A recurrent network model of somatosensory parametric working memory in the prefrontal cortex
Open accessAbstract
A parametric working memory network stores the information of an analog stimulus in the form of persistent neural activity that is monotonically tuned to the stimulus. The family of persistent firing patterns with a continuous range of firing rates must all be realizable under exactly the same external conditions (during the delay when the transient stimulus is withdrawn). How this can be accomplished by neural mechanisms remains an unresolved question. Here we present a recurrent cortical network model of irregularly spiking neurons that was designed to simulate a somatosensory working memory experiment with behaving monkeys. Our model reproduces the observed positively and negatively monotonic persistent activity, and heterogeneous tuning curves of memory activity. We show that fine-tuning mathematically corresponds to a precise alignment of cusps in the bifurcation diagram of the network. Moreover, we show that the fine-tuned network can integrate stimulus inputs over several seconds. Assuming that such time integration occurs in neural populations downstream from a tonically persistent neural population, our model is able to account for the slow ramping-up and ramping-down behaviors of neurons observed in prefrontal cortex.
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Two neural populations coupled by reciprocal inhibition exhibit persistent activity that is positively and negatively monotonically tuned to the first frequency f1, as observed in prefrontal neurons.
“Moreover, two such neural populations coupled by reciprocal inhibition exhibit persistent activity that is positively and negatively, respectively, monotonically tuned to the first frequency f1, as observed in prefrontal neurons in this task (Romo et al., 1999; Brody et al., 2003; Miller et al., 2003).”
From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Miller et al., 2003
Miller et al. showed that fine-tuning of connectivity in the somatosensory discrimination model yields a line attractor capable of parametric working memory.
“Miller et al. (Miller et al., 2003) showed that fine-tuning of connectivity in this model yields a line attractor capable of parametric working memory, similar to the one in the gaze-control system (Seung et al., 2000).”
From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Miller et al., 2003
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- Authors
- Miller, P.
- Affiliations
- Brandeis University (US)
- Corresponding author
- Miller, P.
- ORCID
- Miller, P.
- Venue
- Cerebral Cortex
- Publisher
- Oxford University Press (OUP)
- Published
- 2003-11-01
- Volume
- 13
- Issue
- 11
- Pages
- 1208-1218
- ISSN
- 1047-3211 (ISSN-L)
- 1460-2199
- PMC
- PMC4632206
- OpenAlex
- W2122186032
- Cited by
- 144
- References
- 45
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 2 · 2023: 5 · 2022: 2 · 2020: 7 · 2019: 3 · 2018: 8 · 2017: 5 · 2016: 6 · 2015: 4 · 2014: 7 · 2013: 7 · 2012: 6
- FWCI
- 3.91
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cerebral Cortex (publishedVersion)
- PubMed (publishedVersion)
- Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Howard Hughes Medical Institute
- Alfred P. Sloan Foundation
- Brandeis University
- Consejo Nacional de Ciencia y Tecnología
- National Institutes of Health
- National Institute of Mental Health (R01MH062349, MH-62349, K25MH064497)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- prefrontal cortex, recurrent neural network model, persistent activity, bifurcation analysis, time integration
- MeSH terms
- Action Potentials, Animals, Macaca, Memory, Psychomotor Performance, Neural Networks, Computer, Prefrontal Cortex
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