@ARTICLE{COPPOLINO_2023_ARTICLE_CM_482557, 
 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}, 
 PUBLISHER = {Pergamon, New York, Stati Uniti d'America}, 
 JOURNAL = {Neural networks}, 
}

@ARTICLE{COPPOLINO_2022_ARTICLE_CGM_456837, 
 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 Hippocampus Learning systems Microprocessors Navigation Neurons 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}, 
 PUBLISHER = {Institute of Electrical and Electronics Engineers,-New York, NY, USA, Stati Uniti d'America}, 
 JOURNAL = {IEEE Transactions on Neural Networks and Learning Systems}, 
}

@ARTICLE{BI_2021_ARTICLE_BDT_461993, 
 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 = {752261-1-752261-20}, 
 URL = {https://www.frontiersin.org/articles/10.3389/fnsys.2021.752261/full}, 
 VOLUME = {15}, 
 DOI = {10.3389/fnsys.2021.752261}, 
 PUBLISHER = {Frontiers Research Foundation, Lausanne, Svizzera}, 
 JOURNAL = {Frontiers in systems neuroscience}, 
}

@ARTICLE{TRIMARCO_2021_ARTICLE_TMC_461075, 
 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}, 
 PUBLISHER = {Frontiers Research Foundation, Lausanne, Svizzera}, 
 JOURNAL = {Frontiers in systems neuroscience}, 
}

@ARTICLE{ARENA_2019_ARTICLE_ACPPS_414365, 
 AUTHOR = {Arena, P. and Cali, M. and Patane, L. and Portera, A. and Spinosa, A. G.}, 
 TITLE = {A CNN-based neuromorphic model for classification and decision control}, 
 YEAR = {2019}, 
 ABSTRACT = {In this paper, an insect brain-inspired computational structure was developed. The peculiarity of the core processing layer is the local connectivity among the spiking neurons, which allows for a representation under the cellular nonlinear network paradigm. Moreover, the processing layer works as a liquid state network with fixed internal connections and trainable output weights. Learning was accomplished by adopting a simple supervised, batch approach based on the calculation of the Moore-Penrose matrix. The architecture, taking inspiration from a specific neuropile of the insect brain, the mushroom bodies, is evaluated and compared with other standard and bio-inspired solutions present in the literature, referring to three different scenarios.}, 
 PAGES = {1999-2017}, 
 URL = {http://www.scopus.com/inward/record.url?eid=2-s2.0-85063648707\&partnerID=q2rCbXpz}, 
 VOLUME = {95}, 
 DOI = {10.1007/s11071-018-4673-4}, 
 PUBLISHER = {Kluwer, Dordrecht, Paesi Bassi}, 
 JOURNAL = {Nonlinear dynamics (Dordr., Online)}, 
}

@ARTICLE{CALIGIORE_2019_ARTICLE_CMB_403214, 
 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}, 
 URL = {https://www.frontiersin.org/articles/10.3389/fnins.2019.00550/full}, 
 VOLUME = {13}, 
 DOI = {10.3389/fnins.2019.00550}, 
 PUBLISHER = {Frontiers Research Foundation, Lausanne, Svizzera}, 
 JOURNAL = {Frontiers in neuroscience (Online)}, 
}

@ARTICLE{LUCCIOLI_2019_ARTICLE_LAT_403614, 
 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}, 
 URL = {https://publications.cnr.it/doc/403614}, 
 VOLUME = {99}, 
 DOI = {10.1103/PhysRevE.99.052412}, 
 PUBLISHER = {American Physical Society, Ridge, NY, Stati Uniti d'America}, 
 JOURNAL = {Physical review. E (Print)}, 
}

@ARTICLE{ULLNER_2018_ARTICLE_UPT_396038, 
 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 = {081106-1-081106-5}, 
 URL = {https://arxiv.org/pdf/1711.01096.pdf}, 
 VOLUME = {28}, 
 DOI = {10.1063/1.5049902}, 
 PUBLISHER = {American Institute of Physics, Woodbury, NY, Stati Uniti d'America}, 
 JOURNAL = {Chaos (Woodbury N. Y.)}, 
}

@ARTICLE{PITTORINO_2017_ARTICLE_PIDVB_384255, 
 AUTHOR = {Pittorino, F. and Ibanez Berganza, 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}, 
 DOI = {10.1103/PhysRevLett.118.098102}, 
 PUBLISHER = {American Physical Society, College Park, MD, Stati Uniti d'America}, 
 JOURNAL = {Physical review letters}, 
}

@ARTICLE{BONACINI_2016_ARTICLE_BBDGSV_354169, 
 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}, 
 PUBLISHER = {Pergamon., Oxford, Regno Unito}, 
 JOURNAL = {Chaos, solitons and fractals}, 
}

@ARTICLE{CHERSI_2013_ARTICLE_CMPB_311484, 
 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 = {Dual-system theories postulate that actions are supported either by a goal-directed or by a habit-driven response system. Neuroimaging and anatomo-functional studies have provided evidence that the prefrontal cortex plays a fundamental role in the first type of action control, while internal brain areas such as the basal ganglia are more active during habitual and overtrained responses. Additionally, it has been shown that areas of the cortex and the basal ganglia are connected through multiple parallel "channels", which are thought to function as an action selection mechanism resolving competitions between alternative options available in a given context.}, 
 KEYWORDS = {Autonomous learning, Goal-directed and habitual actions, Motor sequences, Basal ganglia, Spiking neurons}, 
 PAGES = {212-224}, 
 URL = {https://publications.cnr.it/doc/311484}, 
 VOLUME = {41}, 
 DOI = {10.1016/j.neunet.2012.11.009}, 
 PUBLISHER = {Pergamon, New York, Stati Uniti d'America}, 
 JOURNAL = {Neural networks}, 
}

@ARTICLE{CISZAK_2013_ARTICLE_CEAM_293996, 
 AUTHOR = {Ciszak, M. and Euzzor, S. and Arecchi, F. T. and 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}, 
 DOI = {10.1103/PhysRevE.87.022919}, 
 PUBLISHER = {Published by the American Physical Society through the American Institute of Physics, Melville, NY, Stati Uniti d'America}, 
 JOURNAL = {Physical review. E, Statistical, nonlinear, and soft matter physics (Print)}, 
}

@ARTICLE{HOUGHTON_2012_ARTICLE_HK_194198, 
 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}, 
 DOI = {10.3109/0954898X.2012.673048}, 
 PUBLISHER = {Informa Healthcare, [London], Regno Unito}, 
 JOURNAL = {Network (Bristol. Print)}, 
}

@ARTICLE{BIELLA_2009_ARTICLE_BLSB_404830, 
 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, Extraction, patterns, recordings, discharge, spiking}, 
 URL = {https://publications.cnr.it/doc/404830}, 
 PUBLISHER = {Public Library of Science, San Francisco, CA, Stati Uniti d'America}, 
 JOURNAL = {PloS one}, 
}

@ARTICLE{STORCHI_2009_ARTICLE_SBLB_171691, 
 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 = {e4299}, 
 URL = {https://publications.cnr.it/doc/171691}, 
 VOLUME = {4}, 
 DOI = {10.1371/journal.pone.0004299.s003}, 
 PUBLISHER = {Public Library of Science, San Francisco, CA, Stati Uniti d'America}, 
 JOURNAL = {PloS one}, 
}

@ARTICLE{AMIT_2007_ARTICLE_AR_2292, 
 AUTHOR = {Amit, D. and Romani, S.}, 
 TITLE = {Search for fMRI BOLD signals in networks of spiking neurons}, 
 YEAR = {2007}, 
 ABSTRACT = {In a recent experiment, functional magnetic resonance imaging blood oxygen level-dependent (fMRI BOLD) signals were compared in different cortical areas (primary-visual and associative), when subjects were required covertly to name images in two protocols: sequences of images, with and without intervening delays. The amplitude of the BOLD signal in protocols with delay was found to be closer to that without delays in associative areas than in primary areas. The present study provides an exploratory proposal for the identification of the neural activity substrate of the BOLD signal in quasi-realistic networks of spiking neurons, in networks sustaining selective delay activity (associative) and in networks responsive to stimuli, but whose unique stationary state is one of spontaneous activity (primary). A variety of observables are 'recorded' in the network simulations, applying the experimental stimulation protocol. The ratios of the candidate BOLD signals, in the two protocols, are compared in networks with and without delay activity. There are several options for recovering the experimental result in the model networks. One common conclusion is that the distinguishing factor is the presence of delay activity. The effect of NMDAr is marginal. The ultimate quantitative agreement with the experiment results depends on a distinction of the baseline signal level from its value in delay-period spontaneous activity. This may be attributable to the subjects' attention. Modifying the baseline results in a quantitative agreement for the ratios, and provided a definite choice of the candidate signals. The proposed framework produces predictions for the BOLD signal in fMRI experiments, upon modification of the protocol presentation rate and the form of the response function.}, 
 KEYWORDS = {WORKING-MEMORY, PERSISTENT ACTIVITY, NEURAL-NETWORK, CORTEX, MODEL}, 
 PAGES = {1882-1892}, 
 URL = {https://publications.cnr.it/doc/2292}, 
 VOLUME = {25}, 
 PUBLISHER = {Published on behalf of the European Neuroscience Association by Oxford University Press, Oxford, Regno Unito}, 
 JOURNAL = {European journal of neuroscience (Print)}, 
}

@ARTICLE{MIGLIORE_2006_ARTICLE_MCLMH_9527, 
 AUTHOR = {Migliore, M. and Cannia, C. and Lytton, W. 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://publications.cnr.it/doc/9527}, 
 VOLUME = {21}, 
 DOI = {10.1007/s10827-006-7949-5}, 
 PUBLISHER = {Kluwer Academic Publishers, Boston, Stati Uniti d'America}, 
 JOURNAL = {Journal of computational neuroscience}, 
}

@ARTICLE{ROMANI_2006_ARTICLE_RAM_958, 
 AUTHOR = {Romani, S. and Amit, D. and Mongillo, G.}, 
 TITLE = {Mean-field analysis of selective persistent activity in presence of short-term synaptic depression}, 
 YEAR = {2006}, 
 ABSTRACT = {Mean-Field theory is extended to recurrent networks of spiking neurons endowed with short-term depression (STD) of synaptic transmission. The extension involves the use of the distribution of interspike intervals of an integrate-and-fire neuron receiving a Gaussian current, with a given mean and variance, in input. This, in turn, is used to obtain an accurate estimate of the resulting postsynaptic current in presence of STD. The stationary states of the network are obtained requiring self-consistency for the currents-those driving the emission processes and those generated by the emitted spikes. The model network stores in the distribution of two-state efficacies of excitatory-to-excitatory synapses, a randomly composed set of external stimuli. The resulting synaptic structure allows the network to exhibit selective persistent activity for each stimulus in the set. Theory predicts the onset of selective persistent, or working memory (WM) activity upon varying the constitutive parameters (e.g. potentiated/depressed long-term efficacy ratio, parameters associated with STD), and provides the average emission rates in the various steady states. Theoretical estimates are in remarkably good agreement with data 'recorded' in computer simulations of the microscopic model.}, 
 KEYWORDS = {NEOCORTICAL PYRAMIDAL NEURONS, CORTICAL NETWORK MODEL, OBJECT WORKING-MEMORY, LOW SPIKE RATES, FIRE NEURONS}, 
 PAGES = {201-217}, 
 URL = {https://publications.cnr.it/doc/958}, 
 VOLUME = {20}, 
 PUBLISHER = {Kluwer Academic Publishers, Boston, Stati Uniti d'America}, 
 JOURNAL = {Journal of computational neuroscience}, 
}

@ARTICLE{ZILLMER_2006_ARTICLE_ZLPT_166994, 
 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 = {036203}, 
 URL = {https://publications.cnr.it/doc/166994}, 
 VOLUME = {74}, 
 DOI = {10.1103/PhysRevE.74.036203}, 
 PUBLISHER = {Published by the American Physical Society through the American Institute of Physics, Melville, NY, Stati Uniti d'America}, 
 JOURNAL = {Physical review. E, Statistical, nonlinear, and soft matter physics (Print)}, 
}

@ARTICLE{RODRIGUEZ_2003_ARTICLE_RLD_18610, 
 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://publications.cnr.it/doc/18610}, 
 VOLUME = {181}, 
 PUBLISHER = {North-Holland, Amsterdam, Paesi Bassi}, 
 JOURNAL = {Physica. D, Nonlinear phenomena (Print)}, 
}

@INCOLLECTION{CARFORA_2023_INCOLLECTION_C_487039, 
 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}, 
 URL = {https://publications.cnr.it/doc/487039}, 
 DOI = {10.1007/978-3-031-33050-6_8}, 
 PUBLISHER = {Springer, Cham, Heidelberg, New York, Dordrecht, London, CHE}, 
 ISBN = {978-3-031-33049-0}, 
 EDITOR = {Mondaini, R. P.}, 
}

@INPROCEEDINGS{CISZAK_2013_INPROCEEDINGS_CEAM_304034, 
 AUTHOR = {Ciszak, M. and Euzzor, S. and Arecchi, F. T. 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)}, 
 CONFERENCE_PLACE = {San Luis Potosi, Mexico}, 
 CONFERENCE_DATE = {26-29th August 2013}, 
}

@INPROCEEDINGS{CHELLA_2007_INPROCEEDINGS_CRO_180406, 
 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 = {Orlando, USA}, 
 CONFERENCE_DATE = {12-17 August 2007}, 
}

@INPROCEEDINGS{RIANO_2006_INPROCEEDINGS_RRC_77539, 
 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 = {Vancouver}, 
 CONFERENCE_DATE = {16-21 July 2006}, 
}

@INPROCEEDINGS{RODRIGUEZ_2002_INPROCEEDINGS_RLD_78548, 
 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://publications.cnr.it/doc/78548}, 
 CONFERENCE_NAME = {ECMTB2002}, 
 CONFERENCE_PLACE = {Milano}, 
 CONFERENCE_DATE = {2002}, 
}

@INPROCEEDINGS{CALIGIORE_2021_INPROCEEDINGS_CM_453663, 
 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://publications.cnr.it/doc/453663}, 
 DOI = {10.7490/f1000research.1118552.1}, 
 CONFERENCE_NAME = {International Neuroinformatics Coordinating Facility (INCF) Assembly}, 
 CONFERENCE_DATE = {21/04/2021}, 
}