The Experts below are selected from a list of 14049 Experts worldwide ranked by ideXlab platform

Kenji Doya - One of the best experts on this subject based on the ideXlab platform.

  • Near-Saddle-Node Bifurcation Behavior as Dynamics in Working Memory for Goal-Directed Behavior
    Neural Computation, 1998
    Co-Authors: Hiroyuki Nakahara*, Kenji Doya
    Abstract:

    In consideration of working memory as a means for Goal-Directed Behavior in nonstationary environments, we argue that the dynamics of working memory should satisfy two opposing demands: long-term maintenance and quick transition. These two characteristics are contradictory within the linear domain. We propose the near-saddle-node bifurcation Behavior of a sigmoidal unit with a self-connection as a candidate of the dynamical mechanism that satisfies both of these demands. It is shown in evolutionary programming experiments that the near-saddle-node bifurcation Behavior can be found in recurrent networks optimized for a task that requires efficient use of working memory. The result suggests that the near-saddle-node bifurcation Behavior may be a functional necessity for survival in nonstationary environments.

  • Near-saddle-node bifurcation Behavior as dynamics in working memory for Goal-Directed Behavior
    Neural Computation, 1998
    Co-Authors: Hiroyuki Nakahara, Kenji Doya
    Abstract:

    In consideration of working memory as a means for Goal-Directed Behavior in nonstationary environments, we argue that the dynamics of working memory should satisfy two opposing demands: long-term maintenance and quick transition. These two characteristics are contradictory within the linear domain. We propose the near-saddle-node bifurcation Behavior of a sigmoidal unit with a self-connection as a candidate of the dynamical mechanism that satise es both of these demands. It is shown in evolutionary programming experiments that the near-saddle-node bifurcation Behavior can be found in recurrent networks optimized for a task that requires efe cient use of working memory. The result suggests that the near-saddle-node bifurcation Behavior may be a functional necessity for survival in nonstationary environments.

Matthew M. Botvinick - One of the best experts on this subject based on the ideXlab platform.

  • Abstract Structural Representations of Goal-Directed Behavior
    Psychological Science, 2010
    Co-Authors: Kachina Allen, Steven Ibara, Amy Seymour, Natalia Cordova, Matthew M. Botvinick
    Abstract:

    Linguistic theory holds that the structure of a sentence can be described in abstract syntactic terms, independent of the specific words the sentence contains. Nonlinguistic Behavior, including Goal-Directed action, is also theorized to have an underlying structural, or “syntactic,” organization. We propose that purposive action sequences are represented cognitively in terms of a means-ends parse, which is a formal specification of how actions fit together to accomplish desired outcomes. To test this theory, we leveraged the phenomenon of structural priming in two experiments. As predicted, participants read sentences describing action sequences faster when these sentences were presented amid other sentences sharing the same parse. Results from a second experiment indicate that the underlying representations relevant to observed action sequences are not strictly tied to language processing. Our results suggest that the structure of Goal-Directed Behavior may be represented abstractly, independently of spe...

  • Abstract Structural Representations of Goal-Directed Behavior
    Psychological Science, 2010
    Co-Authors: Kachina Allen, Steven Ibara, Amy Seymour, Natalia Cordova, Matthew M. Botvinick
    Abstract:

    Linguistic theory holds that the structure of a sentence can be described in abstract syntactic terms, independent of the specific words the sentence contains. Nonlinguistic Behavior, including Goal-Directed action, is also theorized to have an underlying structural, or "syntactic," organization. We propose that purposive action sequences are represented cognitively in terms of a means-ends parse, which is a formal specification of how actions fit together to accomplish desired outcomes. To test this theory, we leveraged the phenomenon of structural priming in two experiments. As predicted, participants read sentences describing action sequences faster when these sentences were presented amid other sentences sharing the same parse. Results from a second experiment indicate that the underlying representations relevant to observed action sequences are not strictly tied to language processing. Our results suggest that the structure of Goal-Directed Behavior may be represented abstractly, independently of specific actions and goals, just as linguistic syntax is thought to stand independent of other levels of representation.

Karl Deisseroth - One of the best experts on this subject based on the ideXlab platform.

  • global representations of goal directed Behavior in distinct cell types of mouse neocortex
    Neuron, 2017
    Co-Authors: William E Allen, Isaac Kauvar, Michael Z Chen, Ethan B Richman, Samuel Yang, Ken Chan, Viviana Gradinaru, Benjamin E Deverman, Karl Deisseroth
    Abstract:

    The successful planning and execution of adaptive Behaviors in mammals may require long-range coordination of neural networks throughout cerebral cortex. The neuronal implementation of signals that could orchestrate cortex-wide activity remains unclear. Here, we develop and apply methods for cortex-wide Ca^(2+) imaging in mice performing decision-making Behavior and identify a global cortical representation of task engagement encoded in the activity dynamics of both single cells and superficial neuropil distributed across the majority of dorsal cortex. The activity of multiple molecularly defined cell types was found to reflect this representation with type-specific dynamics. Focal optogenetic inhibition tiled across cortex revealed a crucial role for frontal cortex in triggering this cortex-wide phenomenon; local inhibition of this region blocked both the cortex-wide response to task-initiating cues and the voluntary Behavior. These findings reveal cell-type-specific processes in cortex for globally representing Goal-Directed Behavior and identify a major cortical node that gates the global broadcast of task-related information.

Kachina Allen - One of the best experts on this subject based on the ideXlab platform.

  • Abstract Structural Representations of Goal-Directed Behavior
    Psychological Science, 2010
    Co-Authors: Kachina Allen, Steven Ibara, Amy Seymour, Natalia Cordova, Matthew M. Botvinick
    Abstract:

    Linguistic theory holds that the structure of a sentence can be described in abstract syntactic terms, independent of the specific words the sentence contains. Nonlinguistic Behavior, including Goal-Directed action, is also theorized to have an underlying structural, or “syntactic,” organization. We propose that purposive action sequences are represented cognitively in terms of a means-ends parse, which is a formal specification of how actions fit together to accomplish desired outcomes. To test this theory, we leveraged the phenomenon of structural priming in two experiments. As predicted, participants read sentences describing action sequences faster when these sentences were presented amid other sentences sharing the same parse. Results from a second experiment indicate that the underlying representations relevant to observed action sequences are not strictly tied to language processing. Our results suggest that the structure of Goal-Directed Behavior may be represented abstractly, independently of spe...

  • Abstract Structural Representations of Goal-Directed Behavior
    Psychological Science, 2010
    Co-Authors: Kachina Allen, Steven Ibara, Amy Seymour, Natalia Cordova, Matthew M. Botvinick
    Abstract:

    Linguistic theory holds that the structure of a sentence can be described in abstract syntactic terms, independent of the specific words the sentence contains. Nonlinguistic Behavior, including Goal-Directed action, is also theorized to have an underlying structural, or "syntactic," organization. We propose that purposive action sequences are represented cognitively in terms of a means-ends parse, which is a formal specification of how actions fit together to accomplish desired outcomes. To test this theory, we leveraged the phenomenon of structural priming in two experiments. As predicted, participants read sentences describing action sequences faster when these sentences were presented amid other sentences sharing the same parse. Results from a second experiment indicate that the underlying representations relevant to observed action sequences are not strictly tied to language processing. Our results suggest that the structure of Goal-Directed Behavior may be represented abstractly, independently of specific actions and goals, just as linguistic syntax is thought to stand independent of other levels of representation.

Poramate Manoonpong - One of the best experts on this subject based on the ideXlab platform.

  • SMC - Neural Combinatorial Learning of Goal-Directed Behavior with Reservoir Critic and Reward Modulated Hebbian Plasticity
    2013 IEEE International Conference on Systems Man and Cybernetics, 2013
    Co-Authors: Sakyasingha Dasgupta, Jun Morimoto, Florentin Wörgötter, Poramate Manoonpong
    Abstract:

    Learning of Goal-Directed Behaviors in biological systems is broadly based on associations between conditional and unconditional stimuli. This can be further classified as classical conditioning (correlation-based learning) and operant conditioning (reward-based learning). Although traditionally modeled as separate learning systems in artificial agents, numerous animal experiments point towards their co-operative role in Behavioral learning. Based on this concept, the recently introduced framework of neural combinatorial learning combines the two systems where both the systems run in parallel to guide the overall learned Behavior. Such a combinatorial learning demonstrates a faster and efficient learner. In this work, we further improve the framework by applying a reservoir computing network (RC) as an adaptive critic unit and reward modulated Hebbian plasticity. Using a mobile robot system for Goal-Directed Behavior learning, we clearly demonstrate that the reservoir critic outperforms traditional radial basis function (RBF) critics in terms of stability of convergence and learning time. Furthermore the temporal memory in RC allows the system to learn partially observable markov decision process scenario, in contrast to a memory less RBF critic.

  • Neural combinatorial learning of Goal-Directed Behavior with reservoir critic and reward modulated hebbian plasticity
    Proceedings - 2013 IEEE International Conference on Systems Man and Cybernetics SMC 2013, 2013
    Co-Authors: Sakyasingha Dasgupta, Jun Morimoto, Florentin Wörgötter, Poramate Manoonpong
    Abstract:

    Learning of Goal-Directed Behaviors in biological systems is broadly based on associations between conditional and unconditional stimuli. This can be further classified as classical conditioning (correlation-based learning) and operant conditioning (reward-based learning). Although traditionally modeled as separate learning systems in artificial agents, numerous animal experiments point towards their co-operative role in Behavioral learning. Based on this concept, the recently introduced framework of neural combinatorial learning combines the two systems where both the systems run in parallel to guide the overall learned Behavior. Such a combinatorial learning demonstrates a faster and efficient learner. In this work, we further improve the framework by applying a reservoir computing network (RC) as an adaptive critic unit and reward modulated Hebbian plasticity. Using a mobile robot system for Goal-Directed Behavior learning, we clearly demonstrate that the reservoir critic outperforms traditional radial basis function (RBF) critics in terms of stability of convergence and learning time. Furthermore the temporal memory in RC allows the system to learn partially observable markov decision process scenario, in contrast to a memory less RBF critic.