LTD windows of the STDP learning rule and synaptic connections having a large transmission delay enable robust sequence learning amid background noise

Hayashi, H.; Igarashi, Jun

doi:10.1007/s11571-009-9076-2

Cited by 15 publications

(11 citation statements)

References 19 publications

(30 reference statements)

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“…3C). Although this collapse was observed for theta-band LFP power, it can be safely assumed that the synchronization in spiking output is reduced at the same time, which may lead to a net weakening of synapses between OFC and target cells, consistent with STDP principles (Abbott and Nelson, 2000;Hayashi and Igarashi, 2009). Predictive synchronized firing after sampling of the other odor, previously coupled to quinine but now to reward, is regenerated at the moment of unpredicted reward (Fig.…”

Section: Discussionmentioning

confidence: 76%

Theta-Band Phase Locking of Orbitofrontal Neurons during Reward Expectancy

Wingerden¹,

Vinck²,

Lankelma³

et al. 2010

J. Neurosci.

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The expectancy of a rewarding outcome following actions and cues is coded by a network of brain structures including the orbitofrontal cortex. Thus far, predicted reward was considered to be coded by time-averaged spike rates of neurons. However, besides firing rate, the precise timing of action potentials in relation to ongoing oscillations in local field potentials is thought to be of importance for effective communication between brain areas.We performed multineuron and field potential recordings in orbitofrontal cortex of rats performing olfactory discrimination learning to study the temporal structure of coding predictive of outcome. After associative learning, field potentials were marked by theta oscillations, both in advance and during delivery of reward. Orbitofrontal neurons, especially those coding information about upcoming reward with their firing rate, phase locked to these oscillations in anticipation of reward. When established associations were reversed, phase locking collapsed in the anticipatory task phase, but returned when reward became predictable again after relearning. Behaviorally, the outcome anticipation phase was marked by licking responses, but the frequency of lick responses was dissociated from the strength of theta-band phase locking. The strength of theta-band phase locking by orbitofrontal neurons robustly follows the dynamics of associative learning as measured by behavior and correlates with the rat's current outcome expectancy. Theta-band phase locking may facilitate communication of outcome-related information between reward-related brain areas and offers a novel mechanism for coding value signals during reinforcement learning.

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Section: Discussionmentioning

confidence: 76%

Theta-Band Phase Locking of Orbitofrontal Neurons during Reward Expectancy

Wingerden¹,

Vinck²,

Lankelma³

et al. 2010

J. Neurosci.

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show abstract

“…Still, requiring that active granule cells express multiple fields seems to lead, in another simple network model (of how dentate activity may result from entorhinal cortex input [25]), to the necessity of inputs coming from lateral EC, as well as from medial EC. The lateral EC inputs need not carry any spatial information but help to select the DG cells active in one environment.…”

Section: Discussionmentioning

confidence: 99%

How Informative Are Spatial CA3 Representations Established by the Dentate Gyrus?

Cerasti

Treves

2010

PLoS Comput Biol

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In the mammalian hippocampus, the dentate gyrus (DG) is characterized by sparse and powerful unidirectional projections to CA3 pyramidal cells, the so-called mossy fibers. Mossy fiber synapses appear to duplicate, in terms of the information they convey, what CA3 cells already receive from entorhinal cortex layer II cells, which project both to the dentate gyrus and to CA3. Computational models of episodic memory have hypothesized that the function of the mossy fibers is to enforce a new, well separated pattern of activity onto CA3 cells, to represent a new memory, prevailing over the interference produced by the traces of older memories already stored on CA3 recurrent collateral connections. Can this hypothesis apply also to spatial representations, as described by recent neurophysiological recordings in rats? To address this issue quantitatively, we estimate the amount of information DG can impart on a new CA3 pattern of spatial activity, using both mathematical analysis and computer simulations of a simplified model. We confirm that, also in the spatial case, the observed sparse connectivity and level of activity are most appropriate for driving memory storage – and not to initiate retrieval. Surprisingly, the model also indicates that even when DG codes just for space, much of the information it passes on to CA3 acquires a non-spatial and episodic character, akin to that of a random number generator. It is suggested that further hippocampal processing is required to make full spatial use of DG inputs.

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“…Learning sequences and transitions in the Hippocampus was explored previously [126], [127]. However, these studies ignored spatial information or GCs.…”

Section: Discussionmentioning

confidence: 99%

Multi-Transition Systems: A theory for neural spatial navigation

Waniek¹

2017

Preprint

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Abstract-Spatial navigation is considered fundamental for animals and is attributed primarily to place and grid cells in the rodent brain. Commonly believed to either perform path integration or localization, the true objective of grid cells, their hexagonal grid fields, and especially their discrete scales remain puzzling. Here it is proposed that grid cells efficiently encode transitions in sequences. A biologically plausible model for dendritic computation in grid cells is presented. A network of competitive cells shows positive gridness scores early in simulations and realigns the orientation of all cells over time. Then, a scale-space model of grid cells is introduced. It improves behaviorally questionable run-times of a single scale significantly by look-ahead in multiple scales, and it is shown that the optimal scale-increment between consecutive scales is √ 2. Finally, a formal theory for sequences and transitions is stated. It is demonstrated that hexagonal transition encoders are optimal to encode transitions in Euclidean space and emerge due to the sampling theorem. The paper concludes with a discussion about the suggested purpose, makes testable predictions, and highlights relevant connections to computational neuroscience as well as computer science and robotics.

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LTD windows of the STDP learning rule and synaptic connections having a large transmission delay enable robust sequence learning amid background noise

Cited by 15 publications

References 19 publications

Theta-Band Phase Locking of Orbitofrontal Neurons during Reward Expectancy

Theta-Band Phase Locking of Orbitofrontal Neurons during Reward Expectancy

How Informative Are Spatial CA3 Representations Established by the Dentate Gyrus?

Multi-Transition Systems: A theory for neural spatial navigation

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