Chaotic itinerancy: Insufficient perceptual evidence

2001 ◽  
Vol 24 (5) ◽  
pp. 819-820 ◽  
Author(s):  
Leslie M. Kay
Keyword(s):  
Information Processing ◽  
Neural Activity ◽  
Olfactory System ◽  
Attractor Dynamics ◽  
Large Step ◽  
Perceptual Evidence ◽  
Cantor Coding

Chaotic itinerancy is useful for illustrating transitions in attractor dynamics seen in the olfactory system. Cantor coding is a good model for information processing, but so far it lacks perceptual proof. The theories presented provide a large step toward bridging the use of chaos as an interpretive tool and hard examination of chaotic neural activity during perception.

scholarly journals Recurrent circuitry is required to stabilize piriform cortex odor representations across brain states

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Kevin A Bolding ◽  
Shivathmihai Nagappan ◽  
Bao-Xia Han ◽  
Fan Wang ◽  
Kevin M Franks
Keyword(s):  
Neural Activity ◽  
Neural Circuits ◽  
Activity Patterns ◽  
Piriform Cortex ◽  
Theoretical Studies ◽  
Pattern Completion ◽  
Attractor Networks ◽  
Attractor Dynamics ◽  
Brain States ◽  
Associative Function

Pattern completion, or the ability to retrieve stable neural activity patterns from noisy or partial cues, is a fundamental feature of memory. Theoretical studies indicate that recurrently connected auto-associative or discrete attractor networks can perform this process. Although pattern completion and attractor dynamics have been observed in various recurrent neural circuits, the role recurrent circuitry plays in implementing these processes remains unclear. In recordings from head-fixed mice, we found that odor responses in olfactory bulb degrade under ketamine/xylazine anesthesia while responses immediately downstream, in piriform cortex, remain robust. Recurrent connections are required to stabilize cortical odor representations across states. Moreover, piriform odor representations exhibit attractor dynamics, both within and across trials, and these are also abolished when recurrent circuitry is eliminated. Here, we present converging evidence that recurrently-connected piriform populations stabilize sensory representations in response to degraded inputs, consistent with an auto-associative function for piriform cortex supported by recurrent circuitry.

scholarly journals Habituation as a neural algorithm for online odor discrimination

2020 ◽  
Vol 117 (22) ◽  
pp. 12402-12410 ◽  
Author(s):  
Yang Shen ◽  
Sanjoy Dasgupta ◽  
Saket Navlakha
Keyword(s):  
Neural Activity ◽  
Olfactory System ◽  
Simple Algorithm ◽  
Fruit Fly ◽  
Neural Systems ◽  
Redundant Information ◽  
Biological Mechanism ◽  
Sensory Physiology ◽  
Odor Perception ◽  
Odor Mixtures

Habituation is a form of simple memory that suppresses neural activity in response to repeated, neutral stimuli. This process is critical in helping organisms guide attention toward the most salient and novel features in the environment. Here, we follow known circuit mechanisms in the fruit fly olfactory system to derive a simple algorithm for habituation. We show, both empirically and analytically, that this algorithm is able to filter out redundant information, enhance discrimination between odors that share a similar background, and improve detection of novel components in odor mixtures. Overall, we propose an algorithmic perspective on the biological mechanism of habituation and use this perspective to understand how sensory physiology can affect odor perception. Our framework may also help toward understanding the effects of habituation in other more sophisticated neural systems.

scholarly journals Intrinsic Adaptation in Autonomous Recurrent Neural Networks

2012 ◽  
Vol 24 (2) ◽  
pp. 523-540 ◽  
Author(s):  
Dimitrije Marković ◽  
Claudius Gros
Keyword(s):  
Neural Networks ◽  
Information Processing ◽  
Neural Activity ◽  
Recurrent Neural Networks ◽  
Activity Patterns ◽  
Self Organized ◽  
Dynamical Regimes ◽  
Autonomous State ◽  
Adaptation Processes ◽  
External Stimuli

A massively recurrent neural network responds on one side to input stimuli and is autonomously active, on the other side, in the absence of sensory inputs. Stimuli and information processing depend crucially on the qualia of the autonomous-state dynamics of the ongoing neural activity. This default neural activity may be dynamically structured in time and space, showing regular, synchronized, bursting, or chaotic activity patterns. We study the influence of nonsynaptic plasticity on the default dynamical state of recurrent neural networks. The nonsynaptic adaption considered acts on intrinsic neural parameters, such as the threshold and the gain, and is driven by the optimization of the information entropy. We observe, in the presence of the intrinsic adaptation processes, three distinct and globally attracting dynamical regimes: a regular synchronized, an overall chaotic, and an intermittent bursting regime. The intermittent bursting regime is characterized by intervals of regular flows, which are quite insensitive to external stimuli, interceded by chaotic bursts that respond sensitively to input signals. We discuss these findings in the context of self-organized information processing and critical brain dynamics.

scholarly journals Investigating age-related changes in anterior and posterior neural activity throughout the information processing stream

2015 ◽  
Vol 99 ◽  
pp. 118-127 ◽  
Author(s):  
Brittany R. Alperin ◽  
Erich S. Tusch ◽  
Katherine K. Mott ◽  
Phillip J. Holcomb ◽  
Kirk R. Daffner
Keyword(s):  
Information Processing ◽  
Neural Activity ◽  
Processing Stream ◽  
Age Related ◽  
Age Related Changes

Computational Diversity in a Formal Model of the Insect Olfactory Macroglomerulus

1993 ◽  
Vol 5 (2) ◽  
pp. 228-241 ◽  
Author(s):  
C. Linster ◽  
C. Masson ◽  
M. Kerszberg ◽  
L. Personnaz ◽  
G. Dreyfus
Keyword(s):  
Information Processing ◽  
Olfactory System ◽  
Formal Model ◽  
Response Patterns ◽  
Olfactory Information ◽  
Temporal Aspects ◽  
Stochastic Parameters ◽  
Formal Network ◽  
Random Connectivity

We present a model of the specialist olfactory system of selected moth species and the cockroach. The model is built in a semirandom fashion, constrained by biological (physiological and anatomical) data. We propose a classification of the response patterns of individual neurons, based on the temporal aspects of the observed responses. Among the observations made in our simulations a number relate to data about olfactory information processing reported in the literature; others may serve as predictions and as guidelines for further investigations. We discuss the effect of the stochastic parameters of the model on the observed model behavior and on the ability of the model to extract features of the input stimulation. We conclude that a formal network, built with random connectivity, can suffice to reproduce and to explain many aspects of olfactory information processing at the first level of the specialist olfactory system of insects.

Calcium Imaging of Neural Activity in the Olfactory System of Drosophila

2012 ◽  
pp. 43-70 ◽  
Author(s):  
Antonia Strutz ◽  
Thomas Völler ◽  
Thomas Riemensperger ◽  
André Fiala ◽  
Silke Sachse
Keyword(s):  
Neural Activity ◽  
Calcium Imaging ◽  
Olfactory System
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