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Journal of Computational Neuroscience

Erschienen in:

01.06.2011

Capacity analysis in multi-state synaptic models: a retrieval probability perspective

verfasst von: Yibi Huang, Yali Amit

Erschienen in: Journal of Computational Neuroscience | Ausgabe 3/2011

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Abstract

We define the memory capacity of networks of binary neurons with finite-state synapses in terms of retrieval probabilities of learned patterns under standard asynchronous dynamics with a predetermined threshold. The threshold is set to control the proportion of non-selective neurons that fire. An optimal inhibition level is chosen to stabilize network behavior. For any local learning rule we provide a computationally efficient and highly accurate approximation to the retrieval probability of a pattern as a function of its age. The method is applied to the sequential models (Fusi and Abbott, Nat Neurosci 10:485–493, 2007) and meta-plasticity models (Fusi et al., Neuron 45(4):599–611, 2005; Leibold and Kempter, Cereb Cortex 18:67–77, 2008). We show that as the number of synaptic states increases, the capacity, as defined here, either plateaus or decreases. In the few cases where multi-state models exceed the capacity of binary synapse models the improvement is small.

Vorheriger Artikel Compositionality of arm movements can be realized by propagating synchrony

Nächster Artikel Spiking neural network simulation: memory-optimal synaptic event scheduling

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The fraction of non-selective neurons above the threshold is not specified in the criterion since it has been controlled in the threshold selection. Moreover, because of the strong inhibition, there cannot be many non-selective neurons above threshold throughout the dynamics.

The amount of allowable non-selective neurons above the threshold is not specified in the criterion since it has been controlled in the threshold selection.

Abraham, W. C., & Bear, M. F. (1996). Metaplasticity: The plasticity of synaptic plasticity. Trends in Neurosciences, 19(4), 126–130. doi:10.1016/S0166-2236(96)80018-X.PubMedCrossRef

Amit, D. J., & Brunel, N. (1997a). Dynamics of recurrent network of spiking neurons before and following learning. Network, 8, 373–404.CrossRef

Amit, D. J., & Brunel, N. (1997b). Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex. Cerebral Cortex, 7, 237–252.PubMedCrossRef

Amit, D. J., & Fusi, S. (1994). Learning in neural networks with material synapses. Neural Computation, 6, 957–982.CrossRef

Amit, D. J., & Mongillo, G. (2003). Selective delay activity in the cortex: Phenomena and interpretation. Cerebral Cortex, 13, 1139–1150.PubMedCrossRef

Amit, Y., & Huang, Y. (2010). Precise capacity analysis in binary networks with multiple coding level inputs. Neural Computation, 22(3), 660–688. doi:10.1162/neco.2009.02-09-967.PubMedCrossRef

Attwell, D., & Laughlin, S. B. (2001). An energy budget for signaling in the grey matter of the brain. Journal of Cerebral Blood Flow and Metabolism, 21(10), 1133–1145.PubMed

Barnes, C. A., McNaughton, B. L., Mizumori, S. J., Leonard, B. W., & Lin, L. H. (1990). Comparison of spatial and temporal characteristics of neuronal activity in sequential stages of hippocampal processing. Progress in Brain Research, 83, 287–300.PubMedCrossRef

Barret, A. B., & van Rossum, M. C. (2008). Optimal learning rules for discrete synapses. PLoS Computational Biology, 4, 1–7.CrossRef

Ben Dayan Rubin, D. D., & Fusi, S. (2007). Long memory lifetimes require complex synapses and limited sparseness. Frontiers in Computational Neuroscience, 1, 1–14.CrossRef

Brunel, N. (2003). Dynamics and plasticity of stimulus-selective persistent activity in cortical network models. Cerebral Cortex, 13, 1151–1161.PubMedCrossRef

Curti, E., Mongillo, G., La Camera, G., & Amit, D. J. (2004). Mean-field and capacity in realistic networks of spiking neurons storing sparsely coded random memories. Neural Computation, 16, 2597–2637.PubMedCrossRef

Del Giudice, P., Fusi, S., & Mattia, M. (2003). Modelling the formation of working memory with networks of integrate-and-fire neurons connected by plastic synapses. Journal of Physiology Paris, 97(4–6), 659–681. doi:10.1016/j.jphysparis.2004.01.021.CrossRef

Fusi, S., & Abbott, L. F. (2007). Limits on the memory storage capacity of bounded synapses. Nature Neuroscience, 10, 485–493.PubMed

Fusi, S., Drew, P. J., & Abbott, L. (2005). Cascade models of synaptically stored memories. Neuron, 45(4), 599–611. doi:10.1016/j.neuron.2005.02.001.PubMedCrossRef

Fuster, J. (1995). Memory in the cerebral cortex: An empirical approach to neural networks in the human and nonhuman primate. Cambridge, MA: MIT Press.

Jung, M. W., & McNaughton, B. L. (1993). Spatial selectivity of unit activity in the hippocampal granular layer. Hippocampus, 3(0), 165–182.PubMedCrossRef

Leibold, C., & Kempter, R. (2008). Sparseness constrains the prolongation of memory lifetime via synaptic metaplasticity. Cerebral Cortex 18, 67–77.PubMedCrossRef

Lennie P (2003) The cost of cortical computation. Current Biology, 13(6), 493–497. doi:10.1016/S0960-9822(03)00135-0.PubMedCrossRef

Miyashita, Y., & Hayashi, T. (2000). Neural representation of visual objects: Encoding and top-down activation. Current Opinion in Neurobiology, 10(2), 187–194.PubMedCrossRef

Olshausen, B. A., & Field, D. J. (2004). Sparse coding of sensory inputs. Current Opinion in Neurobiology, 14(4), 481–487. doi:10.1016/j.conb.2004.07.007.PubMedCrossRef

Quiroga, R. Q., Reddy, L., Kreiman, G., Koch, C., & Fried, I. (2005). Invariant visual representation by single neurons in the human brain. Nature, 435, 1102–1107.PubMedCrossRef

Robertson, T., Wright, F. T., & Dykstra, R. L. (1988). Order restricted statistical inference. Wiley.

Rolls, E. T., & Tovee, M. J. (1995). Sparseness of the neuronal representation of stimuli in the primate temporal visual cortex. Journal of Physiology, 73(2), 713–726.

Romani, S., Amit, D., & Amit, Y. (2008). Optimizing one-shot learning with binary synapses. Neural Computation, 20, 1928–1950.PubMedCrossRef

Sato, T., Uchida, G., & Tanifuji, M. (2007). The nature of neuronal clustering in inferotemporal cortex of macaque monkey revealed by optical imaging and extracellular recording. In 34th Ann. meet. of soc. for neuroscience. San Diego, USA.

Wang, X. J. (2001). Synaptic reverberation underlying mnemonic persistent activity. Trends in Neurosciences, 24(8), 455–463. doi:10.1016/S0166-2236(00)01868-3.PubMedCrossRef

Willshaw, D., Buneman, O. P., & Longuet-Higgins, H. (1969). Non-holographic associative memory. Nature (London), 222, 960–962.CrossRef

Titel: Capacity analysis in multi-state synaptic models: a retrieval probability perspective
verfasst von: Yibi Huang
Yali Amit
Publikationsdatum: 01.06.2011
Verlag: Springer US
Erschienen in: Journal of Computational Neuroscience / Ausgabe 3/2011
Print ISSN: 0929-5313
Elektronische ISSN: 1573-6873
DOI: https://doi.org/10.1007/s10827-010-0287-7

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