@ARTICLE{BERGOIN_2025_ARTICLE_BTDQZ_545441, 
 AUTHOR = {Bergoin, R. and Torcini, A. and Deco, G. and Quoy, M. and Zamora López, G.}, 
 TITLE = {Emergence and maintenance of modularity in neural networks with Hebbian and anti-Hebbian inhibitory STDP}, 
 YEAR = {2025}, 
 ABSTRACT = {The modular and hierarchical organization of the brain is believed to support the coexistence of segregated (specialization) and integrated (binding) information processes. A relevant question is yet to understand how such architecture naturally emerges and is sustained over time, given the plastic nature of the brain’s wiring. Following evidences that the sensory cortices organize into assemblies under selective stimuli, it has been shown that stable neuronal assemblies can emerge due to targeted stimulation, embedding various forms of synaptic plasticity in presence of homeostatic and/or control mechanisms. Here, we show that simple spike-timing-dependent plasticity (STDP) rules, based only on pre-and post-synaptic spike times, can also lead to the stable encoding of memories in the absence of any control mechanism. We develop a model of spiking neurons, trained by stimuli targeting different sub-populations. The model satisfies some biologically plausible features: (i) it contains excitatory and inhibitory neurons with Hebbian and anti-Hebbian STDP; (ii) neither the neuronal activity nor the synaptic weights are frozen after the learning phase. Instead, the neurons are allowed to fire spontaneously while synaptic plasticity remains active. We find that only the combination of two inhibitory STDP sub-populations allows for the formation of stable modules in the network, with each sub-population playing a distinctive role. The Hebbian sub-population controls for the firing activity, while the anti-Hebbian neurons promote pattern selectivity. After the learning phase, the network settles into an asynchronous irregular resting-state. This post-learning activity is associated with spontaneous memory recalls which turn out to be fundamental for the long-term consolidation of the learned memories. Due to its simplicity, the introduced model can represent a test-bed for further investigations on the role played by STDP on memory storing and maintenance}, 
 KEYWORDS = {--}, 
 PAGES = {35}, 
 URL = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012973}, 
 VOLUME = {21 (4)}, 
 DOI = {10.1371/journal.pcbi.1012973}, 
 ISSN = {1553-7358}, 
 JOURNAL = {PLOS COMPUTATIONAL BIOLOGY}, 
}

@ARTICLE{POLITI_2024_ARTICLE_PT_473481, 
 AUTHOR = {Politi, A. and Torcini, A.}, 
 TITLE = {A robust balancing mechanism for spiking neural networks}, 
 YEAR = {2024}, 
 ABSTRACT = {Dynamical balance of excitation and inhibition is usually invoked to explain the irregular low firing activity observed in the cortex. We propose a robust nonlinear balancing mechanism for a random network of spiking neurons, which works also in the absence of strong external currents. Biologically, the mechanism exploits the plasticity of excitatory-excitatory synapses induced by short-term depression. Mathematically, the nonlinear response of the synaptic activity is the key ingredient responsible for the emergence of a stable balanced regime. Our claim is supported by a simple self-consistent analysis accompanied by extensive simulations performed for increasing network sizes. The observed regime is essentially fluctuation driven and characterized by highly irregular spiking dynamics of all neurons}, 
 PAGES = {8}, 
 URL = {https://pubs.aip.org/search-results?page=1\&q=Arobustbalancingmechanismforspikingneuralnetworks}, 
 VOLUME = {34 (4)}, 
 DOI = {10.1063/5.0199298}, 
 ISSN = {1054-1500}, 
 JOURNAL = {CHAOS}, 
}

@ARTICLE{COPPOLINO_2023_ARTICLE_CM_464991, 
 AUTHOR = {Coppolino, S. and Migliore, M.}, 
 TITLE = {An explainable artificial intelligence approach to spatial navigation based on hippocampal circuitry}, 
 YEAR = {2023}, 
 ABSTRACT = {Learning to navigate a complex environment is not a difficult task for a mammal. For example, finding the correct way to exit a maze following a sequence of cues, does not need a long training session. Just a single or a few runs through a new environment is, in most cases, sufficient to learn an exit path starting from anywhere in the maze. This ability is in striking contrast with the well-known difficulty that any deep learning algorithm has in learning a trajectory through a sequence of objects. Being able to learn an arbitrarily long sequence of objects to reach a specific place could take, in general, prohibitively long training sessions. This is a clear indication that current artificial intelligence methods are essentially unable to capture the way in which a real brain implements a cognitive function. In previous work, we have proposed a proof-of-principle model demonstrating how, using hippocampal circuitry, it is possible to learn an arbitrary sequence of known objects in a single trial. We called this model SLT (Single Learning Trial). In the current work, we extend this model, which we will call e-STL, to introduce the capability of navigating a classic four-arms maze to learn, in a single trial, the correct path to reach an exit ignoring dead ends. We show the conditions under which the e-SLT network, including cells coding for places, head-direction, and objects, can robustly and efficiently implement a fundamental cognitive function. The results shed light on the possible circuit organization and operation of the hippocampus and may represent the building block of a new generation of artificial intelligence algorithms for spatial navigation}, 
 KEYWORDS = {Robot spatial navigation, Spike-time-dependent plasticity, Hippocampal circuitry, Spiking neurons network}, 
 PAGES = {97-107}, 
 URL = {http://www.scopus.com/inward/record.url?eid=2-s2.0-85151678377\&partnerID=q2rCbXpz}, 
 VOLUME = {163}, 
 DOI = {10.1016/j.neunet.2023.03.030}, 
 ISSN = {0893-6080}, 
 JOURNAL = {NEURAL NETWORKS}, 
}

@ARTICLE{GIORDANO_2023_ARTICLE_GAFPPMMFBC_527163, 
 AUTHOR = {Giordano, N. and Alia, C. and Fruzzetti, L. and Pasquini, M. and Palla, G. and Mazzoni, A. and Micera, S. and Fogassi, L. and Bonini, L. and Caleo, M.}, 
 TITLE = {Fast-Spiking Interneurons of the Premotor Cortex Contribute to Initiation and Execution of Spontaneous Actions}, 
 YEAR = {2023}, 
 ABSTRACT = {Planning and execution of voluntary movement depend on the contribution of distinct classes of neurons in primary motor and premotor areas. However, timing and pattern of activation of GABAergic cells during specific motor behaviors remain only partly understood. Here, we directly compared the response properties of putative pyramidal neurons (PNs) and GABAergic fast-spiking neurons (FSNs) during spontaneous licking and forelimb movements in male mice. Recordings centered on the face/mouth motor field of the anterolateral motor cortex (ALM) revealed that FSNs fire longer than PNs and earlier for licking, but not for forelimb movements. Computational analysis revealed that FSNs carry vastly more information than PNs about the onset of movement. While PNs differently modulate their discharge during distinct motor acts, most FSNs respond with a stereotyped increase in firing rate. Accordingly, the informational redundancy was greater among FSNs than PNs. Finally, optogenetic silencing of a subset of FSNs reduced spontaneous licking movement. These data suggest that a global rise of inhibition contributes to the initiation and execution of spontaneous motor actions}, 
 KEYWORDS = {electrophysiology, fast spiking neurons, licking, premotor cortex, pyramidal neurons, single unit activity}, 
 PAGES = {4234-4250}, 
 URL = {https://iris.cnr.it/handle/20.500.14243/527163}, 
 VOLUME = {43 (23)}, 
 DOI = {10.1523/jneurosci.0750-22.2023}, 
 ISSN = {0270-6474}, 
 JOURNAL = {THE JOURNAL OF NEUROSCIENCE}, 
}

@ARTICLE{COPPOLINO_2022_ARTICLE_CGM_400193, 
 AUTHOR = {Coppolino, S. and Giacopelli, G. and Migliore, M.}, 
 TITLE = {Sequence Learning in a Single Trial: A Spiking Neurons Model Based on Hippocampal Circuitry}, 
 YEAR = {2022}, 
 ABSTRACT = {In contrast with our everyday experience using brain circuits, it can take a prohibitively long time to train a computational system to produce the correct sequence of outputs in the presence of a series of inputs. This suggests that something important is missing in the way in which models are trying to reproduce basic cognitive functions. In this work, we introduce a new neuronal network architecture that is able to learn, in a single trial, an arbitrary long sequence of any known objects. The key point of the model is the explicit use of mechanisms and circuitry observed in the hippocampus, which allow the model to reach a level of efficiency and accuracy that, to the best of our knowledge, is not possible with abstract network implementations. By directly following the natural system's layout and circuitry, this type of implementation has the additional advantage that the results can be more easily compared to the experimental data, allowing a deeper and more direct understanding of the mechanisms underlying cognitive functions and dysfunctions and opening the way to a new generation of learning architectures}, 
 KEYWORDS = {Brain modeling, Computer architecture, Hippocampu, Learning system, Microprocessor, Navigation, Neuron, Persistent firing (PF), robot navigation, spike-timing-dependent-plasticity synapse, spiking neurons.}, 
 URL = {http://www.scopus.com/inward/record.url?eid=2-s2.0-85100454421\&partnerID=q2rCbXpz}, 
 DOI = {10.1109/TNNLS.2021.3049281}, 
 ISSN = {2162-237X}, 
 JOURNAL = {IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS}, 
}

@ARTICLE{MENEGHETTI_2022_ARTICLE_MCVTTPCM_413480, 
 AUTHOR = {Meneghetti, N. and Cerri, C. and Vannini, E. and Tantillo, E. and Tottene, A. and Pietrobon, D. and Caleo, M. and Mazzoni, A.}, 
 TITLE = {Synaptic alterations in visual cortex reshape contrast-dependent gamma oscillations and inhibition-excitation ratio in a genetic mouse model of migraine}, 
 YEAR = {2022}, 
 ABSTRACT = {Methods: We recorded extracellular field potentials from the primary visual cortex (V1) of head-fixed awake FHM1 knock-in (n = 12) and wild type (n = 12) mice in response to square-wave gratings with different visual contrasts. Additionally, we reproduced in silico the obtained experimental results with a novel spiking neurons network model of mouse V1, by implementing in the model both the synaptic alterations characterizing the FHM1 genetic mouse model adopted}, 
 KEYWORDS = {Migraine, Visual cortex, Mice, Gamma oscillations, Spiking neurons networks, Familial-hemiplegic-type1-migraine, Mutual information}, 
 PAGES = {18}, 
 URL = {https://iris.cnr.it/handle/20.500.14243/413480}, 
 VOLUME = {23 (1)}, 
 DOI = {10.1186/s10194-022-01495-9}, 
 ISSN = {1129-2369}, 
 JOURNAL = {THE JOURNAL OF HEADACHE AND PAIN (TESTO STAMP.)}, 
}

@ARTICLE{YANG_2022_ARTICLE_YPACBA_529682, 
 AUTHOR = {Yang, J. and Primo, E. and Aleja, D. and Criado, R. and Boccaletti, S. and Alfaro Bittner, K.}, 
 TITLE = {Implementing and morphing Boolean gates with adaptive synchronization: The case of spiking neurons}, 
 YEAR = {2022}, 
 ABSTRACT = {Boolean logic is the paradigm through which modern computation is performed in silica. When nonlinear dynamical systems are interacting in a directed graph, we show that computation abilities emerge spontaneously from adaptive synchronization, which actually can emulate Boolean logic. Precisely, we demonstrate that a single dynamical unit, a spiking neuron modeled by the Hodgkin-Huxley model, can be used as the basic computational unit for realizing all the 16 Boolean logical gates with two inputs and one output, when it is coupled adaptively in a way that depends on the synchronization level between the two input signals. This is realized by means of a set of parameters, whose tuning offers even the possibility of constructing a morphing gate, i. e., a logical gate able to switch efficiently from one to another of such 16 Boolean gates. Extensive simulations demonstrate the efficiency and the accuracy of the proposed computational paradigm}, 
 KEYWORDS = {Boolean logical gates, Dynamical systems, Spiking neurons, Synchronization}, 
 PAGES = {6}, 
 URL = {https://www.sciencedirect.com/science/article/abs/pii/S0960077922006580}, 
 VOLUME = {162}, 
 DOI = {10.1016/j.chaos.2022.112448}, 
 ISSN = {0960-0779}, 
 JOURNAL = {CHAOS, SOLITONS AND FRACTALS}, 
}

@ARTICLE{BI_2021_ARTICLE_BDT_447710, 
 AUTHOR = {Bi, H. and Di Volo, M. and Torcini, A.}, 
 TITLE = {Asynchronous and Coherent Dynamics in Balanced Excitatory-Inhibitory Spiking Networks}, 
 YEAR = {2021}, 
 ABSTRACT = {Dynamic excitatory-inhibitory (E-I) balance is a paradigmatic mechanism invoked to explain the irregular low firing activity observed in the cortex. However, we will show that the E-I balance can be at the origin of other regimes observable in the brain. The analysis is performed by combining extensive simulations of sparse E-I networks composed of N spiking neurons with analytical investigations of low dimensional neural mass models. The bifurcation diagrams, derived for the neural mass model, allow us to classify the possible asynchronous and coherent behaviors emerging in balanced E-I networks with structural heterogeneity for any finite in-degree K. Analytic mean-field (MF) results show that both supra and sub-threshold balanced asynchronous regimes are observable in our system in the limit N \> \> K \> \> 1. Due to the heterogeneity, the asynchronous states are characterized at the microscopic level by the splitting of the neurons in to three groups: silent, fluctuation, and mean driven. These features are consistent with experimental observations reported for heterogeneous neural circuits. The coherent rhythms observed in our system can range from periodic and quasi-periodic collective oscillations (COs) to coherent chaos. These rhythms are characterized by regular or irregular temporal fluctuations joined to spatial coherence somehow similar to coherent fluctuations observed in the cortex over multiple spatial scales. The COs can emerge due to two different mechanisms. A first mechanism analogous to the pyramidal-interneuron gamma (PING), usually invoked for the emergence of ?-oscillations. The second mechanism is intimately related to the presence of current fluctuations, which sustain COs characterized by an essentially simultaneous bursting of the two populations. We observe period-doubling cascades involving the PING-like COs finally leading to the appearance of coherent chaos. Fluctuation driven COs are usually observable in our system as quasi-periodic collective motions characterized by two incommensurate frequencies. However, for sufficiently strong current fluctuations these collective rhythms can lock. This represents a novel mechanism of frequency locking in neural populations promoted by intrinsic fluctuations. COs are observable for any finite in-degree K, however, their existence in the limit N \> \> K \> \> 1 appears as uncertain}, 
 KEYWORDS = {balanced spiking neural populations, sparse inhibitory-excitatory networks, asynchronous dynamics, collective oscillations, neural mass model, quadratic integrate and fire neuron, structural heterogeneity, coherent chaos}, 
 PAGES = {20}, 
 URL = {https://www.frontiersin.org/articles/10.3389/fnsys.2021.752261/full}, 
 VOLUME = {15}, 
 DOI = {10.3389/fnsys.2021.752261}, 
 ISSN = {1662-5137}, 
 JOURNAL = {FRONTIERS IN SYSTEMS NEUROSCIENCE}, 
}

@ARTICLE{STUCCHI_2021_ARTICLE_SPVVB_513361, 
 AUTHOR = {Stucchi, M. and Pittorino, F. and Volo, M. D. and Vezzani, A. and Burioni, R.}, 
 TITLE = {Order symmetry breaking and broad distribution of events in spiking neural networks with continuous membrane potential}, 
 YEAR = {2021}, 
 ABSTRACT = {We introduce an exactly integrable version of the well-known leaky integrate-and-fire (LIF) model, with continuous membrane potential at the spiking event, the c-LIF. We investigate the dynamical regimes of a fully connected network of excitatory c-LIF neurons in the presence of short-term synaptic plasticity. By varying the coupling strength among neurons, we show that a complex chaotic dynamics arises, characterized by scale free avalanches. The origin of this phenomenon in the c-LIF can be related to the order symmetry breaking in neurons spike-times, which corresponds to the onset of a broad activity distribution. Our analysis uncovers a general mechanism through which networks of simple neurons can be attracted to a complex basin in the phase space}, 
 KEYWORDS = {Synchronization, networks of spiking neurons, Order symmetry breaking, Neuronal avalanches.}, 
 PAGES = {110946-1-110946-8}, 
 URL = {https://www.sciencedirect.com/science/article/pii/S0960077921003003}, 
 VOLUME = {147}, 
 DOI = {10.1016/j.chaos.2021.110946}, 
 ISSN = {0960-0779}, 
 JOURNAL = {CHAOS, SOLITONS AND FRACTALS}, 
}

@ARTICLE{TRIMARCO_2021_ARTICLE_TMC_438131, 
 AUTHOR = {Trimarco, E. and Mirino, P. and Caligiore, D.}, 
 TITLE = {Cortico-Cerebellar Hyper-Connections and Reduced Purkinje Cells Behind Abnormal Eyeblink Conditioning in a Computational Model of Autism Spectrum Disorder}, 
 YEAR = {2021}, 
 ABSTRACT = {Empirical evidence suggests that children with autism spectrum disorder (ASD) show abnormal behavior during delay eyeblink conditioning. They show a higher conditioned response learning rate and earlier peak latency of the conditioned response signal. The neuronal mechanisms underlying this autistic behavioral phenotype are still unclear. Here, we use a physiologically constrained spiking neuron model of the cerebellar-cortical system to investigate which features are critical to explaining atypical learning in ASD. Significantly, the computer simulations run with the model suggest that the higher conditioned responses learning rate mainly depends on the reduced number of Purkinje cells. In contrast, the earlier peak latency mainly depends on the hyper-connections of the cerebellum with sensory and motor cortex. Notably, the model has been validated by reproducing the behavioral data collected from studies with real children. Overall, this article is a starting point to understanding the link between the behavioral and neurobiological basis in ASD learning. At the end of the paper, we discuss how this knowledge could be critical for devising new treatments}, 
 KEYWORDS = {Autism, Artificial intelligence, Spiking neurons, Cerebellum, Associative learning, Computational neuroscience, Network neuroscience, Cerebellar-cortical loops, Prefrontal cortex}, 
 PAGES = {1-14}, 
 URL = {https://www.frontiersin.org/articles/10.3389/fnsys.2021.666649/full}, 
 VOLUME = {15}, 
 DOI = {10.3389/fnsys.2021.666649}, 
 ISSN = {1662-5137}, 
 JOURNAL = {FRONTIERS IN SYSTEMS NEUROSCIENCE}, 
}

@ARTICLE{CALIGIORE_2019_ARTICLE_CMB_366792, 
 AUTHOR = {Caligiore, D. and Mannella, F. and Baldassarre, G.}, 
 TITLE = {Different dopaminergic dysfunctions underlying parkinsonian akinesia and tremor}, 
 YEAR = {2019}, 
 ABSTRACT = {Although the occurrence of Parkinsonian akinesia and tremor is traditionally associated to dopaminergic degeneration, the multifaceted neural processes that cause these impairments are not fully understood. As a consequence, current dopamine medications cannot be tailored to the specific dysfunctions of patients with the result that generic drug therapies produce different effects on akinesia and tremor. This article proposes a computational model focusing on the role of dopamine impairments in the occurrence of akinesia and resting tremor. The model has three key features, to date never integrated in a single computational system: (a) an architecture constrained on the basis of the relevant known system-level anatomy of the basal ganglia-thalamo-cortical loops; (b) spiking neurons with physiologically-constrained parameters; (c) a detailed simulation of the effects of both phasic and tonic dopamine release. The model exhibits a neural dynamics compatible with that recorded in the brain of primates and humans. Moreover, it suggests that akinesia might involve both tonic and phasic dopamine dysregulations whereas resting tremor might be primarily caused by impairments involving tonic dopamine release and the responsiveness of dopamine receptors. These results could lead to develop new therapies based on a system-level view of the Parkinson's disease and targeting phasic and tonic dopamine in differential ways}, 
 KEYWORDS = {Parkinson's disease, Akinesia, dopamine}, 
 PAGES = {15}, 
 URL = {https://www.frontiersin.org/articles/10.3389/fnins.2019.00550/full}, 
 VOLUME = {13 (550)}, 
 DOI = {10.3389/fnins.2019.00550}, 
 ISSN = {1662-453X}, 
 JOURNAL = {FRONTIERS IN NEUROSCIENCE (ONLINE)}, 
}

@ARTICLE{LUCCIOLI_2019_ARTICLE_LAT_364761, 
 AUTHOR = {Luccioli, S. and Angulo Garcia, D. and Torcini, A.}, 
 TITLE = {Neural activity of heterogeneous inhibitory spiking networks with delay}, 
 YEAR = {2019}, 
 ABSTRACT = {We study a network of spiking neurons with heterogeneous excitabilities connected via inhibitory delayed pulses. For globally coupled systems the increase of the inhibitory coupling reduces the number of firing neurons by following a winner-takes-all mechanism. For sufficiently large transmission delay we observe the emergence of collective oscillations in the system beyond a critical coupling value. Heterogeneity promotes neural inactivation and asynchronous dynamics and its effect can be counteracted by considering longer time delays. In sparse networks, inhibition has the counterintuitive effect of promoting neural reactivation of silent neurons for sufficiently large coupling. In this regime, current fluctuations are on one side responsible for neural firing of subthreshold neurons and on the other side for their desynchronization. Therefore, collective oscillations are present only in a limited range of coupling values, which remains finite in the thermodynamic limit. Out of this range the dynamics is asynchronous and for very large inhibition neurons display a bursting behavior alternating periods of silence with periods where they fire freely in absence of any inhibition}, 
 KEYWORDS = {Asynchronous dynamics, Collective oscillations, Critical coupling, Current fluctuations, Desynchronization, Inhibitory coupling, Thermodynamic limits, Transmission delays}, 
 PAGES = {13}, 
 URL = {https://pubmed.ncbi.nlm.nih.gov/31212434/}, 
 VOLUME = {99 (5)}, 
 DOI = {10.1103/PhysRevE.99.052412}, 
 ISSN = {2470-0045}, 
 JOURNAL = {PHYSICAL REVIEW. E (PRINT)}, 
}

@ARTICLE{ULLNER_2018_ARTICLE_UPT_344454, 
 AUTHOR = {Ullner, E. and Politi, A. and Torcini, A.}, 
 TITLE = {Ubiquity of collective irregular dynamics in balanced networks of spiking neurons}, 
 YEAR = {2018}, 
 ABSTRACT = {We revisit the dynamics of a prototypical model of balanced activity in networks of spiking neurons. A detailed investigation of the thermodynamic limit for fixed density of connections (massive coupling) shows that, when inhibition prevails, the asymptotic regime is not asynchronous but rather characterized by a self-sustained irregular, macroscopic (collective) dynamics. So long as the connectivity is massive, this regime is found in many different setups: leaky as well as quadratic integrate-and-fire neurons; large and small coupling strength; and weak and strong external currents}, 
 KEYWORDS = {animal, biological model, human nerve cell, nerve cell network, physiology synaptic transmission}, 
 PAGES = {5}, 
 URL = {https://pubs.aip.org/aip/cha/article/28/8/081106/987038/Ubiquity-of-collective-irregular-dynamics-in}, 
 VOLUME = {28 (8)}, 
 DOI = {10.1063/1.5049902}, 
 ISSN = {1054-1500}, 
 JOURNAL = {CHAOS}, 
}

@ARTICLE{PITTORINO_2017_ARTICLE_PIDVB_346214, 
 AUTHOR = {Pittorino, F. and Ibanezberganza, M. and Di Volo, M. and Vezzani, A. and Burioni, R.}, 
 TITLE = {Chaos and Correlated Avalanches in Excitatory Neural Networks with Synaptic Plasticity}, 
 YEAR = {2017}, 
 ABSTRACT = {A collective chaotic phase with power law scaling of activity events is observed in a disordered mean field network of purely excitatory leaky integrate-and-fire neurons with short-term synaptic plasticity. The dynamical phase diagram exhibits two transitions from quasisynchronous and asynchronous regimes to the nontrivial, collective, bursty regime with avalanches. In the homogeneous case without disorder, the system synchronizes and the bursty behavior is reflected into a period doubling transition to chaos for a two dimensional discrete map. Numerical simulations show that the bursty chaotic phase with avalanches exhibits a spontaneous emergence of persistent time correlations and enhanced Kolmogorov complexity. Our analysis reveals a mechanism for the generation of irregular avalanches that emerges from the combination of disorder and deterministic underlying chaotic dynamics}, 
 KEYWORDS = {SELF-ORGANIZED CRITICALITY, INHIBITORY SPIKING NEURONS, PULSE-COUPLED OSCILLATORS, ASYNCHRONOUS STATES, SYNCHRONOUS CHAOS, CORTICAL ACTIVITY, CEREBRAL-CORTEX, DYNAMICS, MODEL, SYNAPSES}, 
 PAGES = {098102-1-098102-5}, 
 URL = {https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.118.098102}, 
 VOLUME = {118 (9)}, 
 DOI = {10.1103/PhysRevLett.118.098102}, 
 ISSN = {1079-7114}, 
 JOURNAL = {PHYSICAL REVIEW LETTERS}, 
}

@ARTICLE{BONACINI_2016_ARTICLE_BBDGSV_315817, 
 AUTHOR = {Bonacini, E. and Burioni, R. and Di Volo, M. and Groppi, M. and Soresina, C. and Vezzani, A.}, 
 TITLE = {How single node dynamics enhances synchronization in neural networks with electrical coupling}, 
 YEAR = {2016}, 
 ABSTRACT = {The stability of the completely synchronous state in neural networks with electrical coupling is analytically investigated applying both the Master Stability Function approach (MSF), developed by Pecora and Carroll (1998), and the Connection Graph Stability method (CGS) proposed by Belykh et al. (2004). The local dynamics is described by Morris-Lecar model for spiking neurons and by Hindmarsh-Rose model in spike, burst, irregular spike and irregular burst regimes. The combined application of both CGS and MSF methods provides an efficient estimate of the synchronization thresholds, namely bounds for the coupling strength ranges in which the synchronous state is stable. In all the considered cases, we observe that high values of coupling strength tend to synchronize the system. Furthermore, we observe a correlation between the single node attractor and the local stability properties given by MSF. The analytical results are compared with numerical simulations on a sample network, with excellent agreement}, 
 KEYWORDS = {Connection Graph Stability, Master Stability Function, Neural network, Synchronization}, 
 PAGES = {32-43}, 
 URL = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84957824050\&partnerID=q2rCbXpz}, 
 VOLUME = {85}, 
 DOI = {10.1016/j.chaos.2016.01.009}, 
 ISSN = {0960-0779}, 
 JOURNAL = {CHAOS, SOLITONS AND FRACTALS}, 
}

@ARTICLE{CHERSI_2013_ARTICLE_CMPB_280810, 
 AUTHOR = {Chersi, F. and Mirolli, M. and Pezzulo, G. and Baldassarre, G.}, 
 TITLE = {A spiking neuron model of the cortico-basal ganglia circuits for goal-directed and habitual action learning}, 
 YEAR = {2013}, 
 ABSTRACT = {In this paper we propose a multi-layer network of spiking neurons that implements in detail the thalamo-cortical circuits that are believed to be involved in action learning and execution. A key feature of this model is that neurons are organized in small pools in the motor cortex and form independent loops with specific pools of the basal ganglia where inhibitory circuits implement a multistep selection mechanism}, 
 KEYWORDS = {Autonomous learning, Goal-directed and habitual actions, Motor sequences, Basal ganglia, Spiking neurons}, 
 PAGES = {212-224}, 
 URL = {https://iris.cnr.it/handle/20.500.14243/280810}, 
 VOLUME = {41}, 
 DOI = {10.1016/j.neunet.2012.11.009}, 
 ISSN = {0893-6080}, 
 JOURNAL = {NEURAL NETWORKS}, 
}

@ARTICLE{MEUCCI_2013_ARTICLE_M_263979, 
 AUTHOR = {Meucci, R.}, 
 TITLE = {Experimental study of firing death in a network of chaotic FitzHugh-Nagumo neurons}, 
 YEAR = {2013}, 
 ABSTRACT = {The FitzHugh-Nagumo neurons driven by a periodic forcing undergo a period-doubling route to chaos and a transition to mixed-mode oscillations. When coupled, their dynamics tend to be synchronized. We show that the chaotically spiking neurons change their internal dynamics to subthreshold oscillations, the phenomenon referred to as firing death. These dynamical changes are observed below the critical coupling strength at which the transition to full chaotic synchronization occurs. Moreover, we find various dynamical regimes in the subthreshold oscillations, namely, regular, quasiperiodic, and chaotic states. We show numerically that these dynamical states may coexist with large-amplitude spiking regimes and that this coexistence is characterized by riddled basins of attraction. The reported results are obtained for neurons implemented in the electronic circuits as well as for the model equations. Finally, we comment on the possible scenarios where the coupling-induced firing death could play an important role in biological systems. DOI: 10. 1103/PhysRevE. 87. 022919}, 
 PAGES = {022919-022919}, 
 URL = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84874546399\&partnerID=q2rCbXpz}, 
 VOLUME = {87 (2)}, 
 DOI = {10.1103/PhysRevE.87.022919}, 
 ISSN = {1539-3755}, 
 JOURNAL = {PHYSICAL REVIEW E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS}, 
}

@ARTICLE{HOUGHTON_2012_ARTICLE_HK_238195, 
 AUTHOR = {Houghton, C. and Kreuz, T.}, 
 TITLE = {On the efficient calculation of van Rossum distances}, 
 YEAR = {2012}, 
 ABSTRACT = {The van Rossum metric measures the distance between two spike trains. Measuring a single van Rossum distance between one pair of spike trains is not a computationally expensive task, however, many applications require a matrix of distances between all the spike trains in a set or the calculation of a multi-neuron distance between two populations of spike trains. Moreover, often these calculations need to be repeated for many different parameter values. An algorithm is presented here to render these calculation less computationally expensive, making the complexity linear in the number of spikes rather than quadratic}, 
 KEYWORDS = {Spiking neurons, information theory, auditory system}, 
 PAGES = {48-58}, 
 URL = {http://informahealthcare.com/doi/abs/10.3109/0954898X.2012.673048}, 
 VOLUME = {23 (1-2)}, 
 DOI = {10.3109/0954898X.2012.673048}, 
 ISSN = {0954-898X}, 
 JOURNAL = {NETWORK}, 
}

@ARTICLE{BIELLA_2009_ARTICLE_BLSB_362399, 
 AUTHOR = {Biella, G. E. M. and Liberati, D. and Storchi, R. and Baselli, G.}, 
 TITLE = {Extraction and Characterization of Essential Discharge Patterns from Multisite Recordings of Spiking Ongoing Activity}, 
 YEAR = {2009}, 
 ABSTRACT = {BackgroundNeural activation patterns proceed often by schemes or motifs distributed across the involved cortical networks. As neurons are correlated, the estimate of all possible dependencies quickly goes out of control. The complex nesting of different oscillation frequencies and their high non-stationariety further hamper any quantitative evaluation of spiking network activities. The problem is exacerbated by the intrinsic variability of neural patterns. Methodology/Principal FindingsOur technique introduces two important novelties and enables to insulate essential patterns on larger sets of spiking neurons and brain activity regimes. First, the sampling procedure over N units is based on a fixed spike number k in order to detect N-dimensional arrays (k-sequences), whose sum over all dimension is k. Then k-sequences variability is greatly reduced by a hierarchical separative clustering, that assigns large amounts of distinct k-sequences to few classes. Iterative separations are stopped when the dimension of each cluster comes to be smaller than a certain threshold. As threshold tuning critically impacts on the number of classes extracted, we developed an effective cost criterion to select the shortest possible description of our dataset. Finally we described three indexes (C, S, R) to evaluate the average pattern complexity, the structure of essential classes and their stability in time. Conclusions/SignificanceWe validated this algorithm with four kinds of surrogated activity, ranging from random to very regular patterned. Then we characterized a selection of ongoing activity recordings. By the S index we identified unstable, moderatly and strongly stable patterns while by the C and the R indices we evidenced their non-random structure. Our algorithm seems able to extract interesting and non-trivial spatial dynamics from multisource neuronal recordings of ongoing and potentially stimulated activity. Combined with time-frequency analysis of LFPs could provide a powerful multiscale approach linking population oscillations with multisite discharge patterns}, 
 KEYWORDS = {Information and computing sciences, Medicine, Mathematics, Neuroscience, characterization, multisite, Extra, patterns, recordings, discharge, spiking}, 
 URL = {https://iris.cnr.it/handle/20.500.14243/362399}, 
 ISSN = {1932-6203}, 
 JOURNAL = {PLOS ONE}, 
}

@ARTICLE{STORCHI_2009_ARTICLE_SBLB_148286, 
 AUTHOR = {Storchi, R. and Biella, G. E. M. and Liberati, D. and Baselli, G.}, 
 TITLE = {Extraction and characterization of essential discharge patterns from multisite recordings of spiking ongoing activity}, 
 YEAR = {2009}, 
 ABSTRACT = {BACKGROUND: Neural activation patterns proceed often by schemes or motifs distributed across the involved cortical networks. As neurons are correlated, the estimate of all possible dependencies quickly goes out of control. The complex nesting of different oscillation frequencies and their high non-stationariety further hamper any quantitative evaluation of spiking network activities. The problem is exacerbated by the intrinsic variability of neural patterns. METHODOLOGY/PRINCIPAL FINDINGS: Our technique introduces two important novelties and enables to insulate essential patterns on larger sets of spiking neurons and brain activity regimes. First, the sampling procedure over N units is based on a fixed spike number k in order to detect N-dimensional arrays (k-sequences), whose sum over all dimension is k. Then k-sequences variability is greatly reduced by a hierarchical separative clustering, that assigns large amounts of distinct k-sequences to few classes. Iterative separations are stopped when the dimension of each cluster comes to be smaller than a certain threshold. As threshold tuning critically impacts on the number of classes extracted, we developed an effective cost criterion to select the shortest possible description of our dataset. Finally we described three indexes (C, S, R) to evaluate the average pattern complexity, the structure of essential classes and their stability in time. CONCLUSIONS/SIGNIFICANCE: We validated this algorithm with four kinds of surrogated activity, ranging from random to very regular patterned. Then we characterized a selection of ongoing activity recordings. By the S index we identified unstable, moderatly and strongly stable patterns while by the C and the R indices we evidenced their non-random structure. Our algorithm seems able to extract interesting and non-trivial spatial dynamics from multisource neuronal recordings of ongoing and potentially stimulated activity. Combined with time-frequency analysis of LFPs could provide a powerful multiscale approach linking population oscillations with multisite discharge patterns}, 
 PAGES = {13}, 
 URL = {https://iris.cnr.it/handle/20.500.14243/148286}, 
 VOLUME = {4}, 
 DOI = {10.1371/journal.pone.0004299.s003}, 
 ISSN = {1932-6203}, 
 JOURNAL = {PLOS ONE}, 
}

@ARTICLE{MIGLIORE_2006_ARTICLE_MCLMH_166408, 
 AUTHOR = {Migliore, M. and Cannia, C. and Lytton, W. and Markram, H. and Hines, M. L.}, 
 TITLE = {Parallel Network simulations with NEURON}, 
 YEAR = {2006}, 
 ABSTRACT = {The NEURON simulation environment has been extended to support parallel network simulations. Each processor integrates the equations for its subnet over an interval equal to the minimum (interprocessor) presynaptic spike generation to postsynaptic spike delivery connection delay. The performance of three published network models with very different spike patterns exhibits superlinear speedup on Beowulf clusters and demonstrates that spike communication overhead is often less than the benefit of an increased fraction of the entire problem fitting into high speed cache. On the EPFL IBM Blue Gene, almost linear speedup was obtained up to 100 processors. Increasing one model from 500 to 40, 000 realistic cells exhibited almost linear speedup on 2000 processors, with an integration time of 9. 8 seconds and communication time of 1. 3 seconds. The potential for speed-ups of several orders of magnitude makes practical the running of large network simulations that could otherwise not be explored}, 
 KEYWORDS = {EVENT-DRIVEN SIMULATION, SPIKING NEURONS, MODEL, CONDUCTANCE}, 
 PAGES = {119-129}, 
 URL = {https://iris.cnr.it/handle/20.500.14243/166408}, 
 VOLUME = {21 (2)}, 
 DOI = {10.1007/s10827-006-7949-5}, 
 ISSN = {0929-5313}, 
 JOURNAL = {JOURNAL OF COMPUTATIONAL NEUROSCIENCE}, 
}

@ARTICLE{ZILLMER_2006_ARTICLE_ZLPT_143611, 
 AUTHOR = {Zillmer, R. and Livi, R. and Politi, A. and Torcini, A.}, 
 TITLE = {Desynchronization in diluted neural networks}, 
 YEAR = {2006}, 
 ABSTRACT = {The dynamical behavior of a weakly diluted fully inhibitory network of pulse-coupled spiking neurons is investigated. Upon increasing the coupling strength, a transition from regular to stochasticlike regime is observed. In the weak-coupling phase, a periodic dynamics is rapidly approached, with all neurons firing with the same rate and mutually phase locked. The strong-coupling phase is characterized by an irregular pattern, even though the maximum Lyapunov exponent is negative. The paradox is solved by drawing an analogy with the phenomenon of 'stable chaos, ' i. e., by observing that the stochasticlike behavior is 'limited' to an exponentially long (with the system size) transient. Remarkably, the transient dynamics turns out to be stationary}, 
 KEYWORDS = {PARTIAL SYNCHRONIZATION, COMPLEX NETWORKS, OSCILLATORS, TRANSIENTS, Neural networks}, 
 PAGES = {10}, 
 URL = {https://journals.aps.org/pre/abstract/10.1103/PhysRevE.74.036203}, 
 VOLUME = {74}, 
 DOI = {10.1103/PhysRevE.74.036203}, 
 ISSN = {1539-3755}, 
 JOURNAL = {PHYSICAL REVIEW E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS}, 
}

@ARTICLE{RODRIGUEZ_2003_ARTICLE_RLD_164634, 
 AUTHOR = {Rodriguez, R. and Lansky, P. and Di Maio, V.}, 
 TITLE = {Vesicular mechanisms and estimates of firing probability in a network of spiking neurons}, 
 YEAR = {2003}, 
 ABSTRACT = {Morris–Lecar neurons. The synaptic transmission is described at the vesicular level. Random number of activated vesicles at synaptic contacts and random quanta of released transmitter are considered. These fluctuations are applied in a form of inhomogeneous Poisson processes, at the time scale of the spike duration. The parameters of these processes depend on the presynaptic spiking activity and on the strength of afferent connections. It is shown how synchronization of the activity in the network appears. A statistical analysis of spiking times is performed, showing smooth mean behavior of response frequencies. Adiffusion approximation of the network Poissonian process is derived from which an analytical formula for firing probability is calculated}, 
 KEYWORDS = {Neural Model, Synaptic Vesicle, Diffusion Approxim., Poisson Process, Neural Network}, 
 PAGES = {132-145}, 
 URL = {https://iris.cnr.it/handle/20.500.14243/164634}, 
 VOLUME = {181}, 
 ISSN = {0167-2789}, 
 JOURNAL = {PHYSICA D-NONLINEAR PHENOMENA}, 
}

@INCOLLECTION{CARFORA_2023_INCOLLECTION_C_460492, 
 AUTHOR = {Carfora, M. F.}, 
 TITLE = {A Review of Stochastic Models of Neuronal Dynamics: From a Single Neuron to Networks}, 
 YEAR = {2023}, 
 ABSTRACT = {After giving some background on neuron physiology, the classical (deterministic) models for the generation of action potentials are briefly introduced and their limitations discussed, so to motivate the need for a stochastic description of the neuronal firing activity. The more relevant stochastic models for single neuron dynamics are reviewed, with particular attention to the phenomenon of spike-frequency adaptation. Then some approaches to the modeling of network dynamics, where populations of excitatory and inhibitory neurons interact, are described. Finally, some recent models applying suitable strategies to reproduce complex neural dynamics emerging from networks of spiking neurons, such as fractional differentiation or other memory effects, are introduced as a perspective for current and future research}, 
 KEYWORDS = {stochastic neuron, leaky-integrate-and-fire model, spike-frequency adaptation, network dynamics}, 
 PAGES = {137-152}, 
 URL = {https://iris.cnr.it/handle/20.500.14243/460492}, 
 DOI = {10.1007/978-3-031-33050-6_8}, 
 PUBLISHER = {Springer (Cham, Heidelberg, New York, Dordrecht, London, CHE)}, 
 ISBN = {978-3-031-33049-0}, 
 CONFERENCE_PLACE = {Cham, Heidelberg, New York, Dordrecht, London}, 
 BOOKTITLE = {Trends in Biomathematics: Modeling Epidemiological, Neuronal, and Social Dynamics}, 
 EDITOR = {Mondaini, R. P.}, 
}

@INPROCEEDINGS{ARENA_2018_INPROCEEDINGS_APSV_364721, 
 AUTHOR = {Arena, P. and Patanè, L. and Sanalitro, D. and Vitanza, A.}, 
 TITLE = {Insect-Inspired Body Size Learning Model on a Humanoid Robot}, 
 YEAR = {2018}, 
 ABSTRACT = {In this paper an insect-inspired body size learning algorithm is adopted in a humanoid robot and a control system, mainly developed with spiking neurons, is proposed. It implements an evaluation of distances by using the typical parallax method performed by different insect species, such as Drosophila melanogaster. A Darwin-OP robot was used as testbed to demonstrate the potential application of the learning method on a humanoid structure. The robot, equipped with a hand extension, was free to move in an environment to discover objects. As consequence, it was able to learn, using an operant conditioning, which objects can be reached, via the estimation of their distance on varying the length of the equipped tool. The learning scheme was tested both in a dynamical simulation environment and with the Darwin-OP robot}, 
 URL = {https://iris.cnr.it/handle/20.500.14243/364721}, 
 DOI = {10.1109/BIOROB.2018.8487635}, 
}

@INPROCEEDINGS{CISZAK_2013_INPROCEEDINGS_CETM_266683, 
 AUTHOR = {Ciszak, M. and Euzzor, S. and Tito Arecchi, F. and Meucci, R.}, 
 TITLE = {Control of dynamical states in a network: firing death and multistability}, 
 YEAR = {2013}, 
 ABSTRACT = {We show that the chaotically spiking neurons coupled in a ring configuration changes their internal dynamics to subthreshold oscillations, the phenomenon referred to as firing death. These dynamical changes are observed below the critical coupling strength at which the transition to full chaotic synchronization occurs. We find various dynamical regimes in the subthreshold oscillations, namely, regular, quasi-periodic and chaotic states. We show numerically that these dynamical states may coexist with large amplitude spiking regimes and that this coexistence is characterized by riddled basins of attraction. Moreover, we show that under a particular coupling configuration, the neural network exhibits bistability between two configurations of clusters. Each cluster composed of two neurons undergoes independent chaotic spiking dynamics. As an appropriate external perturbation is applied to the system, the network undergoes changes in the clusters configuration, involving different neurons at each time. We hypothesize that the winning cluster of neurons, responsible for perception, is that exhibiting higher mean frequency. The clusters features may contribute to an increase of local field potential in the neural network. The reported results are obtained for neurons implemented in the electronic circuits as well as for the model equations}, 
 KEYWORDS = {neural network, chaotic spiking, network topology, bistable attractors}, 
 URL = {http://lib.physcon.ru/doc?id=368eac35b3ff}, 
 CONFERENCE_NAME = {6th International Conference on Physics and Control (PhysCon 2013)}, 
}

@INPROCEEDINGS{CHELLA_2007_INPROCEEDINGS_CRO_13667, 
 AUTHOR = {Chella, A. and Rizzo, R. and Oliveri, A.}, 
 TITLE = {An Application of Spike-Timing-Dependent Plasticity to Readout Circuit for Liquid State Machine}, 
 YEAR = {2007}, 
 ABSTRACT = {Liquid State Machine (LSM) is a neural system based on spiking neurons that implements a mapping between functions of time. A typical application of LSM is classification of time functions obtained observing the state of the liquid by using a memoryless readout circuit, usually implemented by a linear perceptron. Due to the high number of neurons in the liquid the training of the readout is difficult. In this paper we show that using the Spike-Timing-Dependent Plasticity (STDP) a single neuron with short training session can be used to recognize the state of the liquid due to an input signal. Using STDP it is possible to identify the spikes timing of the neurons in the liquid and this allows to correctly classify a large set of input signals, the method is also robust to noise and amplitude variations}, 
 KEYWORDS = {SPike neural netwroks}, 
 PAGES = {1441-1445}, 
 URL = {http://biblioproxy.cnr.it:2093/xpl/articleDetails.jsp?arnumber=4371170}, 
 DOI = {10.1109/IJCNN.2007.4371170}, 
 PUBLISHER = {IEEE (New York, USA)}, 
 ISBN = {978-1-4244-1379-9}, 
 CONFERENCE_NAME = {IEEE International Joint Conference on Neural Networks}, 
 CONFERENCE_PLACE = {New York}, 
 BOOKTITLE = {Proceedings of International Joint Conference on Neural Networks, 2007}, 
}

@INPROCEEDINGS{RIANO_2006_INPROCEEDINGS_RRC_83776, 
 AUTHOR = {Riano, L. and Rizzo, R. and Chella, A.}, 
 TITLE = {A new Unsupervised Neural Network for Pattern Recognition with Spiking Neurons}, 
 YEAR = {2006}, 
 ABSTRACT = {In this paper we propose a three-layered neural network for binary pattern recognition and memorization. Unlike the classic approach to pattern recognition, our net works organizing itself in an unsupervised way, to distinguish beetween different patterns or to recognize similar ones. If we present a binary input to the first layer, after some time steps we could read the output of the net in the third layer, as one and only one neuron activating with high firing rate; the middle layer will act as a generalization layer, i. e. similar pattern will have similar (or the same) representation in the middle layer. We used learning algorithms inspired from other works or from biological data to achieve network stability and a correct pattern memorization. The network can be used for pattern recognition or generalization by selecting output signals from the selection layer or the generalization layer}, 
 KEYWORDS = {SPike neural netwroks}, 
 PAGES = {3903-3910}, 
 URL = {http://biblioproxy.cnr.it:2093/xpl/articleDetails.jsp?arnumber=1716636}, 
 DOI = {10.1109/IJCNN.2006.246888}, 
 PUBLISHER = {IEEE Computer Society (Washington, DC, USA)}, 
 ISBN = {0-7803-9490-9}, 
 CONFERENCE_NAME = {IEEE International Joint Conference on Neural Networks. IJCNN '06}, 
 CONFERENCE_PLACE = {Washington, DC}, 
 BOOKTITLE = {Proceedings of International Joint Conference on Neural Networks, 2006}, 
}

@INPROCEEDINGS{RODRIGUEZ_2002_INPROCEEDINGS_RLD_83110, 
 AUTHOR = {Rodriguez, R. and Lansky, P. and Di Maio, V.}, 
 TITLE = {Vesicular mechanisms and estimates of firing probability in a network of spiking neurons}, 
 YEAR = {2002}, 
 URL = {https://iris.cnr.it/handle/20.500.14243/83110}, 
 CONFERENCE_NAME = {ECMTB2002}, 
}

@INPROCEEDINGS{CALIGIORE_2021_INPROCEEDINGS_CM_401253, 
 AUTHOR = {Caligiore, D. and Mirino, P.}, 
 TITLE = {How the cerebellum and prefrontal cortex cooperate during associative learning}, 
 YEAR = {2021}, 
 ABSTRACT = {Brief description of a spiking neurons computational model developed to study the interactions between the cerebellum and cortical areas during associative learning}, 
 KEYWORDS = {Cerebellum, prefrontal cortex, motor cortex, associative learning, spiking neural networks, artificial intelligence}, 
 URL = {https://iris.cnr.it/handle/20.500.14243/401253}, 
 DOI = {10.7490/f1000research.1118552.1}, 
 CONFERENCE_NAME = {International Neuroinformatics Coordinating Facility (INCF) Assembly}, 
}