Joaquin J. Torres

A unified inverse-design protocol combines ML screening and physics-informed PSO to explore over 2 million anthracenes, identify 1002 SF candidates, and optimize their molecular orientations.

We present a biologically inspired quantum neural network that encodes neuronal populations as fully connected qubits governed by the Lipkin–Meshkov–Glick (LMG) quantum Hamiltonian and modulated by a synaptic-efficacy feedback implementing activity-dependent changes in the collective time scale. The framework links collective quantum many-body modes and collective-state structure to population homeostasis and rhythmogenesis, outlining scalable computational primitives long-lived operating regimes, activity-dependent oscillation periods, and size-dependent robustness that position LMG-based architectures as promising blueprints for bio-inspired quantum brains on future quantum hardware.

The dynamics of higher-order topological signals are increasingly recognized as a key aspect of the activity of complex systems. A paradigmatic example are synaptic dynamics: synaptic efficacy changes over time driven by different mechanisms. Beyond traditional node-driven short-term plasticity, the role of astrocyte modulation through higher-order interactions, in the tripartite synapse, is increasingly recognized. However, the competition and interplay between node-driven and higher-order mechanisms remain poorly understood. Here, we introduce a higher-order model of the tripartite synapse, accounting for astrocyte-synapse-neuron interactions in short-term plasticity, such that astrocyte gliotransmission and pre-synaptic facilitation jointly modulate neurotransmitter release, generalizing earlier short-term plasticity models. We study these mechanisms in a minimal recurrent motif—a directed ring of three excitatory neurons—where one neuron receives external stimulation. Due to strong recurrence, the circuit is prone to self-sustained activity, often ignoring external input. By introducing higher-order interactions via astrocyte modulation, we show this robustly stabilizes circuit dynamics and expands the parameter space supporting stimulus-driven activity. Our findings highlight how astrocytes reshape effective connectivity through higher-order interactions—even in simple recurrent circuits.

We present a biologically inspired quantum neural network that encodes neuronal populations as fully connected qubits governed by the Lipkin-Meshkov-Glick (LMG) quantum Hamiltonian and stabilized by a synaptic-efficacy feedback implementing activity-dependent homeostatic control. The framework links collective quantum many-body modes and attractor structure to population homeostasis and rhythmogenesis, outlining scalable computational primitives -- stable set points, controllable oscillations, and size-dependent robustness -- that position LMG-based architectures as promising blueprints for bio-inspired quantum brains on future quantum hardware.

The dynamics of higher-order topological signals are increasingly recognized as a key aspect of the activity of complex systems. A paradigmatic example are synaptic dynamics: synaptic efficacy changes over time driven by different mechanisms. Beyond traditional node-driven short-term plasticity mechanisms, the role of astrocyte modulation through higher-order interactions, in the so-called tripartite synapse, is increasingly recognized. However, the competition and interplay between node-driven and higher-order mechanisms have yet to be considered. Here, we introduce a simple higher-order model of the tripartite synapse accounting for astrocyte-synapse-neuron interactions in short-term plasticity. In the model, astrocyte gliotransmission and pre-synaptic intrinsic facilitation mechanisms jointly modulate the probability of neurotransmitter release at the synapse, generalizing previous short-term plasticity models. We investigate the implications of such mechanisms in a minimal recurrent motif -- a directed ring of three excitatory leaky integrate-and-fire neurons -- where one neuron receives external stimulation that propagates through the circuit. Due to its strong recurrence, the circuit is highly prone to self-sustained activity, which can make it insensitive to external input. By introducing higher-order interactions among different synapses through astrocyte modulation, we show that higher-order modulation robustly stabilizes circuit dynamics and expands the parameter space that supports stimulus-driven activity. Our findings highlight a plausible mechanism by which astrocytes can reshape effective connectivity and enhance information processing through higher-order structural interactions -- even in the simplest recurrent circuits.

Triadic interactions in the brain are general mechanisms by which a node, e.g. a neuron or a glia cell such as the astrocyte, can regulate directly the link, e.g. synapse between other two nodes. The regulation takes place in a familiar way by either depressing or facilitating synaptic transmission. Such interactions are ubiquitous in neural systems, accounting both for axo-axonic and tripartite synapses mediated by astrocytes, for instance, and have been related to neuronal and synaptic processes at different time-scales, including short- and long-term synaptic plasticity. In the field of network science, triadic interactions have been shown to produce complex spatio-temporal patterns of connectivity. Here, we investigate the emergent behavior of an in silico neural medium constituted by a population of leaky integrate-and-fire neurons with triadic interactions. We observe that, depending on relevant parameters defining triadic interactions, different activity patterns emerge. These include (i) a silent phase, (ii) a low-activity phase in which complex spatio-temporal patterns of low neuronal firing rate emerge that propagate through the medium, (iii) a high-activity phase characterized by complex spatio-temporal patterns of high neuronal firing rate that propagate through the neural medium as waves of high firing activity over a bulk of low activity neurons, and (iv) a pseudo-blinking phase in which the neural medium switches between high and low activity states. Here we analyze in depth the features of such patterns and relate our findings to the recently proposed model of triadic percolation.

Triadic interactions are general mechanisms by which a node or neuron can regulate directly the link or synapse between other two neurons. The regulation takes place in a familiar way by either depressing or facilitating synaptic transmission. Such interactions are ubiquitous in neural systems, accounting for axo-axonic synapses and tripartite synapses mediated by astrocytes, for instance, and have been related to neuronal and synaptic processes at different time-scales, including short and long-term synaptic plasticity. In the field of network science, triadic interactions have been shown to produce complex spatio-temporal patterns of connectivity. Here, we investigate the emergent behavior of an in silico neural medium constituted by a population of leaky integrate-and-fire neurons with triadic interactions. We observe that, depending on relevant parameters defining triadic interactions, different activity patterns emerge. These include i) a silent phase, ii) a low-activity phase in which complex spatio-temporal patterns of low neuronal firing rate emerge that propagate through the medium, iii) a high-activity phase characterized by complex spatio-temporal patterns of high neuronal firing rate that propagate through the neural medium as waves of high firing activity over a bulk of low activity neurons, and iv) a pseudo-blinking phase in which the neural medium switches between high and low activity states, in a similar fashion to up/down state transitions. Here we analyse in depth the features of such patterns and relate our findings to the recently proposed model of triadic percolation.

We present extensive simulations of a quantum version of the Hopfield neural network to explore its emergent behavior. The system is a network of N qubits oscillating at a given Ω frequency and which are coupled via Lindblad jump operators built with local fields h_i depending on some given stored patterns. Our simulations show the emergence of pattern-antipattern oscillations of the overlaps with the stored patterns similar (for large Ω and small temperature) to those reported within a recent mean-field description of such a system, and which are originated deterministically by the quantum term including s x i qubit operators. However, in simulations we observe that such oscillations are stochastic due to the interplay between noise and the inherent metastability of the pattern attractors induced by quantum oscillations, and then are damped in finite systems when one averages over many quantum trajectories. In addition, we report the system behavior for large number of stored patterns at the lowest temperature we can reach in simulations (namely T = 0.005 T C ). Our study reveals that for small-size systems the quantum term of the Hamiltonian has a negative effect on storage capacity, decreasing the overlap with the starting memory pattern for increased values of Ω and number of stored patterns. However, it also impedes the system to be trapped for long time in mixtures and spin-glass states. Interestingly, the system also presents a range of Ω values for which, although the initial pattern is destabilized due to quantum oscillations, other patterns can be retrieved and remain stable even for many stored patterns, implying a quantum-dependent nonlinear relationship between the recall process and the number of stored patterns.