Context codes and the effect of noisy learning on a simplified hippocampal CA3 model

Wu, Xiaogang; Baxter, Robert A.; Levy, William B.

doi:10.1007/s004220050228

Cited by 8 publications

(16 citation statements)

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“…The hippocampal component of the CLS model is part of a long tradition of hippocampal modeling (e.g., Marr, 1971;McNaughton & Morris, 1987;Rolls, 1989;Levy, 1989;Touretzky & Redish, 1996;Burgess & O'Keefe, 1996;Wu, Baxter, & Levy, 1996;Treves & Rolls, 1994;Moll & Miikkulainen, 1997;Hasselmo & Wyble, 1997). Although different hippocampal models may differ slightly in the functions they ascribe to particular hippocampal subcomponents, a remarkable consensus has emerged regarding how the hippocampus supports episodic memory (i.e., by assigning minimally overlapping CA3 representations to different episodes, with recurrent connectivity serving to bind together the constituent features of those episodes).…”

Section: Models Of Hippocampusmentioning

confidence: 99%

Modeling hippocampal and neocortical contributions to recognition memory: A complementary-learning-systems approach.

Norman¹,

O’Reilly²

2003

Psychological Review

1,186

121

1,342

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We present a computational neural network model of recognition memory based on the biological structures of the hippocampus and medial temporal lobe cortex (MTLC), which perform complementary learning functions. The hippocampal component of the model contributes to recognition by recalling specific studied details. MTLC can not support recall, but it is possible to extract a scalar familiarity signal from MTLC that tracks how well the test item matches studied items. We present simulations that establish key qualitative differences in the operating characteristics of the hippocampal recall and MTLC familiarity signals, and we identify several manipulations (e.g., target-lure similarity, interference) that differentially affect the two signals. We also use the model to address the stochastic relationship between recall and familiarity (i.e., are they independent), and the effects of partial vs. complete hippocampal lesions on recognition.

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Section: Models Of Hippocampusmentioning

confidence: 99%

Modeling hippocampal and neocortical contributions to recognition memory: A complementary-learning-systems approach.

Norman¹,

O’Reilly²

2003

Psychological Review

1,186

121

1,342

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“…This self-organization is similar in principle, but differs in its anatomical focus on intrinsic rather than afferent connections. This focus on self-organization of intrinsic connections has been used in models of hippocampus (Levy, 1996;Wallenstein & Hasselmo, 1997;Wu et al, 1996) and piriform cortex (Linster et al, 1996). However, given that most excitatory connections even in primary visual cortex are intrinsic, it is likely that this type of self-organization is very important for regions such as primary visual cortex.…”

Section: Relationship To Cholinergic Modulation Of Self-organization mentioning

confidence: 99%

“…Computational modeling has demonstrated that memory capacity can also be enhanced by recruitment of additional neurons, which are not directly activated by afferent input, through a self-organizing process (Carpenter & Grossberg, 1993;Levy, 1996;Wallenstein & Hasselmo, 1997;Wu, Baxter, & Levy, 1996). In this paper, self-organization refers to models in which the pattern of activity is not directly imposed by external input.…”

Section: Introductionmentioning

confidence: 99%

Enhanced cholinergic suppression of previously strengthened synapses enables the formation of self-organized representations in olfactory cortex

Linster

Maloney

Patil

et al. 2003

Neurobiology of Learning and Memory

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Computational modeling assists in analyzing the specific functional role of the cellular effects of acetylcholine within cortical structures. In particular, acetylcholine may regulate the dynamics of encoding and retrieval of information by regulating the magnitude of synaptic transmission at excitatory recurrent connections. Many abstract models of associative memory function ignore the influence of changes in synaptic strength during the storage process and apply the effect of these changes only during a socalled recall-phase. Efforts to ensure stable activity with more realistic, continuous updating of the synaptic strength during the storage process have shown that the memory capacity of a realistic cortical network can be greatly enhanced if cholinergic modulation blocks transmission at synaptic connections of the association fibers during the learning process. We here present experimental data from an olfactory cortex brain slice preparation showing that previously potentiated fibers show significantly greater suppression (presynaptic inhibition) by the cholinergic agonist carbachol than unpotentiated fibers. We conclude that low suppression of non-potentiated fibers during the learning process ensures the formation of self-organized representations in the neural network while the higher suppression of previously potentiated fibers minimizes interference between overlapping patterns. We show in a computational model of olfactory cortex, that, together, these two phenomena reduce the overlap between patterns that are stored within the same neural network structure. These results further demonstrate the contribution of acetylcholine to mechanisms of cortical plasticity. The results are consistent with the extensive evidence supporting a role for acetylcholine in encoding of new memories and enhancement of response to salient sensory stimuli.

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“…Simulations, to be successful, must demonstrate flexible, goal-code-dependent recall. Successive patterns of recurrent activity called local context codes are characteristic of our CA3 model's ability to resolve the inherent ambiguity of this task [20]. Local context neurons are recurrent neurons that identify a subsequence of a larger sequence, usually by firing repetitively in response to its particular input subsequence [9].…”

Section: Overview Of the Modelmentioning

confidence: 99%

“…Synaptic weights are modified using a temporally asymmetric rule of association [22]. This asymmetry allows the recurrent network model to form context codes, which are critical to its problem-solving capabilities [8,9,20]. The Hebbian-like learning rule is…”

Section: A the Network Modelmentioning

confidence: 99%

T-maze training of a recurrent CA3 model reveals the necessity of novelty-based modulation of LTP in hippocampal region CA3

Monaco

Levy

Proceedings of the International Joint Conference on Neural Networks, 2003.

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Abstract-The rat hippocampus has been shown to mediate a large set of spatial navigation tasks such as the simple T-maze. We investigated the performance of a minimal computational, but biologically based, model of CA3 on this task. For successful performance, the model needs to generate and maintain neuronal codes for each of the two arms of the T-maze. Moreover, each code must be distinctively recalled in a goal-dependent manner. The development of such neuronal codes is aided by the appearance of repetitively firing recurrent neurons -known as local context units, analogous to hippocampal place cells -which promote spatiotemporal association within the T-maze training sequences. The number, longevity, and connectivity of local context units exclusively coding for each arm of the maze grow with training. Although with too much training, the coding for one arm uncontrollably dominates over the other code, and goal appropriate choice-behavior is lost. That is, successful network codes can easily deteriorate with overtraining. The amount of training that produces this deterioration in performance depends on other network parameters. Rather than a failure of the model, we believe these results tell us something important about the biology of the hippocampal system. That is, this result provides support for the hypothesis of a hippocampal afferent system which down-regulates LTP once a task has been successfully learned. Modulatory systems (e.g., dopaminergic, generally D1/D5r) exist which are candidates for this functional role.

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Context codes and the effect of noisy learning on a simplified hippocampal CA3 model

Cited by 8 publications

References 0 publications

Modeling hippocampal and neocortical contributions to recognition memory: A complementary-learning-systems approach.

Modeling hippocampal and neocortical contributions to recognition memory: A complementary-learning-systems approach.

Enhanced cholinergic suppression of previously strengthened synapses enables the formation of self-organized representations in olfactory cortex

T-maze training of a recurrent CA3 model reveals the necessity of novelty-based modulation of LTP in hippocampal region CA3

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