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  1. AU="Sahani, Maneesh"
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  1. Artikel ; Online: Residual dynamics resolves recurrent contributions to neural computation.

    Galgali, Aniruddh R / Sahani, Maneesh / Mante, Valerio

    Nature neuroscience

    2023  Band 26, Heft 2, Seite(n) 326–338

    Abstract: Relating neural activity to behavior requires an understanding of how neural computations arise from the coordinated dynamics of distributed, recurrently connected neural populations. However, inferring the nature of recurrent dynamics from partial ... ...

    Abstract Relating neural activity to behavior requires an understanding of how neural computations arise from the coordinated dynamics of distributed, recurrently connected neural populations. However, inferring the nature of recurrent dynamics from partial recordings of a neural circuit presents considerable challenges. Here we show that some of these challenges can be overcome by a fine-grained analysis of the dynamics of neural residuals-that is, trial-by-trial variability around the mean neural population trajectory for a given task condition. Residual dynamics in macaque prefrontal cortex (PFC) in a saccade-based perceptual decision-making task reveals recurrent dynamics that is time dependent, but consistently stable, and suggests that pronounced rotational structure in PFC trajectories during saccades is driven by inputs from upstream areas. The properties of residual dynamics restrict the possible contributions of PFC to decision-making and saccade generation and suggest a path toward fully characterizing distributed neural computations with large-scale neural recordings and targeted causal perturbations.
    Mesh-Begriff(e) Animals ; Prefrontal Cortex ; Saccades ; Macaca
    Sprache Englisch
    Erscheinungsdatum 2023-01-12
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1420596-8
    ISSN 1546-1726 ; 1097-6256
    ISSN (online) 1546-1726
    ISSN 1097-6256
    DOI 10.1038/s41593-022-01230-2
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 4891: Hefte anzeigen Standort:
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  2. Artikel ; Online: Dynamics on the manifold: Identifying computational dynamical activity from neural population recordings.

    Duncker, Lea / Sahani, Maneesh

    Current opinion in neurobiology

    2021  Band 70, Seite(n) 163–170

    Abstract: The question of how the collective activity of neural populations gives rise to complex behaviour is fundamental to neuroscience. At the core of this question lie considerations about how neural circuits can perform computations that enable sensory ... ...

    Abstract The question of how the collective activity of neural populations gives rise to complex behaviour is fundamental to neuroscience. At the core of this question lie considerations about how neural circuits can perform computations that enable sensory perception, decision making, and motor control. It is thought that such computations are implemented through the dynamical evolution of distributed activity in recurrent circuits. Thus, identifying dynamical structure in neural population activity is a key challenge towards a better understanding of neural computation. At the same time, interpreting this structure in light of the computation of interest is essential for linking the time-varying activity patterns of the neural population to ongoing computational processes. Here, we review methods that aim to quantify structure in neural population recordings through a dynamical system defined in a low-dimensional latent variable space. We discuss advantages and limitations of different modelling approaches and address future challenges for the field.
    Mesh-Begriff(e) Nerve Net
    Sprache Englisch
    Erscheinungsdatum 2021-11-24
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't ; Review
    ZDB-ID 1078046-4
    ISSN 1873-6882 ; 0959-4388
    ISSN (online) 1873-6882
    ISSN 0959-4388
    DOI 10.1016/j.conb.2021.10.014
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Buch ; Online: Successor-Predecessor Intrinsic Exploration

    Yu, Changmin / Burgess, Neil / Sahani, Maneesh / Gershman, Sam

    2023  

    Abstract: Exploration is essential in reinforcement learning, particularly in environments where external rewards are sparse. Here we focus on exploration with intrinsic rewards, where the agent transiently augments the external rewards with self-generated ... ...

    Abstract Exploration is essential in reinforcement learning, particularly in environments where external rewards are sparse. Here we focus on exploration with intrinsic rewards, where the agent transiently augments the external rewards with self-generated intrinsic rewards. Although the study of intrinsic rewards has a long history, existing methods focus on composing the intrinsic reward based on measures of future prospects of states, ignoring the information contained in the retrospective structure of transition sequences. Here we argue that the agent can utilise retrospective information to generate explorative behaviour with structure-awareness, facilitating efficient exploration based on global instead of local information. We propose Successor-Predecessor Intrinsic Exploration (SPIE), an exploration algorithm based on a novel intrinsic reward combining prospective and retrospective information. We show that SPIE yields more efficient and ethologically plausible exploratory behaviour in environments with sparse rewards and bottleneck states than competing methods. We also implement SPIE in deep reinforcement learning agents, and show that the resulting agent achieves stronger empirical performance than existing methods on sparse-reward Atari games.
    Schlagwörter Computer Science - Machine Learning
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2023-05-24
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  4. Buch ; Online: Prediction under Latent Subgroup Shifts with High-Dimensional Observations

    Walker, William I. / Gretton, Arthur / Sahani, Maneesh

    2023  

    Abstract: We introduce a new approach to prediction in graphical models with latent-shift adaptation, i.e., where source and target environments differ in the distribution of an unobserved confounding latent variable. Previous work has shown that as long as " ... ...

    Abstract We introduce a new approach to prediction in graphical models with latent-shift adaptation, i.e., where source and target environments differ in the distribution of an unobserved confounding latent variable. Previous work has shown that as long as "concept" and "proxy" variables with appropriate dependence are observed in the source environment, the latent-associated distributional changes can be identified, and target predictions adapted accurately. However, practical estimation methods do not scale well when the observations are complex and high-dimensional, even if the confounding latent is categorical. Here we build upon a recently proposed probabilistic unsupervised learning framework, the recognition-parametrised model (RPM), to recover low-dimensional, discrete latents from image observations. Applied to the problem of latent shifts, our novel form of RPM identifies causal latent structure in the source environment, and adapts properly to predict in the target. We demonstrate results in settings where predictor and proxy are high-dimensional images, a context to which previous methods fail to scale.
    Schlagwörter Statistics - Machine Learning ; Computer Science - Machine Learning
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2023-06-23
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  5. Artikel ; Online: Editorial overview: Machine learning, big data, and neuroscience.

    Jonathan Pillow / Sahani, Maneesh

    Current opinion in neurobiology

    2019  Band 55, Seite(n) iii–iv

    Mesh-Begriff(e) Big Data ; Machine Learning ; Neurosciences
    Sprache Englisch
    Erscheinungsdatum 2019-06-04
    Erscheinungsland England
    Dokumenttyp Editorial ; Introductory Journal Article
    ZDB-ID 1078046-4
    ISSN 1873-6882 ; 0959-4388
    ISSN (online) 1873-6882
    ISSN 0959-4388
    DOI 10.1016/j.conb.2019.05.002
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 3260: Hefte anzeigen Standort:
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    Jg. 1995 - 2021: Lesesall (2.OG)
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  6. Artikel ; Online: Movement initiation and grasp representation in premotor and primary motor cortex mirror neurons.

    Jerjian, Steven Jack / Sahani, Maneesh / Kraskov, Alexander

    eLife

    2020  Band 9

    Abstract: Pyramidal tract neurons (PTNs) within macaque rostral ventral premotor cortex (F5) and (M1) provide direct input to spinal circuitry and are critical for skilled movement control. Contrary to initial hypotheses, they can also be active during action ... ...

    Abstract Pyramidal tract neurons (PTNs) within macaque rostral ventral premotor cortex (F5) and (M1) provide direct input to spinal circuitry and are critical for skilled movement control. Contrary to initial hypotheses, they can also be active during action observation, in the absence of any movement. A population-level understanding of this phenomenon is currently lacking. We recorded from single neurons, including identified PTNs, in (M1) (n = 187), and F5 (n = 115) as two adult male macaques executed, observed, or withheld (NoGo) reach-to-grasp actions. F5 maintained a similar representation of grasping actions during both execution and observation. In contrast, although many individual M1 neurons were active during observation, M1 population activity was distinct from execution, and more closely aligned to NoGo activity, suggesting this activity contributes to withholding of self-movement. M1 and its outputs may dissociate initiation of movement from representation of grasp in order to flexibly guide behaviour.
    Mesh-Begriff(e) Animals ; Electromyography ; Hand Strength/physiology ; Macaca mulatta ; Male ; Mirror Neurons/physiology ; Motor Cortex/cytology ; Motor Cortex/physiology ; Movement/physiology ; Reaction Time
    Sprache Englisch
    Erscheinungsdatum 2020-07-06
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2687154-3
    ISSN 2050-084X ; 2050-084X
    ISSN (online) 2050-084X
    ISSN 2050-084X
    DOI 10.7554/eLife.54139
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  7. Buch ; Online: Unsupervised representation learning with recognition-parametrised probabilistic models

    Walker, William I. / Soulat, Hugo / Yu, Changmin / Sahani, Maneesh

    2022  

    Abstract: We introduce a new approach to probabilistic unsupervised learning based on the recognition-parametrised model (RPM): a normalised semi-parametric hypothesis class for joint distributions over observed and latent variables. Under the key assumption that ... ...

    Abstract We introduce a new approach to probabilistic unsupervised learning based on the recognition-parametrised model (RPM): a normalised semi-parametric hypothesis class for joint distributions over observed and latent variables. Under the key assumption that observations are conditionally independent given latents, the RPM combines parametric prior and observation-conditioned latent distributions with non-parametric observation marginals. This approach leads to a flexible learnt recognition model capturing latent dependence between observations, without the need for an explicit, parametric generative model. The RPM admits exact maximum-likelihood learning for discrete latents, even for powerful neural-network-based recognition. We develop effective approximations applicable in the continuous-latent case. Experiments demonstrate the effectiveness of the RPM on high-dimensional data, learning image classification from weak indirect supervision; direct image-level latent Dirichlet allocation; and recognition-parametrised Gaussian process factor analysis (RP-GPFA) applied to multi-factorial spatiotemporal datasets. The RPM provides a powerful framework to discover meaningful latent structure underlying observational data, a function critical to both animal and artificial intelligence.
    Schlagwörter Computer Science - Machine Learning ; Statistics - Machine Learning
    Thema/Rubrik (Code) 006 ; 004
    Erscheinungsdatum 2022-09-12
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  8. Buch ; Online: Minimum Description Length Control

    Moskovitz, Ted / Kao, Ta-Chu / Sahani, Maneesh / Botvinick, Matthew M.

    2022  

    Abstract: We propose a novel framework for multitask reinforcement learning based on the minimum description length (MDL) principle. In this approach, which we term MDL-control (MDL-C), the agent learns the common structure among the tasks with which it is faced ... ...

    Abstract We propose a novel framework for multitask reinforcement learning based on the minimum description length (MDL) principle. In this approach, which we term MDL-control (MDL-C), the agent learns the common structure among the tasks with which it is faced and then distills it into a simpler representation which facilitates faster convergence and generalization to new tasks. In doing so, MDL-C naturally balances adaptation to each task with epistemic uncertainty about the task distribution. We motivate MDL-C via formal connections between the MDL principle and Bayesian inference, derive theoretical performance guarantees, and demonstrate MDL-C's empirical effectiveness on both discrete and high-dimensional continuous control tasks.
    Schlagwörter Computer Science - Machine Learning
    Erscheinungsdatum 2022-07-17
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  9. Buch ; Online: A Unified Theory of Dual-Process Control

    Moskovitz, Ted / Miller, Kevin / Sahani, Maneesh / Botvinick, Matthew M.

    2022  

    Abstract: Dual-process theories play a central role in both psychology and neuroscience, figuring prominently in fields ranging from executive control to reward-based learning to judgment and decision making. In each of these domains, two mechanisms appear to ... ...

    Abstract Dual-process theories play a central role in both psychology and neuroscience, figuring prominently in fields ranging from executive control to reward-based learning to judgment and decision making. In each of these domains, two mechanisms appear to operate concurrently, one relatively high in computational complexity, the other relatively simple. Why is neural information processing organized in this way? We propose an answer to this question based on the notion of compression. The key insight is that dual-process structure can enhance adaptive behavior by allowing an agent to minimize the description length of its own behavior. We apply a single model based on this observation to findings from research on executive control, reward-based learning, and judgment and decision making, showing that seemingly diverse dual-process phenomena can be understood as domain-specific consequences of a single underlying set of computational principles.
    Schlagwörter Quantitative Biology - Neurons and Cognition
    Erscheinungsdatum 2022-11-13
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  10. Buch ; Online: A First-Occupancy Representation for Reinforcement Learning

    Moskovitz, Ted / Wilson, Spencer R. / Sahani, Maneesh

    2021  

    Abstract: Both animals and artificial agents benefit from state representations that support rapid transfer of learning across tasks and which enable them to efficiently traverse their environments to reach rewarding states. The successor representation (SR), ... ...

    Abstract Both animals and artificial agents benefit from state representations that support rapid transfer of learning across tasks and which enable them to efficiently traverse their environments to reach rewarding states. The successor representation (SR), which measures the expected cumulative, discounted state occupancy under a fixed policy, enables efficient transfer to different reward structures in an otherwise constant Markovian environment and has been hypothesized to underlie aspects of biological behavior and neural activity. However, in the real world, rewards may move or only be available for consumption once, may shift location, or agents may simply aim to reach goal states as rapidly as possible without the constraint of artificially imposed task horizons. In such cases, the most behaviorally-relevant representation would carry information about when the agent was likely to first reach states of interest, rather than how often it should expect to visit them over a potentially infinite time span. To reflect such demands, we introduce the first-occupancy representation (FR), which measures the expected temporal discount to the first time a state is accessed. We demonstrate that the FR facilitates exploration, the selection of efficient paths to desired states, allows the agent, under certain conditions, to plan provably optimal trajectories defined by a sequence of subgoals, and induces similar behavior to animals avoiding threatening stimuli.
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
    Thema/Rubrik (Code) 004
    Erscheinungsdatum 2021-09-28
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    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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