Characterization of Predictive Behavior of a Retina by Mutual Information

Chen, Kevin Sean; Chen, Chun-Chung; Chan, C. K.

doi:10.3389/fncom.2017.00066

Cited by 17 publications

(8 citation statements)

References 18 publications

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“…Our choice of a tank with a length of 21.6cm was based on these observations to optimize the interactions between the two fish. To investigate the temporal relation between U and V , we make use of the TLMI [9] between U and V which is defined as:…”

Section: Resultsmentioning

confidence: 99%

Detection of Anticipatory Dynamics Between a Pair of Zebrafish

Chen¹,

Lin²,

Hsu³

et al. 2021

Preprint

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Trajectories from a pair of interacting zebrafish are used to test for the existence of anticipatory dynamics in natural systems. Anticipatory dynamics (AD) is unusual in that causal events are not necessarily ordered by their temporal order. However, their causal order can still be established if the direction of information flow (DIF) is known. In order to obtain DIF between trajectories of the two fish, we have made use of the difference of the transfer entropy between the trajectories with a history length established by experiments with known DIF. Our experimental results indicate that AD can be observed much more often in fish pairs of different genders. The use of DIF to determine causal order is further verified by the simulation of two chaotic Lorenz oscillators with anticipatory coupling; mimicking the interaction between the fish. Our simulation results further suggest that the two fish are interacting with their own internal dynamics, not by adaptation.

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Section: Resultsmentioning

confidence: 99%

Detection of Anticipatory Dynamics Between a Pair of Zebrafish

Chen¹,

Lin²,

Hsu³

et al. 2021

Preprint

Self Cite

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“…The efficient coding principle focuses on how the nervous system extracts predictive information from environmental stimuli. Both theories have been supported by experimental evidence, primarily in the visual and auditory systems [9][10][11][12] .…”

Section: Sources Of Predictive Information In Dynamical Neural Networkmentioning

confidence: 89%

Sources of predictive information in dynamical neural networks

Candadai¹,

Izquierdo²

2020

Sci Rep

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Behavior involves the ongoing interaction between an organism and its environment. One of the prevailing theories of adaptive behavior is that organisms are constantly making predictions about their future environmental stimuli. However, how they acquire that predictive information is still poorly understood. Two complementary mechanisms have been proposed: predictions are generated from an agent’s internal model of the world or predictions are extracted directly from the environmental stimulus. In this work, we demonstrate that predictive information, measured using bivariate mutual information, cannot distinguish between these two kinds of systems. Furthermore, we show that predictive information cannot distinguish between organisms that are adapted to their environments and random dynamical systems exposed to the same environment. To understand the role of predictive information in adaptive behavior, we need to be able to identify where it is generated. To do this, we decompose information transfer across the different components of the organism-environment system and track the flow of information in the system over time. To validate the proposed framework, we examined it on a set of computational models of idealized agent-environment systems. Analysis of the systems revealed three key insights. First, predictive information, when sourced from the environment, can be reflected in any agent irrespective of its ability to perform a task. Second, predictive information, when sourced from the nervous system, requires special dynamics acquired during the process of adapting to the environment. Third, the magnitude of predictive information in a system can be different for the same task if the environmental structure changes.

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“…Previous experimental [Schwartz and Berry 2nd, 2008, Werner et al, 2008, Deshmukh, 2015] and computational [Maheswaranathan et al, 2019, Tanaka et al, 2019, Chen et al, 2017] studies sought to understand the neural mechanisms underlying the OSR. However, there remains some controversy over which of the proposed theories could explain all of the experimentally observed features of the OSR, such as e.g.…”

Section: Discussionmentioning

confidence: 99%

Encoding surprise by retinal ganglion cells

Despotovic

Joffrois

Marre

et al. 2022

Preprint

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The efficient coding hypothesis posits that early sensory neurons transmit maximal information about sensory stimuli, given internal constraints. A central prediction of this theory is that neurons should preferentially encode stimuli that are most surprising. Previous studies suggest this may be the case in early visual areas, where many neurons respond strongly to rare or surprising stimuli. For example, previous research showed that when presented with a rhythmic sequence of full-field flashes, many retinal ganglion cells (RGCs) respond strongly at the instance the flash sequence stops, and when another flash would be expected. This phenomenon is called the `omitted stimulus response'. However, it is not known whether the responses of these cells varies in a graded way depending on the level of stimulus surprise. To investigate this, we presented retinal neurons with extended sequences of stochastic flashes. With this stimulus, the surprise associated with a particular flash/silence, could be quantified analytically, and varied in a graded manner depending on the previous sequences of flashes and silences. Interestingly, we found that RGC responses could be well explained by a simple normative model, which described how they optimally combined their prior expectations and recent stimulus history, so as to encode surprise. Further, much of the diversity in RGC responses could be explained by the model, due to the different prior expectations that different neurons had about the stimulus statistics. These results suggest that even as early as the retina many cells encode surprise, relative to their own, internally generated expectations.

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Characterization of Predictive Behavior of a Retina by Mutual Information

Cited by 17 publications

References 18 publications

Detection of Anticipatory Dynamics Between a Pair of Zebrafish

Detection of Anticipatory Dynamics Between a Pair of Zebrafish

Sources of predictive information in dynamical neural networks

Encoding surprise by retinal ganglion cells

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