RICERCA ITALIANA     

Programmi di ricerca simili:

• 1 - Metodi stocastici in finanza
• 2 - Modellizzazione Matematica del Comportamento Naturale e Artificiale
• 3 - Analisi e simulazione di modelli dinamici con aspettative eterogenee
• 4 - MODELLI, STRATEGIE E RAPPRESENTAZIONI NELL' EPISTEMOLOGIA DELLA RICERCA SCIENTIFICA
• 5 - Metodologie statistiche per l'analisi di impatto e la valutazione della regolamentazione
• 6 - Modelli non lineari in economia e finanza; interazioni, complessità e previsioni
• 7 - Campi aleatori, evoluzioni stocastiche ed applicazioni a modelli di sistemi interagenti.
• 8 - Integrazione delle informazioni nel sistema visivo: modelli computazionali dei processi sensoriali e decisionali
• 9 - METODI E MODELLI STATISTICI PER LA VALUTAZIONE DEI PROCESSI FORMATIVI
• 10 - Il comportamento adattativo dei sistemi biologici e il metodo scientifico: il ruolo dei modelli formali e materiali, le spiegazioni funzionali, gli approcci disciplinari e l'unificazione

Classificazione scientifico-disciplinare

• Area scientifico disciplinare: Scienze matematiche e informatiche
  • INFORMATICA
  • PROBABILITÀ E STATISTICA MATEMATICA
• Area scientifico disciplinare: Scienze biologiche

Classificazione brevettuale

• PHYSICS
  • COMPUTING; CALCULATING; COUNTING (score computers for games A63; combinations of writing applicances with computing devices B43K29/08)
    • COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS [N0004]
  • EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS (devices for psychotechnics or for testing reaction times A61B5/16; games, sports, amusements A63; projectors, projector screens G03B)

Classificazione geografica

• Regione: Campania

Bibliografia

[Abeles and Gat, 2001] Abeles M., Gat I. (2001) Detecting precise firing sequences in experimental data. J. Neurosc. Meth. 107 (1-2), 141-154.

[Arbib, 2002] Arbib M.A. (2002) Handbook of Brain Theory and Neural Networks, Second Edition. The MIT Press, Cambridge, pp. 1290.

[Borst and Theunissen, 1999] Borst A., Theunissen F.E. (1999) Information theory and neural coding. Nature Neuroscience, 2(11), pp. 947-957.

[Buonocore and Visentin, 1992] Buonocore A., Visentin F. (1992) On the numerical evaluation of first-passage-time probability densities for one-dimensional diffusion processes. Ricerche Mat. Vol. 41, no. 1, pp. 147-161.

[Buonocore et al., 2003] Buonocore A., Esposito G., Giorno V., Valerio C. (2003) Towards dead time inclusion in neuronal modeling. Scientiae Mathematicae Japonicae 58, no. 2, 323-334.

[Buonocore et al., 2004] Buonocore A., Caputo L., Ishii Y., Pirozzi E., Yanagida T., Ricciardi L.M. (2004) On Myosin II dynamics in the presence of external loads. BioSystems. (to appear).

[Burkitt, 2001] Burkitt A.N. (2001) Balanced neurons: analysis of leaky integrate-and-fire neurons with reversal potential. Biological Cybernetics 85, pp. 247-255.

[Capocelli and Ricciardi, 1971] Capocelli R.M., Ricciardi L.M (1971) Diffusion approximation and first passage time problem for a model neuron. Kybernetic 8, 214-223.

[Cartling, 1997] Cartling B. (1997) Control of computational dynamics of coupled integrate-and-fire neurons. Biological Cybernetics 76, pp. 383-395.

[Dayan and Abbott, 2001] Dayan P., Abbott L.F. (2001) Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. The MIT Press, 1st edition.

[Di Crescenzo and Longobardi, 2002] Di Crescenzo A., Longobardi M. (2002) Entropy-based measure of uncertainty in past lifetime distributions. J. Appl. Probab. 39, no. 2, 434-440.

[Di Crescenzo and Longobardi, 2004] Di Crescenzo A, Longobardi M. (2004) A measure of discrimination between past lifetime distributions. Statistics and Probability Letters. Vol. 67, pp. 173-182 ISSN: 0167-7152.

[Di Crescenzo et al., 2004] Di Crescenzo A., Longobardi M., Nastro A. (2004). On the mutual information between residual lifetime distributions. In Cybernetics and Systems 2004 (Trappl R., ed.) Vol. 1, pp. 142-145. ISBN: 3-85206-169-5. Austrian Society for Cybernetics Studies, Vienna, Austria.

[Di Crescenzo et al., 2005] Di Crescenzo A., Martinucci B., Pirozzi E. (2005) On the dynamics of a pair of coupled neurons subject to alternating input rates. BioSystems 79, pp. 109-116.

[Di Nardo et al., 2003a] Di Nardo E., Nobile A.G., Pirozzi E., Ricciardi L.M. (2003) On the asymptotic behavior of first passage time densities for stationary Gaussian processes and varying boundaries. Methodology and Computing in Applied Probability, 5, pp. 211-233.

[Di Nardo et al., 2003b] Di Nardo E., Nobile A.G., Pirozzi E., Ricciardi L.M. (2003) Towards the modeling of neuronal firing by Gaussian processes. Scientiae Mathematicae Japonicae 58, no. 2, pp. 255-264.

[Elson et al., 1998] Elson R.C., Selverston A.I., Huerta R., Rulkov N.F., Rabinovich M.I., Abarbanel H.D.I. (1998) Synchronous Behavior of Two Coupled Biological Neurons. Physical Review Letters 81(25), pp. 5692-5695.

[Giorno et al., 1990] Giorno V., Nobile A.G., Ricciardi L.M. (1990) On the asymptotic behaviour of first-passage-time densities for one-dimensional diffusion processes and varying boundaries. Adv. Appl. Prob. 22, pp. 883-914.

[Giorno et al., 2004] Giorno V., Nobile A.G., Pirozzi E., Ricciardi L.M. (2004) Non-stationary Gauss-Markov Processes and Neuronal Modeling. In Cybernetics and Systems 2004 (Trappl R., ed.). EMCSR 2004 Austrian Society for Cybernetics Studies, Vienna, ISBN 3 85206 169 5, 211-215.

[Giraudo and Sacerdote, 1997] Giraudo M.T., Sacerdote L. (1997) Jump-Diffusion processes as models for neuronal activity. BioSystems 40, 75-82.

[Giraudo et al., 2001] Giraudo M.T., Sacerdote L., Zucca C. (2001) Evaluation of first passage times of diffusion processes through boundaries by means of a totally simulative algorithm. Methodology and Computing in Applied Probability. 3, 215-231.

[Johnson and Glantz, 2002] Johnson D.H., Glantz R.M. (2004) When does interval coding occur? Neurocomputing Vol. 59-60, pp. 13-18.

[Ricciardi et al., 1999] Ricciardi L.M., Di Crescenzo A., Giorno V., Nobile A.G. (1999) An outline of theoretical and algorithmic approaches to first passage time problems with applications to biological modeling. Math. Japonica 50, pp. 247-322.

[Ricciardi et al., 2003] Ricciardi L.M., Esposito G., Giorno V., Valerio C. (2003) Modeling Neuronal Firing in the Presence of Refractoriness. In Computational Methods in Neural Modeling (J. Mira, J.R. Alvarez Eds.) Proceeding of 7th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2003. (Mao, Menorca, Spain, June 2003). Lecture Notes in Computer Sciences Vol. 2686, 1-8, Springer.

[Sacerdote and Sirovich, 2003] Sacerdote L., Sirovich R (2003) Multimodality of the interspike interval distribution in a simple jump-diffusion model Scientiae Mathematicae Japonicae Vol. 58, 307-322.

[Sacerdote and Smith, 2000] Sacerdote L., Smith C.E. (2000) New Parameter Relationships Determined Via Stochastic Ordering for Spike Activity in a Reversal Potential Model. BioSystems 58, 59-65.

[Sacerdote and Smith, 2004] Sacerdote L., Smith C.E. (2004) Almost sure comparisons for first passage times of diffusion processes through boundaries. Methodology and Computing in Applied Probability 6, 323-341.

[Smith and Smith, 1984] Smith C.E., Smith M.V. (1984) Moments of voltage trajectories for Stein's model with synaptic reversal potential. J. Theoretical Neurobiology 3, pp. 67-77.

[Stein, 1965] Stein R.B. (1965) A theoretical analysis of neuronal variability, Biophys. J. 5, pp. 385-386.

[Tuckwell, 1979] Tuckwell H. (1979) Synaptic transmission in a model for stochastic neural activity. J. Theor. Biol. 77, pp. 65-81.

[Tuckwell, 1988] Tuckwell H.C. (1988) Introduction to theoretical neurobiology, Vol. 2, Nonlinear and stochastic theories. Cambridge University Press, Cambridge.

Parole Chiave

CIBERNETICA; MODELLI NEURONALI; REFRATTARIETA'; MISURE DI INFORMAZIONE; SIMULAZIONE; AFFIDABILITA'; CODIFICA DI INFORMAZIONE; TRENI DI SPIKES; TEMPI DI SPARO