scholarly journals Population response magnitude variation in inferotemporal cortex predicts image memorability

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Andrew Jaegle ◽  
Vahid Mehrpour ◽  
Yalda Mohsenzadeh ◽  
Travis Meyer ◽  
Aude Oliva ◽  
...  
Keyword(s):  
Visual Processing ◽  
Visual Memory ◽  
Memory Task ◽  
Network Models ◽  
Response Magnitude ◽  
Inferotemporal Cortex ◽  
Neural Network Models ◽  
Population Response ◽  
Magnitude Variation ◽  
The Brain

Most accounts of image and object encoding in inferotemporal cortex (IT) focus on the distinct patterns of spikes that different images evoke across the IT population. By analyzing data collected from IT as monkeys performed a visual memory task, we demonstrate that variation in a complementary coding scheme, the magnitude of the population response, can largely account for how well images will be remembered. To investigate the origin of IT image memorability modulation, we probed convolutional neural network models trained to categorize objects. We found that, like the brain, different natural images evoked different magnitude responses from these networks, and in higher layers, larger magnitude responses were correlated with the images that humans and monkeys find most memorable. Together, these results suggest that variation in IT population response magnitude is a natural consequence of the optimizations required for visual processing, and that this variation has consequences for visual memory.

scholarly journals Author response: Population response magnitude variation in inferotemporal cortex predicts image memorability

2019 ◽  
Author(s):  
Andrew Jaegle ◽  
Vahid Mehrpour ◽  
Yalda Mohsenzadeh ◽  
Travis Meyer ◽  
Aude Oliva ◽  
...  
Keyword(s):  
Response Magnitude ◽  
Author Response ◽  
Inferotemporal Cortex ◽  
Population Response ◽  
Magnitude Variation

Material Demands for Optical Neural Networks

MRS Bulletin ◽  
1988 ◽  
Vol 13 (8) ◽  
pp. 30-35 ◽  
Author(s):  
Dana Z. Anderson
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Speech Processing ◽  
Visual Processing ◽  
Network Models ◽  
Human Memory ◽  
Information Storage ◽  
Physical Damage ◽  
Neural Network Models ◽  
The Brain

From the time of their conception, holography and holograms have evolved as a metaphor for human memory. Holograms can be made so that the information they contain is distributed throughout the holographic medium—destroy part of the hologram and the stored information remains wholly intact, except for a loss of detail. In this property holograms evidently have something in common with human memory, which is to some extent resilient against physical damage to the brain. There is much more to the metaphor than simply that information is stored in a distributed manner.Research in the optics community is now looking to holography, in particular dynamic holography, not only for information storage, but for information processing as well. The ideas are based upon neural network models. Neural networks are models for processing that are inspired by the apparent architecture of the brain. This is a processing paradigm that is new to optics. From within this network paradigm we look to build machines that can store and recall information associatively, play back a chain of recorded events, undergo learning and possibly forgetting, make decisions, adapt to a particular environment, and self-organize to evolve some desirable behavior. We hope that neural network models will give rise to optical machines for memory, speech processing, visual processing, language acquisition, motor control, and so on.

scholarly journals Engineering recurrent neural networks from task-relevant manifolds and dynamics

2019 ◽  
Author(s):  
Eli Pollock ◽  
Mehrdad Jazayeri
Keyword(s):  
Neural Network ◽  
Working Memory ◽  
Recurrent Neural Network ◽  
Network Dynamics ◽  
Memory Task ◽  
Network Models ◽  
Network Activity ◽  
Mathematical Framework ◽  
Neural Network Models ◽  
The Brain

AbstractMany cognitive processes involve transformations of distributed representations in neural populations, creating a need for population-level models. Recurrent neural network models fulfill this need, but there are many open questions about how their connectivity gives rise to dynamics that solve a task. Here, we present a method for finding the connectivity of networks for which the dynamics are specified to solve a task in an interpretable way. We apply our method to a working memory task by synthesizing a network that implements a drift-diffusion process over a ring-shaped manifold. We also use our method to demonstrate how inputs can be used to control network dynamics for cognitive flexibility and explore the relationship between representation geometry and network capacity. Our work fits within the broader context of understanding neural computations as dynamics over relatively low-dimensional manifolds formed by correlated patterns of neurons.Author SummaryNeurons in the brain form intricate networks that can produce a vast array of activity patterns. To support goal-directed behavior, the brain must adjust the connections between neurons so that network dynamics can perform desirable computations on behaviorally relevant variables. A fundamental goal in computational neuroscience is to provide an understanding of how network connectivity aligns the dynamics in the brain to the dynamics needed to track those variables. Here, we develop a mathematical framework for creating recurrent neural network models that can address this problem. Specifically, we derive a set of linear equations that constrain the connectivity to afford a direct mapping of task-relevant dynamics onto network activity. We demonstrate the utility of this technique by creating and analyzing a set of network models that can perform a simple working memory task. We then extend the approach to show how additional constraints can furnish networks whose dynamics are controlled flexibly by external inputs. Finally, we exploit the flexibility of this technique to explore the robustness and capacity limitations of recurrent networks. This network synthesis method provides a powerful means for generating and validating hypotheses about how task-relevant computations can emerge from network dynamics.

scholarly journals A neural correlate of image memorability

2019 ◽  
Author(s):  
Andrew Jaegle ◽  
Vahid Mehrpour ◽  
Yalda Mohsenzadeh ◽  
Travis Meyer ◽  
Aude Oliva ◽  
...  
Keyword(s):  
Visual Memory ◽  
Memory Task ◽  
Network Models ◽  
Neural Correlates ◽  
Strongly Correlated ◽  
Object Identity ◽  
Neural Network Models ◽  
Full Account ◽  
Coding Scheme ◽  
Visual Encoding

Some images are easy to remember while others are easily forgotten. While variation in image memorability is consistent across individuals, we lack a full account of its neural correlates. By analyzing data collected from inferotemporal cortex (IT) as monkeys performed a visual memory task, we demonstrate that a simple property of the visual encoding of an image, its population response magnitude, is strongly correlated with its memorability. These results establish a novel behavioral role for the magnitude of the IT response, which lies largely orthogonal to the coding scheme that IT uses to represent object identity. To investigate the origin of IT memorability modulation, we also probed convolutional neural network models trained to categorize objects. We found brain-analogous correlates of memorability that grew in strength across the hierarchy of these networks, suggesting that this memorability correlate is likely to arise from the optimizations required for visual as opposed to mnemonic processing.

Measuring the World

2018 ◽  
Author(s):  
Ann-Sophie Barwich
Keyword(s):  
Sensory Information ◽  
Network Models ◽  
Model System ◽  
Stimulus Input ◽  
Expectancy Effects ◽  
Neural Network Models ◽  
Perceptual Object ◽  
External Objects ◽  
The Common ◽  
The Brain

How much does stimulus input shape perception? The common-sense view is that our perceptions are representations of objects and their features and that the stimulus structures the perceptual object. The problem for this view concerns perceptual biases as responsible for distortions and the subjectivity of perceptual experience. These biases are increasingly studied as constitutive factors of brain processes in recent neuroscience. In neural network models the brain is said to cope with the plethora of sensory information by predicting stimulus regularities on the basis of previous experiences. Drawing on this development, this chapter analyses perceptions as processes. Looking at olfaction as a model system, it argues for the need to abandon a stimulus-centred perspective, where smells are thought of as stable percepts, computationally linked to external objects such as odorous molecules. Perception here is presented as a measure of changing signal ratios in an environment informed by expectancy effects from top-down processes.

scholarly journals Strategic Cognitive Sequencing: A Computational Cognitive Neuroscience Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-18 ◽  
Author(s):  
Seth A. Herd ◽  
Kai A. Krueger ◽  
Trenton E. Kriete ◽  
Tsung-Ren Huang ◽  
Thomas E. Hazy ◽  
...  
Keyword(s):  
Cognitive Process ◽  
Network Models ◽  
Brain Regions ◽  
Human Cognition ◽  
Neural Network Models ◽  
Future Directions ◽  
Error Learning ◽  
Cognitive Actions ◽  
Self Instruction ◽  
The Brain

We address strategic cognitive sequencing, the “outer loop” of human cognition: how the brain decides what cognitive process to apply at a given moment to solve complex, multistep cognitive tasks. We argue that this topic has been neglected relative to its importance for systematic reasons but that recent work on how individual brain systems accomplish their computations has set the stage for productively addressing how brain regions coordinate over time to accomplish our most impressive thinking. We present four preliminary neural network models. The first addresses how the prefrontal cortex (PFC) and basal ganglia (BG) cooperate to perform trial-and-error learning of short sequences; the next, how several areas of PFC learn to make predictions of likely reward, and how this contributes to the BG making decisions at the level of strategies. The third models address how PFC, BG, parietal cortex, and hippocampus can work together to memorize sequences of cognitive actions from instruction (or “self-instruction”). The last shows how a constraint satisfaction process can find useful plans. The PFC maintains current and goal states and associates from both of these to find a “bridging” state, an abstract plan. We discuss how these processes could work together to produce strategic cognitive sequencing and discuss future directions in this area.

Electrophysiology in Neuropsychiatric Research: A Network Perspective

CNS Spectrums ◽  
1999 ◽  
Vol 4 (8) ◽  
pp. 17-29 ◽  
Author(s):  
Georg Winterer ◽  
Werner M. Herrmann ◽  
Richard Coppola
Keyword(s):  
Network Model ◽  
Network Models ◽  
Disease Classification ◽  
Pharmacodynamic Modeling ◽  
Neural Network Models ◽  
New Concepts ◽  
Discrete Processing ◽  
Motor Information ◽  
Brain Processing ◽  
The Brain

ABSTRACTA growing number of anatomic and physiologic studies have shown that parallel sensory and motor information processing occurs in multiple cortical areas. These findings challenge the traditional model of brain processing, which states that the brain is a collection of physically discrete processing modules that pass information to each other by neuronal impulses in a stepwise manner. New concepts based on neural network models suggest that the brain is a dynamically shifting collection of interpenetrating, distributed, and transient neural networks. Neither of these models is necessarily mutually exclusive, but each gives different perspectives on the brain that might be complementary. Each model has its own research methodology, with functional magnetic resonance imaging supporting notions of modular processing, and electrophysiology (eg, electroencephalography) emphasizing the network model. These two technologies might be combined fruitfully in the near future to provide us with a better understanding of the brain. However, this common enterprise can succeed only when the inherent limitations and advantages of both models and technologies are known. After a general introduction about electrophysiology as a research tool and its relation to the network model, several practical examples are given on the generation of pathophysiologic models and disease classification, intermediate phenotyping for genetic investigations, and pharmacodynamic modeling. Finally, proposals are made about how to integrate electrophysiology and neuroimaging methods.

scholarly journals Modeling the online social transmission through the theory of complex adaptive systems and neural network models based on the brain dynamics

2015 ◽  
Author(s):  
Ηλίας Λυμπερόπουλος
Keyword(s):  
Neural Network ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Network Models ◽  
Social Transmission ◽  
Brain Dynamics ◽  
Neural Network Models ◽  
Complex Adaptive ◽  
The Brain

Η μοντελοποίηση δυναμικών κοινωνικών διαδικασιών που λαμβάνουν χώρα στο διαδίκτυο αποτελεί ένα απαιτητικό εγχείρημα για τους παρακάτω λόγους: Πρώτον, τα πρόσωπα που αλληλεπιδρούν είναι ετερογενή και το καθένα ξεχωριστά αποτελεί ένα πολύπλοκο σύστημα. Δεύτερον, οι αλληλεπιδράσεις μεταξύ χρηστών επηρεάζονται από θόρυβο και τυχαιότητα, ενώ παράλληλα το δίκτυο των διαπροσωπικών επικοινωνιών είναι εξαιρετικά πολύπλοκο. Τρίτον, τα κοινωνικά συστήματα δεν βρίσκονται σε κατάσταση ισορροπίας καθώς η δυναμική τους επηρεάζεται από εξωτερικές διαταραχές των οποίων η κατανομή, συσχέτιση με ένα κοινωνικό σύστημα, καθώς και η μη στασιμότητά τους, είναι δύσκολο να καθοριστούν και να συμπεριληφθούν σ’ ένα δυναμικό κοινωνικό μοντέλο. Η επιτυχής μοντελοποίηση της online κοινωνικής μετάδοσης απαιτεί μια προσέγγιση ικανή να ανταπεξέλθει στις παραπάνω προκλήσεις. Γι’ αυτό το σκοπό αναπτύσσω και εφαρμόζω ένα πλαίσιο υλοποίησης βασισμένο στην θεωρία των πολύπλοκων προσαρμοστικών συστημάτων}. Μέσω μια τέτοιας μεθοδολογίας μπορεί να μελετηθεί η δυναμική φύση των αλληλεπιδράσεων μεταξύ χρηστών καθώς και οι μακροσκοπικές ιδιότητες της δραστηριότητας τους υπό την παρουσία εξωτερικών επιρροών. Ένα εξαιρετικά σύνθετο πολύπλοκο προσαρμοστικό σύστημα είναι αυτό του ανθρώπινου εγκεφάλου. Τα κοινωνικά δίκτυα είναι ακόμα πιο πολύπλοκα καθώς ουσιαστικά αποτελούνται από διασυνδεδεμένους εγκεφάλους. Ως αποτέλεσμα η μοντελοποίηση δυναμικών διαδικασιών που λαμβάνουν χώρα στα online κοινωνικά δίκτυα αποτελεί ένα υπερβολικά περίπλοκο έργο. Για την αντιμετώπιση των προκλήσεων μιας τέτοιας προσπάθειας εξετάζω τις online κοινωνικές διεργασίες μέσα από την προοπτική της νευροεπιστήμης θεωρώντας τη δυναμική των online κοινωνικών δικτύων ανάλογη με την δυναμική δικτύων αποτελούμενων από νευρώνες ολοκλήρωσης και πυροδότησης. Μέσω αυτού του ισομορφισμού εισάγω ένα νέο μοντέλο για την online κοινωνική μετάδοση το οποίο βασίζεται σε τρεις πηγές θετικής ή αρνητικής επιρροής: Την αυτοπαραγόμενη, τη διαπροσωπική και την εξωτερική. Το προτεινόμενο μοντέλο εξηγεί την ανάπτυξη της online δραστηριότητας καθώς και τις μορφές μετάδοσής της σε συνάρτηση με το δίκτυο των αλληλεπιδράσεων, την ενδογενή και εξωγενή επιρροή καθώς και τον μηχανισμό ενεργοποίησης των χρηστών. Μέσω πειραμάτων εξομοίωσης και ελέγχων εγκυρότητας των παραγόμενων αποτελεσμάτων μετά από σύγκριση με πραγματικά δεδομένα από το κοινωνικό δίκτυο Twitter, δείχνω ότι το μοντέλο αναπαράγει με ακρίβεια πρότυπα συλλογικής δραστηριότητας προερχόμενα από την απόκριση των χρηστών σε διαφόρων μορφών ερεθίσματα. Η συγκριτική αξιολόγηση των επιδόσεων του προτεινόμενου μοντέλου σε συνάρτηση με αυτή μοντέλων αναφοράς δείχνει ότι αυτό υπερτερεί σημαντικά στην ακρίβεια αναπαραγωγής πραγματικών προτύπων online δραστηριότητας. Μια ακόμα διαδικασία online κοινωνικής μετάδοσης την οποία μοντελοποιώ με καινοτόμο τρόπο σε αυτή τη Διδακτορική Διατριβή αφορά στη μετάδοση online πληροφορίας. Τη δυναμική αυτής της διαδικασίας την αναπαράγω μέσω ενός δικτυακού δυναμικού συστήματος αποτελούμενο από νευρώνες ολοκλήρωσης και πυροδότησης με θορυβώδη εισροή πληροφορίας. Μέσω του συνδυασμού ντετερμινιστικών και στοχαστικών συνιστωσών το προτεινόμενο μοντέλο αναπαράγει με ακρίβεια τα πρότυπα μετάδοσης online πληροφορίας, υποδηλώνοντας ότι αυτά εξαρτώνται από την χρονική δομή, ισχύ, καθώς και το λόγο σήματος-θορύβου των ερεθισμάτων που επηρεάζουν τους διασυνδεδεμένους χρήστες. Ο προτεινόμενος μηχανισμός ενσωματώνει τις έννοιες της ``απλής'' και ``πολύπλοκης'' μετάδοσης και επεκτείνει τις υπάρχουσες προσεγγίσεις καθώς συμπεριλαμβάνει σε ένα ενιαίο μοντέλο ενδογενείς και εξωγενείς, θετικές και αρνητικές, ντετερμινιστικές και στοχαστικές πηγές επιρροής. Τα προτεινόμενα μοντέλα νευρωνικών δικτύων είναι εύκολα προσαρμόσιμα και κατάλληλα για τη μελέτη ενός μεγάλου αριθμού από online και offline κοινωνικές δυναμικές διαδικασίες που αφορούν στη διάδοση συμπεριφορών, τάσεων και φημών, καθώς και στη διάχυση και προώθηση νέων προϊόντων. Τελικά, τη γνώση που προέκυψε από την μοντελοποίηση των προτύπων της online κοινωνικής δραστηριότητας την αξιοποιώ περαιτέρω με την ανάπτυξη ενός προβλεπτικού μοντέλου ικανού να παράγει ακριβείς προβλέψεις σχετικά με τη διάδοση online περιεχομένου.

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