Mean-field analysis of selective persistent activity in presence of short-term synaptic depression

Journal of Computational Neuroscience

Volumn 20, Issue 2, 2006, Pages 201-217

  Romani, Sandro ^a   Amit, Daniel J ^b,c   Mongillo, Gianluigi ^d,e  

^a SAPIENZA UNIVERSITY OF ROME   (Italy)

^b UNIVERSITÀ DI ROMA LA SAPIENZA   (Italy)

^c HEBREW UNIVERSITY OF JERUSALEM   (Israel)

^d INSTITUT DES SCIENCES COGNITIVES   (France)

^e UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

Mean field analysis; Overlapping memories; Selective persistent activity; Spiking neuron; STD

Indexed keywords

NEURAL NETWORKS; NEURONS;

'CURRENT; MEAN FIELD ANALYSIS; MEAN-FIELD THEORIES; OVERLAPPING MEMORY; PERSISTENT ACTIVITIES; RECURRENT NETWORKS; SELECTIVE PERSISTENT ACTIVITY; SHORT-TERM DEPRESSION; SPIKING NEURONES; SYNAPTIC DEPRESSION;

MEAN FIELD THEORY;

ACCURACY; ANALYSIS OF VARIANCE; ARTICLE; BRAIN NERVE CELL; COMPUTER SIMULATION; CONTROLLED STUDY; FIELD STUDY; NERVE STIMULATION; NEUROTRANSMISSION; NORMAL DISTRIBUTION; PREDICTION; PRIORITY JOURNAL; RANDOMIZATION; SPIKE WAVE; STEADY STATE; SYNAPSE; WORKING MEMORY;

EID: 33646536945     PISSN: 09295313     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10827-006-6308-x     Document Type: Article

References (40)

1. Abbott LF, Varela JA, Sen K, Nelson SB (1997) Synaptic depression and cortical gain control. Science 275: 220-224.
2. Amit DJ (1998) Simulation in neurobiology - theory or experiment? TINS 21: 231-237.
3. Amit DJ, Bernacchia A, Yakovlev V (2003) Multiple working memory - model for behavioral experiment. Cerebral Cortex 13: 435-443.
4. Amit DJ, Brunel N (1997a) Dynamics of recurrent network of spiking neurons before and following learning. Network 8: 373-404.
5. Amit DJ, Brunel N (1997b) Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex. Cereb. Cortex 7: 237-252.
6. Amit DJ, Mongillo G (2003a) Selective delay activity in the cortex: Phenomena and Interpretation. Cereb. Cortex 13: 1139-1150.
7. Amit DJ, Mongillo G (2003b) Spike-driven synaptic dynamics generating working memory states. Neural Comp. 15: 565-596.
8. Amit DJ, Tsodyks MV (1991a) Quantitative study of attractor neural networks retrieving at low spike rates I: Substrate spikes, rates and neurons gain. Network 2: 259-274.
9. Amit DJ, Tsodyks MV (1991b) Quantitative study of attractor neural networks retrieving at low spike rates. II: Low-rate retrieval in symmetric networks. Network 2: 275-294.
10. Bibitchkov D, Herrmann JM, Geisel T (2002) Pattern storage and processing in attractor networks with short-time synaptic dynamics. Network 13: 115-129.
11. Brunel N (2003) Dynamics and plasticity of stimulus-selective persistent activity in cortical network models. Cerebral Cortex 13: 1151-1161.
12. Brunel N, Carusi F, Fusi S (1998) Slow stochastic hebbian learning of classes of stimuli in a recurrent neural network. Network 9: 123-152.
13. Brunel N, Hakim V (1999) Fast global oscillations in networks of integrate-and-fire neurons with low firing rates. Neural Computation 11: 1621-1671.
14. Brunel N, Sergi S (1998) Firing frequency of leaky integrate-and-fire neurons with synaptic currents dynamics. J. Theor. Biol. 195: 87-95.
15. Brunel N, Wang X-J (2001) Effects of neuromodulation in a cortical network model of object working memory dominated by recurrent inhibition. J. Comput. Neurosci. 11: 63-85.
16. Compte A, Brunel N, Goldman-Rakic PS, Wang X-J (2000) Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. Cereb. Cortex 10: 910-923.
17. Compte A, Constantinidis C, Tegner J, Raghavachari S, Chafee MV, Goldman-Rakic PS, Wang X-J (2003) Temporally irregular mnemonic persistent activity in prefrontal neurons of monkeys during a delayed response task. J. Neurophysiol. 90(5): 3441-3454.
18. Cox D, Miller HD (1977) The Theory of stochastic processes. CRC Press.
19. Curti E, Mongillo G, La Camera G, Amit DJ (2004) Mean-field and capacity in realistic networks of spiking neurons storing sparsely coded random memories. Neural Comp. 16: 2597-2637.
20. Erickson CA, Desimone R (1999) Responses of macaque perirhinal neurons during and after visual stimulus association learning. J. Neurosci. 19: 10404-10416.
21. Fusi S, Annunziato M, Badoni D, Salamon A, Amit DJ (2000) Spike-driven synaptic plasticity: Theory, simulation, VLSI implementation. Neural Comp. 12: 2227-2258.
22. Galarreta M, Hestrin S (1998) Frequency-dependent synaptic depression and the balance of excitation and inhibition in the neocortex. Nat. Neurosci. 1: 587-594.
23. Kistler WM, van Hemmen JL (1999) Short-term synaptic plasticity and network behavior. Neural Computation 11: 1579-1594.
24. Loebel A, Tsodyks M (2002) Computation by ensemble synchronization in recurrent networks with synaptic depression. J. Comput. Neurosci. 13: 111-124.
25. Markram H, Wang Y, Tsodyks M (1998) Differential signaling via the same axon of neocortical pyramidal neurons. PNAS 95: 5323-5328.
26. Mongillo G, Amit DJ, Brunel N (2003) Retrospective and prospective persistent activity induced by Hebbian learning in a recurrent cortical network. Eur. J. Neurosci. 18: 2011-2024.
27. Mongillo G, Curti E, Romani S, Amit DJ (2005) Learning in realistic networks of spiking neurons and spike-driven plastic synapses. Eur. J. Neurosci. 21: 3143-3160.
28. Moreno-Bote R, Parga N (2004) Role of synaptic filtering on the firing response of simple model neurons. Phys. Rev. Lett. 92: 028102.
29. Pantic L, Torres JJ, Kappen HJ, Gielen SC (2002) Associative memory with dynamic synapses. Neural Computation 14: 2903-2923.
30. Patie P (2004) On some First Passage Time Problems Motivated by Financial Applications. PhD thesis, ETH Zurich.
31. Rao SC, Rainer G, Miller EK (1997) Integration of what and where in the primate prefrontal cortex. Science 276: 821-824.
32. Rauch A, La Camera G, Luscher H-R, Senn W, Fusi S (2003) Neocortical pyramidal cells respond as integrate-and-fire neurons to in vivo-like input currents. J. Neurophysiol. 90: 1598-1612.
33. Renart A, Brunel N, Wang X-J (2003) Computational Neuroscience: A Comprehensive Approach, chapter 15. CRC press, pp. 431-490.
34. Tsodyks MV, Markram H (1997) The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. PNAS 94: 719-723.
35. Tsodyks MV, Pawelzik K, Markram H (1998) Neural networks with dynamic synapses. Neural Comp. 10: 821-835.
36. Tsodyks MV, Uziel A, Markram H (2000) Synchrony generation in recurrent networks with frequency-dependent synapses. J. Neurosci. 20(RC50): 1-5.
37. Tuckwell HC (1988). Introduction to theoretical neurobiology. Cambridge University Press.
38. van Vreeswijk CA, Sompolinsky H (1996) Chaos in neural networks with balanced excitatory and inhibitory activity. Science 274: 1724-1726.
39. Varela JA, Song S, Turrigiano GG, Nelson SB (1999) Differential depression at excitatory and inhibitory synapses in visual cortex. J. Neurosci. 19: 4293-4304.
40. Wang X-J (1999) Synaptic basis of cortical persistent activity: The importance of NMDA receptors to working memory. J. Neurosci. 19: 9587-9603.