Selective hippocampal lesions disrupt a novel cue effect but fail to eliminate blocking in rabbit eyeblink conditioning

Allen, Merrill J.; Padilla, Yahaira; Myers, Catherine E.; Gluck, Mark A.

doi:10.3758/cabn.2.4.318

Cited by 11 publications

(13 citation statements)

References 41 publications

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“…The blocking effect can be eliminated by extending the conditioning of the second phase, which made it longer, as shown in Figure 12 and confirmed experimentally by [ 38 , 41 , 42 ]. Although lesioning the CHCQ model did not affect the output CR, as shown in Figure 13 , this is supported by the experimental findings of [ 43 , 44 , 45 ].…”

Section: Resultssupporting

confidence: 73%

Cortico-Hippocampal Computational Modeling Using Quantum Neural Networks to Simulate Classical Conditioning Paradigms

Khalid

Ali

et al. 2020

Brain Sciences

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Most existing cortico-hippocampal computational models use different artificial neural network topologies. These conventional approaches, which simulate various biological paradigms, can get slow training and inadequate conditioned responses for two reasons: increases in the number of conditioned stimuli and in the complexity of the simulated biological paradigms in different phases. In this paper, a cortico-hippocampal computational quantum (CHCQ) model is proposed for modeling intact and lesioned systems. The CHCQ model is the first computational model that uses the quantum neural networks for simulating the biological paradigms. The model consists of two entangled quantum neural networks: an adaptive single-layer feedforward quantum neural network and an autoencoder quantum neural network. The CHCQ model adaptively updates all the weights of its quantum neural networks using quantum instar, outstar, and Widrow–Hoff learning algorithms. Our model successfully simulated several biological processes and maintained the output-conditioned responses quickly and efficiently. Moreover, the results were consistent with prior biological studies.

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Section: Resultssupporting

confidence: 73%

Cortico-Hippocampal Computational Modeling Using Quantum Neural Networks to Simulate Classical Conditioning Paradigms

Khalid

Ali

et al. 2020

Brain Sciences

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“…In conditioning, animals with hippocampal lesions do not show performance decrements typically found in healthy organisms when conditioned stimuli are presented in novel contexts (M. T. Allen, Padilla, Myers, & Gluck, 2002;Penick & Solomon, 1991).…”

Section: A Common Principle Of Cognition: Theoretical Implicationsmentioning

confidence: 98%

Binding of intrinsic and extrinsic features in working memory.

Ecker¹,

Maybery²,

Zimmer³

2013

Journal of Experimental Psychology: General

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There is ongoing debate concerning the mechanisms of feature binding in working memory. In particular, there is controversy regarding the extent to which these binding processes are automatic. The present article demonstrates that binding mechanisms differ depending on whether the to-be-integrated features are perceived as forming a coherent object. We presented a series of experiments that investigated the binding of color and shape, whereby color was either an intrinsic feature of the shape or an extrinsic feature of the shape's background. Results show that intrinsic color affected shape recognition, even when it was incidentally studied and irrelevant for the recognition task. In contrast, extrinsic color did not affect shape recognition, even when the association of color and shape was encoded and retrievable on demand. This strongly suggests that binding of intrinsic intra-item information but not extrinsic contextual information is obligatory in visual working memory. We highlight links to perception as well as implicit and explicit long-term memory, which suggest that the intrinsic-extrinsic dimension is a principle relevant to multiple domains of human cognition.

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“…One prominent feature of hippocampal neuron activity is a clear sensitivity to stimulus novelty (Knight, 1996; Stern et al, 1996; Dolan and Fletcher, 1997; Vinogradova, 2001; Rutishauser et al, 2006), suggesting that the hippocampus may act as a novelty detector (Parkin, 1997; Kumaran and Maguire, 2007). Supporting this idea, novelty exposure influences synaptic plasticity in the hippocampus (Manahan-Vaughan and Braunewell, 1999; Lemon and Manahan-Vaughan, 2006), and hippocampal-lesioned animals display deficits in novelty detection (Buhusi et al, 1998; Allen et al, 2002; Hunsaker et al, 2008). The novelty-dependent activation of hippocampal neurons is likely to be a critical feature for learning, allowing circuit modifications that optimize stimulus prediction.…”

Section: Introductionmentioning

confidence: 94%

Functional division of hippocampal area CA1 via modulatory gating of entorhinal cortical inputs

Itô

Schuman

2011

Hippocampus

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The hippocampus receives two streams of information, spatial and nonspatial, via major afferent inputs from the medial (MEC) and lateral entorhinal cortexes (LEC). The MEC and LEC projections in the temporoammonic pathway are topographically organized along the transverseaxis of area CA1. The potential for functional segregation of area CA1, however, remains relatively unexplored. Here, we demonstrated differential novelty-induced c-Fos expression along the transverse-axis of area CA1 corresponding to topographic projections of MEC and LEC inputs. We found that, while novel place exposure induced a uniform c-Fos expression along the transverse-axis of area CA1, novel object exposure primarily activated the distal half of CA1 neurons. In hippocampal slices, we observed distinct presynaptic properties between LEC and MEC terminals, and application of either DA or NE produced a largely selective influence on one set of inputs (LEC). Finally, we demonstrated that differential c-Fos expression along the transverse axis of area CA1 was largely abolished by an antagonist of neuromodulatory receptors, clozapine. Our results suggest that neuromodulators can control topographic TA projections allowing the hippocampus to differentially encode new information along the transverse axis of area CA1.

show abstract

Selective hippocampal lesions disrupt a novel cue effect but fail to eliminate blocking in rabbit eyeblink conditioning

Cited by 11 publications

References 41 publications

Cortico-Hippocampal Computational Modeling Using Quantum Neural Networks to Simulate Classical Conditioning Paradigms

Cortico-Hippocampal Computational Modeling Using Quantum Neural Networks to Simulate Classical Conditioning Paradigms

Binding of intrinsic and extrinsic features in working memory.

Functional division of hippocampal area CA1 via modulatory gating of entorhinal cortical inputs

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