The DOI matches its Crossref record; 83 of the 85 references it cites were found in a registry or an index.
Internal representation of task rules by recurrent dynamics: the importance of the diversity of neural responses
Open accessAbstract
Neural activity of behaving animals, especially in the prefrontal cortex, is highly heterogeneous, with selective responses to diverse aspects of the executed task. We propose a general model of recurrent neural networks that perform complex rule-based tasks, and we show that the diversity of neuronal responses plays a fundamental role when the behavioral responses are context-dependent. Specifically, we found that when the inner mental states encoding the task rules are represented by stable patterns of neural activity (attractors of the neural dynamics), the neurons must be selective for combinations of sensory stimuli and inner mental states. Such mixed selectivity is easily obtained by neurons that connect with random synaptic strengths both to the recurrent network and to neurons encoding sensory inputs. The number of randomly connected neurons needed to solve a task is on average only three times as large as the number of neurons needed in a network designed ad hoc. Moreover, the number of needed neurons grows only linearly with the number of task-relevant events and mental states, provided that each neuron responds to a large proportion of events (dense/distributed coding). A biologically realistic implementation of the model captures several aspects of the activity recorded from monkeys performing context-dependent tasks. Our findings explain the importance of the diversity of neural responses and provide us with simple and general principles for designing attractor neural networks that perform complex computation.
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“However, neural responses with combinatorial (mixed) properties are hallmarks of goal-directed cognitive control (Rigotti et al., 2010), and in the context of information selection may embody the bank of knowledge that is necessary for selecting cues.”
From Attention, learning, and the value of information (2012) · cites this paper as Rigotti et al., 2010
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- Minsky M. Papert S. (1969). Perceptrons. Cambridge: MIT Press.
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The importance of mixed selectivity in complex cognitive tasks
Rigotti, M., Barak, O., Warden, M. R., Wang, X.-J., Daw, N. D., Miller, E. K., & Fusi, S. (2013) · Nature · doi:10.1038/nature12160 · Cited by 1,639
Self-citationTraining Spiking Neural Networks Using Lessons From Deep Learning
Eshraghian, J. K., Ward, M., Neftci, E., Wang, X., Lenz, G., Dwivedi, G., Bennamoun, M., Jeong, D. S., & Lu, W. D. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3308088 · Cited by 1,106
Why neurons mix: high dimensionality for higher cognition
Fusi, S., Miller, E. K., & Rigotti, M. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2016.01.010 · Cited by 766
Reworking the language network
Fedorenko, E., & Thompson-Schill, S. L. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.12.006 · Cited by 615
Emotion, Cognition, and Mental State Representation in Amygdala and Prefrontal Cortex
Salzman, C. D., & Fusi, S. (2010) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.051508.135256 · Cited by 628
Metadata
- Authors
- Rigotti, M.
- Affiliations
- University of Zurich (CH)
- Corresponding author
- Rigotti, Mattia
- ORCID
- Rigotti, Mattia
- Venue
- Frontiers in Computational Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2010
- Volume
- 4
- Pages
- 24-24
- ISSN
- 1662-5188 (ISSN-L)
- PMC
- PMC2967380
- OpenAlex
- W2153719229
- Cited by
- 184
- References
- 85
- Citations by year
- 2026: 8 · 2025: 13 · 2024: 19 · 2023: 10 · 2022: 7 · 2021: 17 · 2020: 13 · 2019: 11 · 2018: 6 · 2017: 8 · 2016: 9 · 2015: 8 · 2014: 12 · 2013: 20 · 2012: 8
- FWCI
- 8.12
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- other-oa
- Locations
- Frontiers in Computational Neuroscience (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (106556)
- National Institute of Mental Health (R01MH062349)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- recurrent neural networks, attractor dynamics, mixed selectivity, prefrontal cortex, context-dependent behavior, random connectivity, distributed coding, dense coding
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