Plasticity-induced multistability on fast and slow timescales enables optimal information encoding and spontaneous sequence discrimination
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| Format: | Preprint |
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2025
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| _version_ | 1866918142983274496 |
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| author | Barzon, Giacomo Busiello, Daniel M. Nicoletti, Giorgio |
| author_facet | Barzon, Giacomo Busiello, Daniel M. Nicoletti, Giorgio |
| contents | Neural circuits exhibit remarkable computational flexibility, enabling adaptive responses to noisy and ever-changing environmental cues. A fundamental question in neuroscience concerns how a wide range of behaviors can emerge from a relatively limited set of underlying biological mechanisms. In particular, the interaction between activities of neuronal populations and plasticity modulation of synaptic connections may endow neural circuits with a variety of functional responses when coordinated over different characteristic timescales. Here, we develop an information-theoretic framework to quantitatively explore this idea. We consider a stochastic model for neural activities that incorporates the presence of a coupled dynamic plasticity and time-varying stimuli. We show that long-term plasticity modulations play the functional role of steering neural activities towards a regime of optimal information encoding. By constructing the associated phase diagram, we demonstrate that either Hebbian or anti-Hebbian plasticity may become optimal strategies depending on how the external input is projected to the target neural populations. Conversely, short-term plasticity enables the discrimination of temporal ordering in sequences of inputs by navigating the emergent multistable attractor landscape. By allowing a degree of variability in external stimuli, we also highlight the existence of an optimal variability for sequence discrimination at a given plasticity strength. In summary, the timescale of plasticity modulation shapes how inputs are represented in neural activities, thereby fundamentally altering the computational properties of the system. Our approach offers a unifying information-theoretic perspective of the role of plasticity, paving the way for a quantitative understanding of the emergence of complex computations in coupled neuronal-synaptic dynamics. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_13867 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Plasticity-induced multistability on fast and slow timescales enables optimal information encoding and spontaneous sequence discrimination Barzon, Giacomo Busiello, Daniel M. Nicoletti, Giorgio Statistical Mechanics Neurons and Cognition Neural circuits exhibit remarkable computational flexibility, enabling adaptive responses to noisy and ever-changing environmental cues. A fundamental question in neuroscience concerns how a wide range of behaviors can emerge from a relatively limited set of underlying biological mechanisms. In particular, the interaction between activities of neuronal populations and plasticity modulation of synaptic connections may endow neural circuits with a variety of functional responses when coordinated over different characteristic timescales. Here, we develop an information-theoretic framework to quantitatively explore this idea. We consider a stochastic model for neural activities that incorporates the presence of a coupled dynamic plasticity and time-varying stimuli. We show that long-term plasticity modulations play the functional role of steering neural activities towards a regime of optimal information encoding. By constructing the associated phase diagram, we demonstrate that either Hebbian or anti-Hebbian plasticity may become optimal strategies depending on how the external input is projected to the target neural populations. Conversely, short-term plasticity enables the discrimination of temporal ordering in sequences of inputs by navigating the emergent multistable attractor landscape. By allowing a degree of variability in external stimuli, we also highlight the existence of an optimal variability for sequence discrimination at a given plasticity strength. In summary, the timescale of plasticity modulation shapes how inputs are represented in neural activities, thereby fundamentally altering the computational properties of the system. Our approach offers a unifying information-theoretic perspective of the role of plasticity, paving the way for a quantitative understanding of the emergence of complex computations in coupled neuronal-synaptic dynamics. |
| title | Plasticity-induced multistability on fast and slow timescales enables optimal information encoding and spontaneous sequence discrimination |
| topic | Statistical Mechanics Neurons and Cognition |
| url | https://arxiv.org/abs/2509.13867 |