scholarly journals The anticipation of events in time

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Matthias Grabenhorst ◽  
Georgios Michalareas ◽  
Laurence T. Maloney ◽  
David Poeppel
Keyword(s):  
Probability Density ◽  
Time Estimation ◽  
Elapsed Time ◽  
Event Probability ◽  
Cortical Hierarchy ◽  
Multiple Levels ◽  
Stable Computation ◽  
Uncertainty Parameters ◽  
Neuronal Computation ◽  
Perceptual Systems

AbstractHumans anticipate events signaled by sensory cues. It is commonly assumed that two uncertainty parameters modulate the brain's capacity to predict: the hazard rate (HR) of event probability and the uncertainty in time estimation which increases with elapsed time. We investigate both assumptions by presenting event probability density functions (PDFs) in each of three sensory modalities. We show that perceptual systems use the reciprocal PDF and not the HR to model event probability density. We also demonstrate that temporal uncertainty does not necessarily grow with elapsed time but can also diminish, depending on the event PDF. Previous research identified neuronal activity related to event probability in multiple levels of the cortical hierarchy (sensory (V4), association (LIP), motor and other areas) proposing the HR as an elementary neuronal computation. Our results—consistent across vision, audition, and somatosensation—suggest that the neurobiological implementation of event anticipation is based on a different, simpler and more stable computation than HR: the reciprocal PDF of events in time.

scholarly journals The Anticipation of Events in Time

2019 ◽  
Author(s):  
Matthias Grabenhorst ◽  
Georgios Michalareas ◽  
Laurence T. Maloney ◽  
David Poeppel
Keyword(s):  
Probability Density ◽  
Hazard Rate ◽  
Time Estimation ◽  
Temporal Uncertainty ◽  
Elapsed Time ◽  
Event Probability ◽  
Sensory Modalities ◽  
Stable Computation ◽  
Uncertainty Parameters ◽  
Neuronal Computation

AbstractHumans use sensory input to anticipate events. The brain’s capacity to predict cues in time is commonly assumed to be modulated by two uncertainty parameters, the hazard rate (HR) of event probability and the uncertainty in time estimation, which increases with elapsed time. We investigate both assumptions by manipulating event probability density functions (PDF) in three sensory modalities. First we show, contrary to expectation, that perceptual systems use the reciprocal PDF – and not the HR – to model event probability density. Next we demonstrate that temporal uncertainty does not necessarily grow with elapsed time but also diminishes, depending on the event PDF. Finally we show that reaction time (RT) distributions comprise modality-specific and modality-independent components, the latter likely reflecting similarity in processing of probability density across sensory modalities. The results are consistent across vision, audition, and somatosensation, indicating that probability density is more fundamental than hazard rate in terms of the neural operations determining event anticipation and temporal uncertainty. Previous research identified neuronal activitity related to event probability in multiple levels of the cortical hierarchy such as early and higher sensory (V1, V4), association (LIP), motor and other areas. This work proposed that the elementary neuronal computation in estimation of probability across time is the HR. In contrast, our results suggest that the neurobiological implementation of probability estimation is based on a different, much simpler and more stable computation than HR: the reciprocal PDF of events in time.

Countercurrents in Matter and Mind

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 177-177
Author(s):  
S Hochstein ◽  
M Ahissar
Keyword(s):  
Perceptual Learning ◽  
Sensory Information ◽  
External World ◽  
Learning Effects ◽  
Efficient Manner ◽  
Cortical Hierarchy ◽  
Internal Information ◽  
Task Conditions ◽  
Perceptual Systems ◽  
The Brain

An especially efficient manner of transmission of matter or energy, employed by numerous biological systems, is the countercurrent mechanism. Transfer is effected between two closely aligned streaming currents where the currents flow in opposite directions. Final transfer can be 100% rather than the 50% ceiling of concurrent streams. We now report that perceptual systems may employ a similar mechanism. Information derived from the external world by the senses is transferred to the perceptual system in a hierarchy of processing areas. Simultaneously, this information is intermixed with previously stored internal information. The degree of mixture of previously existing information, with new, unprocessed information is titrated along the hierarchy. The brain may tap various points along the countercurrents to obtain the mixtures required for different tasks. Perceptual learning affects first the inner levels of this cortical hierarchy and only later descends to their input levels to achieve better performance with more difficult task conditions. Learning effects discussed at ECVP over the last two decades are reviewed in the light of this cortical scheme. Many seemingly contradictory findings are reconciled when put in the framework of countercurrent streams which respectively process sensory information and guide perceptual learning.

scholarly journals How to Be Proactive About Interference

2012 ◽  
Vol 23 (5) ◽  
pp. 453-458 ◽  
Author(s):  
Anthony A. Wright ◽  
Jeffrey S. Katz ◽  
Wei Ji Ma
Keyword(s):  
Proactive Interference ◽  
Signal Detection Theory ◽  
Time Estimation ◽  
Model System ◽  
Estimation Model ◽  
Recent Trial ◽  
Current Trial ◽  
Elapsed Time ◽  
Different Types ◽  
Two Delays

Processes of proactive interference were explored using the pigeon as a model system of memory. This study shows that proactive interference extends back in time at least 16 trials (and as many minutes), revealing a continuum of interference and providing a framework for studying memory. Pigeons were tested in a delayed same/different task containing trial-unique pictures. On interference trials, sample pictures from previous trials reappeared as test pictures on different trials. Proactive-interference functions showed greatest interference from the most recent trial and with the longer of two delays (10 s vs. 1 s). These interference functions are accounted for by a time-estimation model based on signal detection theory. The model predicts that accuracy at test is determined solely by the ratio of the elapsed time since the offset of the current-trial sample to the elapsed time since the offset of the interfering sample. Implications for comparing memory of different species and different types of memory (e.g., familiarity vs. recollection) are discussed.

scholarly journals An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity

2017 ◽  
Vol 29 (5) ◽  
pp. 1229-1262 ◽  
Author(s):  
James C. R. Whittington ◽  
Rafal Bogacz
Keyword(s):  
Synaptic Plasticity ◽  
Predictive Coding ◽  
Complex Function ◽  
External Control ◽  
Backpropagation Algorithm ◽  
Cortical Networks ◽  
Hebbian Plasticity ◽  
Cortical Hierarchy ◽  
Multiple Levels ◽  
Efficient Learning

To efficiently learn from feedback, cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error backpropagation algorithm. However, in this algorithm, the change in synaptic weights is a complex function of weights and activities of neurons not directly connected with the synapse being modified, whereas the changes in biological synapses are determined only by the activity of presynaptic and postsynaptic neurons. Several models have been proposed that approximate the backpropagation algorithm with local synaptic plasticity, but these models require complex external control over the network or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning fully autonomously, employing only simple local Hebbian plasticity. Furthermore, for certain parameters, the weight change in the predictive coding model converges to that of the backpropagation algorithm. This suggests that it is possible for cortical networks with simple Hebbian synaptic plasticity to implement efficient learning algorithms in which synapses in areas on multiple levels of hierarchy are modified to minimize the error on the output.

scholarly journals Scalable representation of time in the hippocampus

2021 ◽  
Vol 7 (6) ◽  
pp. eabd7013 ◽  
Author(s):  
Akihiro Shimbo ◽  
Ei-Ichi Izawa ◽  
Shigeyoshi Fujisawa
Keyword(s):  
Time Estimation ◽  
Time Intervals ◽  
Elapsed Time ◽  
Temporal Representation ◽  
Temporal Relationships ◽  
Phase Precession ◽  
Temporal Structures ◽  
Theta Phase ◽  
Theta Phase Precession ◽  
Representation Of Time

Hippocampal “time cells” encode specific moments of temporally organized experiences that may support hippocampal functions for episodic memory. However, little is known about the reorganization of the temporal representation of time cells during changes in temporal structures of episodes. We investigated CA1 neuronal activity during temporal bisection tasks, in which the sets of time intervals to be discriminated were designed to be extended or contracted across the blocks of trials. Assemblies of neurons encoded elapsed time during the interval, and the representation was scaled when the set of interval times was varied. Theta phase precession and theta sequences of time cells were also scalable, and the fine temporal relationships were preserved between pairs in theta cycles. Moreover, theta sequences reflected the rats’ decisions on the basis of their time estimation. These findings demonstrate that scalable features of time cells may support the capability of flexible temporal representation for memory formation.

scholarly journals Confabulations on Time: Relationship between Confabulations and Timing Deviations in Alzheimer’s Disease

2020 ◽  
Vol 35 (4) ◽  
pp. 377-384 ◽  
Author(s):  
Mohamad El Haj ◽  
Frank Larøi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Time Perception ◽  
Time Estimation ◽  
Time Intervals ◽  
Ongoing Activity ◽  
Elapsed Time ◽  
Verbal Estimation ◽  
Positive Correlations ◽  
The Relationship

Abstract Objectives We investigated the relationship between confabulations and the ability to process chronological characteristics of memories in Alzheimer’s Disease (AD). Methods We evaluated provoked confabulations, spontaneous confabulations, and time perception in 31 AD patients. We evaluated provoked confabulations with questions probing general and personal knowledge. We evaluated spontaneous confabulations with a scale rated by nursing and medical staff. Regarding time perception, we invited the participants to perform a simple ongoing activity (i.e., deciding whether words were abstract or concrete), in order to provide a verbal estimation of the elapsed time intervals. Results We observed significant positive correlations between provoked/spontaneous confabulations and deviations in time estimation on the time perception task. Conclusions These findings demonstrate a relationship between confabulations in AD and difficulties in processing the chronological characteristics of elapsed events.

Time Estimation by Hyperactive and Normal Children

1977 ◽  
Vol 44 (3) ◽  
pp. 787-790 ◽  
Author(s):  
Betty Cappella ◽  
J. Ronald Gentile ◽  
Daniel B. Juliano
Keyword(s):  
Time Estimation ◽  
Main Study ◽  
Time Intervals ◽  
Normal Children ◽  
Elapsed Time

In two studies [a pilot (12 7- to 10-yr.-olds) and a main study (100 8- to 12-yr.-olds)] hyperactive and normal children were compared on the ability to estimate time intervals ranging from 7 to 60 sec. The differences between estimated and elapsed time were larger for hyperactives than for normals, with the differences between the groups increasing with the length of the interval to be estimated.

scholarly journals Recruitment of inhibition and excitation across mouse visual cortex depends on the hierarchy of interconnecting areas

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Rinaldo David D'Souza ◽  
Andrew Max Meier ◽  
Pawan Bista ◽  
Quanxin Wang ◽  
Andreas Burkhalter
Keyword(s):  
Pyramidal Neurons ◽  
Gain Control ◽  
Sensory Stimuli ◽  
Visual Areas ◽  
Cortical Hierarchy ◽  
Flow Through ◽  
Multiple Levels ◽  
Mouse Visual Cortex ◽  
Higher Visual Areas ◽  
Feedback Pathway

Diverse features of sensory stimuli are selectively processed in distinct brain areas. The relative recruitment of inhibitory and excitatory neurons within an area controls the gain of neurons for appropriate stimulus coding. We examined how such a balance of inhibition and excitation is differentially recruited across multiple levels of a cortical hierarchy by mapping the locations and strengths of synaptic inputs to pyramidal and parvalbumin (PV)-expressing neurons in feedforward and feedback pathways interconnecting primary (V1) and two higher visual areas. While interareal excitation was stronger in PV than in pyramidal neurons in all layer 2/3 pathways, we observed a gradual scaling down of the inhibition/excitation ratio from the most feedforward to the most feedback pathway. Our results indicate that interareal gain control depends on the hierarchical position of the source and the target, the direction of information flow through the network, and the laminar location of target neurons.

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