Biology subjects

Mongillo, G.

Publications and source records attributed to Mongillo, G..

5 recordsLinked to original sources

bioRxiv2025

Is the cortical dynamics ergodic? A numerical study in partially symmetric networks of spiking neurons

Cortical activity in-vivo displays relaxational time scales much longer than the membrane time constant of the neurons or the deactivation time of ionotropic synaptic conductances. The mechanisms responsible for such slow dynamics are not understood. Here, we show that slow dynamics naturally and robustly emerges in dynamically-balanced networks of spiking neurons. This requires only partial symmetry in the synaptic connectivity, a feature of local cortical networks observed in experiments. The symmetry generates an effective, excitatory self-coupling of the neurons that leads to long-lived fluctuations in the network activity, without destroying the dynamical balance. When the excitatory self-coupling is suitably strong, the same mechanism leads to multiple equilibrium states of the network dynamics. Our results reveal a novel dynamical regime of the collective activity in spiking networks, where the memory of the initial state persists for very long times and ergodicity is broken.

neuroscience↗

bioRxiv2025

Synaptic Encoding of Time in Working Memory

AO_SCPLOWBSTRACTC_SCPLOWThe processing of temporally-extended sequences of stimuli critically relies on Working Memory (WM). Yet, how WM supports the encoding and retrieval of novel sequences is unknown. Existing theories rely on associative learning driven by repetitions and are, thus, unable to explain how people can reproduce novel sequences of stimuli immediately. Here, we propose that detailed temporal information about a novel sequence can be rapidly stored in WM by short-term synaptic plasticity over multiple time scales. To substantiate this proposal, we extend our previously-proposed synaptic theory of WM to include synaptic augmentation, besides more short-lived depression and facilitation, consistently with experimental observations. The long time scales associated with augmentation naturally lead to the emergence of a temporal gradient in the synaptic efficacies, which can be used to immediately replay, at normal speed or in a time-compressed way, novel sequences. The theory is consistent with behavioral and neurophysiological observations.

neuroscience↗

bioRxiv2024

Synaptic Theory of Working Memory for Serial Order

AO_SCPLOWBSTRACTC_SCPLOWPeople effortlessly remember short, but otherwise arbitrary, sequences of familiar stimuli over a brief period of time. This deceptively mundane ability is core for purposeful behavior and cognition. Surprisingly, however, it remains largely unexplained. Here, we propose that both the stimuli and their order of occurrence are encoded by transient synaptic enhancement over multiple time scales. To substantiate this proposal, we extend our previously-proposed synaptic theory of Working Memory (WM) to include synaptic augmentation besides short-term depression and facilitation, consistently with experimental observations. Like facilitation, augmentation builds up with repetitive activation but persists for much longer. We find that the long time scales associated with augmentation naturally lead to the emergence of a "primacy gradient" in the synaptic efficacies that can be used to reconstruct the order of presentation at recall. The novel theory accounts for prominent features of the behavior of humans recalling lists of items, makes testable predictions and, intriguingly, suggests that WM capacity limitations result from a failure in retrieving, rather than storing, information. Taken together, our results advance the understanding of the neuronal mechanisms underpinning the encoding of serial order and offer novel insights into the origin of WM capacity.

neuroscience↗

bioRxiv2022

Glassy phase in dynamically-balanced neural networks

We present a novel mean-field theory for balanced neuronal networks with arbitrary levels of symmetry in the synaptic connectivity. The theory determines the fixed point of the network dynamics and the conditions for its stability. The fixed point becomes unstable by increasing the synaptic gain beyond a critical value that depends on the level of symmetry. Beyond this critical gain, for positive levels of symmetry, we find a previously unreported phase. In this phase, the dynamical landscape is dominated by a large number of marginally-stable fixed points. As a result, the network dynamics exhibit non-exponential relaxation and ergodicity is broken. We discuss the relevance of such a glassy phase for understanding dynamical and computational aspects of cortical operation.

neuroscience↗

bioRxiv2017

Discrete confidence levels revealed by sequential decisions

Whether humans are optimal decision makers is still a debated issue in the realm of perceptual decisions. Taking advantage of the direct link between an optimal decision-making and the confidence in that decision, we offer a new dual-decisions method of inferring such confidence without asking for its explicit valuation. Our method circumvents the well-known miscalibration issue with explicit confidence reports as well as the specification of the cost-function required by opt-out or post-decision wagering methods. We show that observers inferred confidence in their first decision and its use in a subsequent decision (conditioned upon the correctness of the first) fall short of both the ideal Bayesian strategy, as well as of an under-sampling approximation or a modified Bayesian strategy augmented with an additional bias term to accommodate global miscalibration of confidence. The observed data are instead significantly better fitted by a model positing that observers use only few confidence levels or states, at odds with the continuous confidence function of stimulus level prescribed by a normative behavior. These findings question the validity of normative-Bayesian accounts of subjective confidence and metaperceptual judgments.

neuroscience↗