to the more reliable information. It has been proposed that statistical-optimality often observed in human perception and decision-making requires that humans have access to the uncertainty of both their senses and their decisions. However, the mechanisms underlying the processes of uncertainty estimation remain largely unexplored. In this paper we introduce a novel visual tracking experiment that requires subjects to continuously report their evolving perception of the mean and uncertainty of noisy visual cues over time. We show that subjects accumulate sensory information over the course of a trial to form a continuous estimate of the mean, hindered only by natural kinematic constraints (sensorimotor latency etc.). Furthermore, subjects have access to a measure of their continuous objective uncertainty, rapidly acquired from sensory information available within a trial, but limited by natural kinematic constraints and a conservative margin for error. Our results provide the first direct evidence of the continuous mean and uncertainty estimation mechanisms in humans that may underlie optimal decision making.

Serial decision-making and noise in the assembly of neural circuits

Accurate decision-making is essential for the correct assembly of sensory-motor circuits, which are composed of neurons whose axonal growth cones execute discrete, binary decisions at sequential trajectory selection points to connect motor neurons to target muscles. Limb motor axon trajectory selection is controlled by guidance cues: ephrin ligands and Eph receptors [1], whose expression levels are variable. Some cues direct axons to opposite trajectories. Genetic inactivation of Eph and ephrin cues results in inaccurate trajectory decisions and incorrect neural circuit topology [2]. To understand this decision making process we developed a model of an axon growth cone as it grows from the spinal chord,passing binary decision regions to innervate its target muscle. Our model linearly sums noisy guidance cues at receptors on the growth cone surface, which in turn drive cytoskeletal dynamics of their nearest-neighbour microtubule (resulting in the protraction/ of individual filopodia of the growth cone). Our simple model incorporates three basic constraints: 1. guidance cues are noisy signal due to gene expression variability and stochastic ligand-receptor interactions, 2. guidance cue combination is linear and additive and, 3. raw material constraints during growth, limit the total size of the growth cone. Combining these basic constraints suggests a decision-making model that explains in a unified way 4 experimental findings: First, at binary decision points genetically homogenous populations of axons can partition on the two possible trajectories tissues with an unequal ratio. Unequal ratios are observed in very different developmental decisions e.g. Bacillus subtilis cell fate (80:20), Drosophila photoreceptor type(70:30), the optic chiasma of vertebrate retinae(97:3) and in frog, chicken and mouse motor systems(93:7-96:4) [2,3]. It was unclear how such unequal ratios are reliably produced, as deterministic mechanisms for decision-making could account for 100:0, and 50:50 ratios but not intermediate ones. Second, shifts in partition ratios were observed in genetic mutation studies where cues were removed or added, in our ephrin and Eph mouse mutants, these decision were inordinately variable (from 95:5 in wild-type to 80:20, 60:40, 0:100) [2]. Third, axons growing in genetically symmetrised limbs (where dorsal or ventral limb halves are duplicated) grow with equal probability towards either target or fail to enter the limb [4]. Fourth, experiments showed that growth cones faced with decisions slow their movement considerably (500-1,000%) and grow in diameter (300-1000%). Thus, the decision-making machinery grows commensurately in size and sensing capacity (receptor-covered axon surface area). Our model reproduces this as the repulsive cues for each target competitively engage cytoskeletal material towards targets and reducing availability of material devoted to forward movement. Once decisions are made, our model growth cone shrinks as cytoskeletal material is used for rapid axon growth in accordance with experiments. In conclusion, we developed a mechanistic model of serial decision-making in axonal growth cones that explains all four experimental findings. Our findings highlight the axons and generally neurons during circuit assembly in development, face computational problems that are comparable to decision making under uncertainty in whole animals, such as sensory processing, decision-making and control of movement [5].

Optimal storage and recall with biologically plausible synapses

AbstractSynaptic plasticity is widely accepted to underlie learning and memory. Yet, models of associative networks with biologically plausible synapses fail to match brain performance: memories stored in such networks are quickly overwritten by ongoing plasticity (Amit & Fusi 1996, Fusi et al 2007). Metaplasticity - the process by which neural activity changes the ability of synapses to exhibit further plasticity - is believed to increase memory capacity (Fusi et al 2005). However, it remains unclear if neurons can make use of this additional information during recall. In particular, previous attempts at reading out information in metaplastic synapses using heuristic recall dynamics led to rather poor performance (Huang & Amit 2010).Here, we developed a theoretical framework for storage and recall with finite-state synapses that allowed us to find neural and synaptic dynamics that maximize the efficiency of autoassociative recall.Since information storage by synaptic plasticity is lossy, we formulated the problem of recalling a previously stored pattern from a noisy cue as probabilistic inference (Lengyel et al 2005) and derived neural dynamics efficiently implementing such inferences. Our approach is general and can be applied to any synaptic plasticity model which involves stochastic transitions between a finite set of states.We show how synaptic plasticity rules need to be matched to the statistics of stored patterns, and how recall dynamics need to be matched both to input statistics and to the plasticity rule itself in order to achieve optimal performance. In particular, for binary synapses with metastates we demonstrate for the first time that memories can be efficiently read out with biologically plausible network dynamics that we derive directly from the synaptic metaplasticity rule with virtually no free parameters.

The Noisy Cue Abstraction Model is Equivalent to the Multiplicative Prototype Model

We prove that the noisy cue abstraction model for binary choice and the multiplicative prototype model of categorization are formally identical.

Cortical memory dynamics.

Biological memories have a number of unique features, including (1) hierarchical, reciprocally interacting layers, (2) lateral inhibitory interactions within layers, and (3) Hebbian synaptic modifications. We incorporate these key features into a mathematical and computational model in which we derive and study Hebbian learning dynamics and recall dynamics. Introducing the construct of a feasible memory (a memory that formally responds correctly to a specified collection of noisy cues that are known in advance), we study stability and convergence of the two kinds of dynamics by both analytical and computational methods. A conservation law for memory feasibility under Hebbian dynamics is derived. An infomax net is one where the synaptic weights resolve the most uncertainty about a neural input based on knowledge of the output. The infomax notion is described and is used to grade memories and memory performance. We characterize the recall dynamics of the most favorable solutions from an infomax perspective. This characterization includes the dynamical behavior when the net is presented with external stimuli (noisy cues) and a description of the accuracy of recall. The observed richness of dynamical behavior, such as its initial state sensitivity, provides some hints for possible biological parallels to this model.

A model for visual flow-field cueing and self-motion estimation

A computational model for visual flow-field cueing and self-motion estimation is developed and simulated. The model is predicated on the notion that the pilot makes noisy, sampled measurements on the spatially distributed visual flow-field surrounding him and, on the basis of these measurements, generates estimates of his own linear and angular terrain-relative velocities which optimally satisfy, in a least-squares sense, the visual kinematic flow constraints. The least-squares formulation is applicable to general observer motions and viewing geometries; it is projection-plane independent and rational in its treatment of redundant and noisy flow cues. A subsidiary but significant output of the model is an `impact time' map, and observer-centered spatially scaled replica of the viewed surface. Simulations are presented to demonstrate the parametric sensitivity and ability to model relevant human visual performance data. Preliminary simulation results of human visual performance are encouraging.

Construction of a Distributed Associative Memory on the Basis of Bayes Discriminant Rule

The purpose of this correspondence is to propose a new construction method of distributed associative memory which operates with discrete-valued signals. In this method, memorized pairs of vectors (cue vectors and data vectors) are recorded in the form of a matrix W and a vector T. From an input vector X, the data vector is recalled by an operation u(XW + T) where X is a cue vector or a noisy cue vector. and u is a quantizing function. The methods of memorization and recall are similar to the Associatron; however, the proposed model can recall the data vectors optimally in Bayesian sense even when noisy cue vectors are given as the input vectors.