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When and How-Long: A Unified Approach for Time Perception.

Maniadakis M, Trahanias P - Front Psychol (2016)

Bottom Line: This information, although rather standard in humans, is largely missing from artificial cognitive systems.In this work we consider how a time perception model that is based on neural networks and the Striatal Beat Frequency (SBF) theory is extended in a way that besides the duration of events, facilitates the encoding of the time of occurrence in memory.The extended model is capable to support skills assumed in temporal cognition and answer time-related questions about the unfolded events.

View Article: PubMed Central - PubMed

Affiliation: Computational Vision and Robotics Laboratory, Institute of Computer Science, Foundation for Research and Technology Hellas Heraklion, Greece.

ABSTRACT
The representation of the environment assumes the encoding of four basic dimensions in the brain, that is the 3D space and time. The vital role of time for cognition is a topic that recently attracted increasing research interest. Surprisingly, the scientific community investigating mind-time interactions has mainly focused on interval timing, paying less attention on the encoding and processing of distant moments. The present work highlights two basic capacities that are necessary for developing temporal cognition in artificial systems. In particular, the seamless integration of agents in the environment assumes they are able to consider when events have occurred and how-long they have lasted. This information, although rather standard in humans, is largely missing from artificial cognitive systems. In this work we consider how a time perception model that is based on neural networks and the Striatal Beat Frequency (SBF) theory is extended in a way that besides the duration of events, facilitates the encoding of the time of occurrence in memory. The extended model is capable to support skills assumed in temporal cognition and answer time-related questions about the unfolded events.

No MeSH data available.


The structure of the first model of interval timing. A recurrent TimeSense module blends oscillatory signals to develop the filling of flowing time, properly formulated to enable interval timing in the modules t-Duration1 …t-Duration6 that are also fed by the external tone signal and the id of the perceived event.
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Figure 1: The structure of the first model of interval timing. A recurrent TimeSense module blends oscillatory signals to develop the filling of flowing time, properly formulated to enable interval timing in the modules t-Duration1 …t-Duration6 that are also fed by the external tone signal and the id of the perceived event.

Mentions: The structure of the neural network model is shown in Figure 1. In short Continuous Time Recurrent Neural Networks (CTRNNs) are used as modules to develop a composite cognitive system. CTRNNs represent knowledge in terms of internal neurodynamic attractors and it is therefore particularly appropriate for implementing cognitive capacity that is inherently continuous, similar to time perception. The neurons implementing CTRNN components are governed by the standard leaky integrator equation:(1)dγidt=1τ(−γi+∑k = 1RwiksIk+∑m = 1NwimpAm)where γi is the state (cell potential) of the i-th neuron. All neurons in a network share the same time constant τ = 0.25 in order to avoid explicit differentiation in the functionality of CTRNN parts. The state of each neuron is updated according to external sensory input I weighted by ws, and the activity of presynaptic neurons A weighted by wp. After estimating the state of neurons based on the above equation, the activation of the i-th neuron is calculated by the non-linear sigmoid function according to:(2)Ai=11+e−(γi−θi)where θi is the activation bias applied on the i-th neuron. The model considered in the present study assumes 16 neurons for the building blocks tSen1, tSen2, and 2 neurons for the blocks implementing t-Duration1, …, t-Duration6. A hierarchical coevolutionary procedure is used as a mechanism for tuning CTRNN modules, specifying synaptic weights and activation bias of neurons.


When and How-Long: A Unified Approach for Time Perception.

Maniadakis M, Trahanias P - Front Psychol (2016)

The structure of the first model of interval timing. A recurrent TimeSense module blends oscillatory signals to develop the filling of flowing time, properly formulated to enable interval timing in the modules t-Duration1 …t-Duration6 that are also fed by the external tone signal and the id of the perceived event.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4814468&req=5

Figure 1: The structure of the first model of interval timing. A recurrent TimeSense module blends oscillatory signals to develop the filling of flowing time, properly formulated to enable interval timing in the modules t-Duration1 …t-Duration6 that are also fed by the external tone signal and the id of the perceived event.
Mentions: The structure of the neural network model is shown in Figure 1. In short Continuous Time Recurrent Neural Networks (CTRNNs) are used as modules to develop a composite cognitive system. CTRNNs represent knowledge in terms of internal neurodynamic attractors and it is therefore particularly appropriate for implementing cognitive capacity that is inherently continuous, similar to time perception. The neurons implementing CTRNN components are governed by the standard leaky integrator equation:(1)dγidt=1τ(−γi+∑k = 1RwiksIk+∑m = 1NwimpAm)where γi is the state (cell potential) of the i-th neuron. All neurons in a network share the same time constant τ = 0.25 in order to avoid explicit differentiation in the functionality of CTRNN parts. The state of each neuron is updated according to external sensory input I weighted by ws, and the activity of presynaptic neurons A weighted by wp. After estimating the state of neurons based on the above equation, the activation of the i-th neuron is calculated by the non-linear sigmoid function according to:(2)Ai=11+e−(γi−θi)where θi is the activation bias applied on the i-th neuron. The model considered in the present study assumes 16 neurons for the building blocks tSen1, tSen2, and 2 neurons for the blocks implementing t-Duration1, …, t-Duration6. A hierarchical coevolutionary procedure is used as a mechanism for tuning CTRNN modules, specifying synaptic weights and activation bias of neurons.

Bottom Line: This information, although rather standard in humans, is largely missing from artificial cognitive systems.In this work we consider how a time perception model that is based on neural networks and the Striatal Beat Frequency (SBF) theory is extended in a way that besides the duration of events, facilitates the encoding of the time of occurrence in memory.The extended model is capable to support skills assumed in temporal cognition and answer time-related questions about the unfolded events.

View Article: PubMed Central - PubMed

Affiliation: Computational Vision and Robotics Laboratory, Institute of Computer Science, Foundation for Research and Technology Hellas Heraklion, Greece.

ABSTRACT
The representation of the environment assumes the encoding of four basic dimensions in the brain, that is the 3D space and time. The vital role of time for cognition is a topic that recently attracted increasing research interest. Surprisingly, the scientific community investigating mind-time interactions has mainly focused on interval timing, paying less attention on the encoding and processing of distant moments. The present work highlights two basic capacities that are necessary for developing temporal cognition in artificial systems. In particular, the seamless integration of agents in the environment assumes they are able to consider when events have occurred and how-long they have lasted. This information, although rather standard in humans, is largely missing from artificial cognitive systems. In this work we consider how a time perception model that is based on neural networks and the Striatal Beat Frequency (SBF) theory is extended in a way that besides the duration of events, facilitates the encoding of the time of occurrence in memory. The extended model is capable to support skills assumed in temporal cognition and answer time-related questions about the unfolded events.

No MeSH data available.