A Diversity of Intrinsic Timescales Underlie Neural Computations
Neural processing occurs across a range of temporal scales. To facilitate this, the brain uses fast-changing representations reflecting momentary sensory input alongside more temporally extended representations, which integrate across both short and long temporal windows. The temporal flexibility of...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2020-12-01
|
| Series: | Frontiers in Neural Circuits |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fncir.2020.615626/full |
| _version_ | 1819275330702016512 |
|---|---|
| author | Sean E. Cavanagh Laurence T. Hunt Laurence T. Hunt Laurence T. Hunt Laurence T. Hunt Steven W. Kennerley |
| author_facet | Sean E. Cavanagh Laurence T. Hunt Laurence T. Hunt Laurence T. Hunt Laurence T. Hunt Steven W. Kennerley |
| author_sort | Sean E. Cavanagh |
| collection | DOAJ |
| description | Neural processing occurs across a range of temporal scales. To facilitate this, the brain uses fast-changing representations reflecting momentary sensory input alongside more temporally extended representations, which integrate across both short and long temporal windows. The temporal flexibility of these representations allows animals to behave adaptively. Short temporal windows facilitate adaptive responding in dynamic environments, while longer temporal windows promote the gradual integration of information across time. In the cognitive and motor domains, the brain sets overarching goals to be achieved within a long temporal window, which must be broken down into sequences of actions and precise movement control processed across much shorter temporal windows. Previous human neuroimaging studies and large-scale artificial network models have ascribed different processing timescales to different cortical regions, linking this to each region’s position in an anatomical hierarchy determined by patterns of inter-regional connectivity. However, even within cortical regions, there is variability in responses when studied with single-neuron electrophysiology. Here, we review a series of recent electrophysiology experiments that demonstrate the heterogeneity of temporal receptive fields at the level of single neurons within a cortical region. This heterogeneity appears functionally relevant for the computations that neurons perform during decision-making and working memory. We consider anatomical and biophysical mechanisms that may give rise to a heterogeneity of timescales, including recurrent connectivity, cortical layer distribution, and neurotransmitter receptor expression. Finally, we reflect on the computational relevance of each brain region possessing a heterogeneity of neuronal timescales. We argue that this architecture is of particular importance for sensory, motor, and cognitive computations. |
| first_indexed | 2024-12-23T23:22:37Z |
| format | Article |
| id | doaj.art-da9ac2fca02347d7b869c157dbae5b16 |
| institution | Directory Open Access Journal |
| issn | 1662-5110 |
| language | English |
| last_indexed | 2024-12-23T23:22:37Z |
| publishDate | 2020-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neural Circuits |
| spelling | doaj.art-da9ac2fca02347d7b869c157dbae5b162022-12-21T17:26:18ZengFrontiers Media S.A.Frontiers in Neural Circuits1662-51102020-12-011410.3389/fncir.2020.615626615626A Diversity of Intrinsic Timescales Underlie Neural ComputationsSean E. Cavanagh0Laurence T. Hunt1Laurence T. Hunt2Laurence T. Hunt3Laurence T. Hunt4Steven W. Kennerley5Department of Clinical and Movement Neurosciences, University College London, London, United KingdomDepartment of Clinical and Movement Neurosciences, University College London, London, United KingdomWellcome Trust Centre for Neuroimaging, University College London, London, United KingdomMax Planck-UCL Centre for Computational Psychiatry and Aging, University College London, London, United KingdomDepartment of Psychiatry, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United KingdomDepartment of Clinical and Movement Neurosciences, University College London, London, United KingdomNeural processing occurs across a range of temporal scales. To facilitate this, the brain uses fast-changing representations reflecting momentary sensory input alongside more temporally extended representations, which integrate across both short and long temporal windows. The temporal flexibility of these representations allows animals to behave adaptively. Short temporal windows facilitate adaptive responding in dynamic environments, while longer temporal windows promote the gradual integration of information across time. In the cognitive and motor domains, the brain sets overarching goals to be achieved within a long temporal window, which must be broken down into sequences of actions and precise movement control processed across much shorter temporal windows. Previous human neuroimaging studies and large-scale artificial network models have ascribed different processing timescales to different cortical regions, linking this to each region’s position in an anatomical hierarchy determined by patterns of inter-regional connectivity. However, even within cortical regions, there is variability in responses when studied with single-neuron electrophysiology. Here, we review a series of recent electrophysiology experiments that demonstrate the heterogeneity of temporal receptive fields at the level of single neurons within a cortical region. This heterogeneity appears functionally relevant for the computations that neurons perform during decision-making and working memory. We consider anatomical and biophysical mechanisms that may give rise to a heterogeneity of timescales, including recurrent connectivity, cortical layer distribution, and neurotransmitter receptor expression. Finally, we reflect on the computational relevance of each brain region possessing a heterogeneity of neuronal timescales. We argue that this architecture is of particular importance for sensory, motor, and cognitive computations.https://www.frontiersin.org/articles/10.3389/fncir.2020.615626/fullneuronal timescaleautocorrelationtime constantdecision-makingworking memory |
| spellingShingle | Sean E. Cavanagh Laurence T. Hunt Laurence T. Hunt Laurence T. Hunt Laurence T. Hunt Steven W. Kennerley A Diversity of Intrinsic Timescales Underlie Neural Computations Frontiers in Neural Circuits neuronal timescale autocorrelation time constant decision-making working memory |
| title | A Diversity of Intrinsic Timescales Underlie Neural Computations |
| title_full | A Diversity of Intrinsic Timescales Underlie Neural Computations |
| title_fullStr | A Diversity of Intrinsic Timescales Underlie Neural Computations |
| title_full_unstemmed | A Diversity of Intrinsic Timescales Underlie Neural Computations |
| title_short | A Diversity of Intrinsic Timescales Underlie Neural Computations |
| title_sort | diversity of intrinsic timescales underlie neural computations |
| topic | neuronal timescale autocorrelation time constant decision-making working memory |
| url | https://www.frontiersin.org/articles/10.3389/fncir.2020.615626/full |
| work_keys_str_mv | AT seanecavanagh adiversityofintrinsictimescalesunderlieneuralcomputations AT laurencethunt adiversityofintrinsictimescalesunderlieneuralcomputations AT laurencethunt adiversityofintrinsictimescalesunderlieneuralcomputations AT laurencethunt adiversityofintrinsictimescalesunderlieneuralcomputations AT laurencethunt adiversityofintrinsictimescalesunderlieneuralcomputations AT stevenwkennerley adiversityofintrinsictimescalesunderlieneuralcomputations AT seanecavanagh diversityofintrinsictimescalesunderlieneuralcomputations AT laurencethunt diversityofintrinsictimescalesunderlieneuralcomputations AT laurencethunt diversityofintrinsictimescalesunderlieneuralcomputations AT laurencethunt diversityofintrinsictimescalesunderlieneuralcomputations AT laurencethunt diversityofintrinsictimescalesunderlieneuralcomputations AT stevenwkennerley diversityofintrinsictimescalesunderlieneuralcomputations |