Comparison of computational models of familiarity discrimination in the perirhinal cortex
Abstract
This study compares the efficiency and plausibility of published computational models of familiarity discrimination in the perirhinal cortex. Substantial evidence indicates that the perirhinal cortex is involved in both the familiarity discrimination aspect of recognition memory and in perceptual functions involved with representations of complete stimuli (i.e., object identification). Published models of how the perirhinal cortex may perform familiarity discrimination can be divided into two groups. The first group assumes that a proportion of perirhinal neurons form a network specialised just for familiarity discrimination (these models may be based on Hebbian or anti‐Hebbian synaptic plasticity). In contrast, the second group assumes that both familiarity discrimination and learning representations of complete stimuli are performed within a single combined network. This study establishes that when the responses of neurons that provide input to the familiarity discrimination network are correlated (as indicated by experimental data), specialised networks based on anti‐Hebbian learning may recognise the previous occurrence of many more stimuli (i.e., have a capacity up to thousands of times larger) than specialised networks based on Hebbian learning. The currently published combined models do not learn an optimal stimulus representation (they do not fully extract statistically independent features), and hence their capacities are even lower than those of the specialised models based on Hebbian learning. Hence, the combined models published thus far are critically less efficient than the specialised models based on anti‐Hebbian learning. This study also compares the consistency of the models with experimental observations concerning what is known of synaptic plasticity in the perirhinal cortex and the responses of its neurons. Many theoretically important parameters remain undetermined, and experiments are suggested to provide information critical for refining and distinguishing between the various models. However, the above theoretical arguments and currently published data favour the existence of a separate network specialised for familiarity discrimination. Hippocampus 2003;13:494–524. © 2003 Wiley‐Liss, Inc.
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This depression of synaptic efficacy could contribute to the repetition suppression of neural responses in areas such as the perirhinal cortex.
“This depression of synaptic efficacy could contribute to the repetition suppression of neural responses in areas such as the perirhinal cortex (Bogacz and Brown, 2003; Warburton et al., 2003).”
From Modes and models of forebrain cholinergic neuromodulation of cognition (2011) · cites this paper as Bogacz and Brown, 2003
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“…longer term familiarity, or novelty (Xiang and Brown, 1998), and the calculation of episodic familiarity as measured in the laboratory very likely utilizes all of these signals (for instance, computationally, episodic familiarity can be conceptualized as 1/novelty (cf. Bogacz and Brown, 2003).”
Barker, G. R. I., Bashir, Z. I., Brown, M. W., & Warburton, E. C. (2006) · Learning & Memory · doi:10.1101/lm.77806 · Cited by 88 · see citing works
“2000), and it has been hypothesized (Brown and Bashir 2002; Bogacz and Brown 2003) that the mechanisms used in the production of LTD in”
MethodSelf-citationNeuronal Responses Related to Long-Term Recognition Memory Processes in Prefrontal Cortex
Xiang, J.-Z., & Brown, M. W. (2004) · Neuron · doi:10.1016/j.neuron.2004.05.013 · Cited by 86 · see citing works
Self-citationBrown, M. W., Barker, G. R. I., Aggleton, J. P., & Warburton, E. C. (2012) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2012.07.034 · Cited by 84 · see citing works
“However, the potential storage capacity of perirhinal cortex for familiarity discrimination of objects is sufficiently high that such storage might reside permanently within the perirhinal cortex (Androulidakis et al., 2008; Bogacz & Brown, 2003).”
BackgroundSelf-citationBenzodiazepine impairment of perirhinal cortical plasticity and recognition memory
Wan, H., Warburton, E. C., Zhu, X. O., Koder, T., Park, Y., Aggleton, J. P., Cho, K., Bashir, Z. I., & Brown, M. W. (2004) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2004.03688.x · Cited by 81 · see citing works
“Further, theoretical modelling (Bogacz & Brown, 2003) indicates that familiarity discrimination could be performed by networks relying on synapse-specific plasticity involving mechanisms such as those underlying LTP or long-term depression (LTD).”
BackgroundSelf-citationMoncada, D., & Viola, H. (2006) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2005.12.002 · Cited by 65 · see citing works
“The eYciency and speed of novelty detection confer an evolutionary advantage that provide a reason for the existence of a familiarity discrimination network in addition to those networks used for recollection (Bogacz & Brown, 2003; Brown & Aggleton, 2001).”
BackgroundSeoane, A., Massey, P. V., Keen, H., Bashir, Z. I., & Brown, M. W. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.5199-08.2009 · Cited by 62 · see citing works
“…2007), our investigation appears to be the first reporting a role of L-type VDCCs in both LTD and depotentiation in PRH. Synaptic weakening, as is produced by LTD or depotentiation, has been argued to underlie perirhinal familiarity discrimination (Brown and Bashir, 2002; Bogacz and Brown, 2003).”
BackgroundSelf-citationPriority coding in the visual system
Rust, N. C., & Cohen, M. R. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00582-9 · Cited by 55 · see citing works
In search of a recognition memory engram
Brown, M. W., & Banks, P. J. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2014.09.016 · Cited by 56 · see citing works
Self-citationVisual novelty, curiosity, and intrinsic reward in machine learning and the brain
Jaegle, A., Mehrpour, V., & Rust, N. C. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.08.004 · Cited by 54 · see citing works
“…largely mediated by the hippocampus, familiarity is thought to be mediated by another brain area in the medial temporal lobe, perirhinal cortex, as well its input from the part of the visual system involved in signaling object and scene information, inferotemporal cortex (IT) [reviewed by 36,37].”
BackgroundMeta-learning synaptic plasticity and memory addressing for continual familiarity detection
Tyulmankov, D., Yang, G. R., & Abbott, L. F. (2021) · Neuron · doi:10.1016/j.neuron.2021.11.009 · Cited by 54 · see citing works
“556 B Lead contact B Materials availability B Data and code availability d METHOD DETAILS B Continual familiarity detection task B HebbFF and RNN training B Bogacz-Brown (Bogacz and Brown, 2003) model implementation B Training FLD and SCC decoders B Simultaneous classification and familiarity detection B Idealized mod…”
BackgroundMethodResultInfluentialIntegrating Incremental Learning and Episodic Memory Models of the Hippocampal Region.
Meeter, M., Myers, C. E., & Gluck, M. A. (2005) · Psychological Review · doi:10.1037/0033-295x.112.3.560 · Cited by 52 · see citing works
“Bogacz and Brown (2002, 2003) also proposed another model, in which familiarity neurons activated strongly by a novel stimulus undergo LTD instead of LTP.”
BackgroundMethodInfluentialPerception and Memory in the Ventral Visual Stream and Medial Temporal Lobe
Martin, C. B., & Barense, M. D. (2023) · Annual Review of Vision Science · doi:10.1146/annurev-vision-120222-014200 · Cited by 46 · see citing works
Muñoz, P., Aspé-Sánchez, M., Contreras, L. S., & Palacios, A. G. (2010) · Biological Research · doi:10.4067/s0716-97602010000200013 · Cited by 49 · see citing works
“In consideration of the presence of high levels of Dnmt3a and Dnmt1 in postmitotic neurons (Feng et al., 2005), we speculate that increased methylation in the hippocampus could be mediated by Dnmt3a or Dnmt1, playing an important role in DNA methylation in the BDNF promoter when neuronal activity is suppressed, as sug…”
BackgroundInfluentialLulham, A., Bogacz, R., Vogt, S., & Brown, M. W. (2011) · Neural Computation · doi:10.1162/neco_a_00097 · Cited by 44 · see citing works
“The second type of patterns (type 2) is generated by combining multiple features and was previously used by Bogacz and Brown (2003) to assess the ability of models to perform feature extraction.”
BackgroundMethodInfluentialSelf-citationCuriosity: primate neural circuits for novelty and information seeking
Monosov, I. E. (2024) · Nature Reviews Neuroscience · doi:10.1038/s41583-023-00784-9 · Cited by 44 · see citing works
Computational Models of Episodic Memory
Norman, K. A., Detre, G. J., & Polyn, S. M. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511816772.011 · Cited by 45 · see citing works
A Stimulus-Oriented Approach to Memory
Sekuler, R., & Kahana, M. J. (2007) · Current Directions in Psychological Science · doi:10.1111/j.1467-8721.2007.00526.x · Cited by 42 · see citing works
Robustness of Visual Place Cells in Dynamic Indoor and Outdoor Environment
Giovannangeli, C., Gaussier, P., & Banquet, J. P. (2006) · International Journal of Advanced Robotic Systems · doi:10.5772/5748 · Cited by 42 · see citing works
“Moreover, Pr being thought to discriminate familiarity [Bogacz and Brown, 2003], a contextual computation would help to eliminate some interpretations that could not be valid in the current context computed in Pr itself.”
Backgroundla Fuente, I. M. D., Cortés, J. M., Pelta, D. A., & Veguillas, J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0058284 · Cited by 35 · see citing works
“Much less studies exist on the storage capacity of Hopfield network regarding familiarity memory [69,70]; the possibility that the neural network can discriminate previously encoded patterns from patterns which are novel and have not been encoded before.”
BackgroundVisual recognition memory: a view from V1
Cooke, S. F., & Bear, M. F. (2015) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2015.06.008 · Cited by 35 · see citing works
Greve, A., Donaldson, D. I., & van Rossum, M. C. W. (2009) · Hippocampus · doi:10.1002/hipo.20606 · Cited by 33 · see citing works
“In contrast to hippocampal models of recollection, only a few models attempt to simulate familiarity-based retrieval (Sohal and Hasselmo, 2000; Bogacz and Brown, 2003; Norman and O'Reilly, 2003; Meeter et al., 2005; Norman et al., 2005).”
Mechanisms of Synaptic Plasticity and Recognition Memory in the Perirhinal Cortex
Banks, P. J., Warburton, E. C., Brown, M. W., & Bashir, Z. I. (2014) · Progress in molecular biology and translational science · doi:10.1016/b978-0-12-420170-5.00007-6 · Cited by 32 · see citing works
Self-citationComputational models can replicate the capacity of human recognition memory
Androulidakis, Z., Lulham, A., Bogacz, R., & Brown, M. W. (2008) · Network Computation in Neural Systems · doi:10.1080/09548980802412638 · Cited by 28 · see citing works
“…et al. 2001) – an abstract model whose simplicity allows mathematical analysis, (ii) Anti-Hebbian model (Kohonen et al. 1974; Brown and Xiang 1998; Bogacz and Brown 2003) – a model that uses synaptic weakening, and which achieved the best performance on realistic inputs in the comparison study of…”
BackgroundMethodResultInfluentialSelf-citationObject and object‐memory representations across the proximodistal axis of CA1
Vandrey, B., Duncan, S., & Ainge, J. A. (2021) · Hippocampus · doi:10.1002/hipo.23331 · Cited by 28 · see citing works
“…2000; Norman & Eacott, 2005; Wan, Aggleton, & Brown, 1999; Winters, Forwood, Cowell, Saksida, & Bussey, 2004) and contains single neurons that respond to objects (Ahn & Lee, 2015; Bogacz & Brown, 2003; Bogacz, Brown, & Giraud-Carrier, 2001; Burke et al., 2012; Deshmukh, Johnson, & Knierim, 2012).”
Energy Efficient Sparse Connectivity from Imbalanced Synaptic Plasticity Rules
Sacramento, J., Wichert, A., & van Rossum, M. C. W. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004265 · Cited by 29 · see citing works
Serotonergic neuromodulation of synaptic plasticity
Higa, G. S. V., Viana, F. J. C., Francis-Oliveira, J., Cruvinel, E., Franchin, T. S., Marcourakis, T., Ulrich, H., & De Pasquale, R. (2024) · Neuropharmacology · doi:10.1016/j.neuropharm.2024.110036 · Cited by 29 · see citing works
Recognition memory and synaptic plasticity in the perirhinal and prefrontal cortices
Banks, P. J., Bashir, Z. I., & Brown, M. W. (2012) · Hippocampus · doi:10.1002/hipo.22067 · Cited by 29 · see citing works
Self-citationPlasticity Mechanisms of Memory Consolidation and Reconsolidation in the Perirhinal Cortex
Miranda, M., & Bekinschtein, P. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2017.06.002 · Cited by 29 · see citing works
Surprise and recency in novelty detection in the primate brain
Zhang, K., Bromberg-Martin, E. S., Sogukpinar, F., Kocher, K., & Monosov, I. E. (2022) · Current Biology · doi:10.1016/j.cub.2022.03.064 · Cited by 27 · see citing works
“Our data replicate the findings that AVMTC is a key node of novelty processing and provide a first demonstration for the linkage between recency, surprise, and novelty computations suggested by theory.(9,68,69) Notably, we could have found instead that novelty sensitivity was not correlated or negatively correlated to…”
Dopamine and Consolidation of Episodic Memory: Timing Is Everything
Grogan, J. P., Bogacz, R., Tsivos, D., Whone, A. L., & Coulthard, E. J. (2015) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00840 · Cited by 27 · see citing works
“…the observed effects of dopaminergic medication on recognition memory (i.e., higher tendency to classify both previously seen and unseen stimuli as familiar when on medication during encoding; see Figure 4B), we used a simple anti-Hebbian model of familiarity discrimination (Bogacz & Brown, 2003a).”
Self-citationLexico-semantic structure and the word-frequency effect in recognition memoryPreprint
Monaco, J. D., Abbott, L. F., & Kahana, M. J. (2007) · Learning & Memory · doi:10.1101/lm.363207 · Cited by 26 · see citing works
“Reading out the internal network energy as a stimulus-familiarity signal is much more efficient than involving recollective processes (Bogacz et al. 2001a; Bogacz and Brown 2003), which typically involve relaxing the network to reconstruct an attractor state (Murdock 1982; Humphreys et al.”
BackgroundMethodInfluentialGluck, M. A., Myers, C. E., & Meeter, M. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.08.003 · Cited by 25 · see citing works
“In Meeter et al. (2005), we agued that this familiarity effect, the decreased parahippocampal response to familiar stimuli, is what causes the effects of stimulus novelty on the speed of conditioning (other accounts can be found in Sohal & Hasselmo, 2000; Bogacz & Brown, 2002; 2003 ).”
Weightless Neural Networks as Memory Segmented Bloom Filters
Santiago, L., Verona, L. D., Rangel, F., de Faria, F. F., Menasché, D. S., Caarls, W., Breternitz, M., Kundu, S. K., Lima, P. M. V., & França, F. M. G. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.01.115 · Cited by 25 · see citing works
Remembering the Past to See the Future
Rust, N. C., & Palmer, S. E. (2021) · Annual Review of Vision Science · doi:10.1146/annurev-vision-093019-112249 · Cited by 24 · see citing works
Universal Memory Mechanism for Familiarity Recognition and Identification
Yakovlev, V., Amit, D. J., Romani, S., & Hochstein, S. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.4799-07.2008 · Cited by 21 · see citing works
“…to account for the decreased response for repeated stimuli [whether familiar, recent, or non-novel (Brown and Xiang, 1998; Bogacz et al., 2001; Bogacz and Brown, 2003; Norman and O’Reilly, 2003; Meeter et al., 2005)] and some of them also account for the enormous memory capacity observed by…”
Viskontas, I. V. (2008) · Current Opinion in Neurology · doi:10.1097/wco.0b013e3283168e03 · Cited by 20 · see citing works
Computational models of perirhinal cortex function
Cowell, R. A. (2012) · Hippocampus · doi:10.1002/hipo.22064 · Cited by 19 · see citing works
“…stimuli, during which the features possessed by the stimuli are extracted by exploiting statistical regularities in the input—do not have a sufficiently high capacity for storing familiar items to account for the impressive recognition memory of humans (Bogacz et al., 2001; Bogacz and Brown, 2003b).”
BackgroundMethodInfluentialBogacz, R., & Brown, M. W. (2002) · Network Computation in Neural Systems · doi:10.1088/0954-898x_13_4_303 · Cited by 19 · see citing works
Self-citationOptimizing One-Shot Learning with Binary Synapses
Romani, S., Amit, D. J., & Amit, Y. (2008) · Neural Computation · doi:10.1162/neco.2008.10-07-618 · Cited by 18 · see citing works
Simple synaptic modulations implement diverse novelty computations
Aitken, K., Campagnola, L., Garrett, M., Olsen, S. R., & Mihalaş, Ş. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114188 · Cited by 21 · see citing works
PKMζ inactivation induces spatial familiarityPreprint
Moncada, D., & Viola, H. (2008) · Learning & Memory · doi:10.1101/lm.1139508 · Cited by 17 · see citing works
“The efficiency and speed of novelty detection confer an evolutionary advantage that provide a reason for the existence of a familiarity discrimination network in addition to those networks used for recollection (Brown and Aggleton 2001; Bogacz and Brown 2003).”
BackgroundPerirhinal and Postrhinal Functional Inputs to the Hippocampus
Ho, J. W., & Burwell, R. D. (2014) · Springer eBooks · doi:10.1007/978-3-7091-1292-2_3 · Cited by 15 · see citing works
“A mechanism in PER centered on response decrements is hypothesized to have the necessary speed, capacity, and properties to signal information on familiarity and recency of single items (Bogacz and Brown 2003).”
BackgroundAn anti-Hebbian model of familiarity discrimination in the perirhinal cortex
Bogacz, R., & Brown, M. W. (2003) · Neurocomputing · doi:10.1016/s0925-2312(02)00738-5 · Cited by 14 · see citing works
Self-citationHebbian learning revisited and its inference underlying cognitive function
Lim, S. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.02.006 · Cited by 14 · see citing works
Overcoming Catastrophic Interference in Connectionist Networks Using Gram-Schmidt Orthogonalization
Srivastava, V., Sampath, S., & Parker, D. (2014) · PLoS ONE · doi:10.1371/journal.pone.0105619 · Cited by 13 · see citing works
“hippocampal and neocortical networks; [17]); setting limits on the magnitude or rate of learning [18]); the creation of new storage components through neurogenesis [19]; anti-Hebbian plasticity [20]; reducing the overlap between different patterns by sparse coding or by limiting or ‘‘sharpening’’ the number of units u…”
BackgroundKazanovich, Y. B., & Borisyuk, R. M. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.07.022 · Cited by 10 · see citing works
“1) is a feedforward two-layer neural network with the anti-Hebbian learning rule which is adapted to the inputs of real numbers instead of binary values used in models by Bogacz & Brown (2003) and Androulidakis et al., (2008).”
InfluentialDynamics and Robustness of Familiarity Memory
Cortés, J. M., Greve, A., Barrett, A. B., & van Rossum, M. C. W. (2009) · Neural Computation · doi:10.1162/neco.2009.12-08-921 · Cited by 12 · see citing works
“Familiarity discrimination has only been studied much more recently (Bogacz and Brown, 2003).”
BackgroundMethodResultInfluentialNeuroimaging and cognitive changes during déjà vu
Kovács, N., Auer, T., Balás, I., Karádi, K., Zámbó, K., Schwarcz, A., Klivènyi, P., Jokeit, H., Horváth, K., Nagy, F. T., et al. (2008) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2008.08.017 · Cited by 12 · see citing works
“hippocampus, enables recall and conscious recollection of contextual elements, while the other system, including the structures of the parahippocampal gyrus, is important for familiarity judgments [46, 47].”
Optimal learning rules for familiarity detection
Greve, A., Sterratt, D. C., Donaldson, D. I., Willshaw, D. J., & van Rossum, M. C. W. (2008) · Biological Cybernetics · doi:10.1007/s00422-008-0275-4 · Cited by 12 · see citing works
“In Bogacz et al. [2001] a network is introduced that calculates a quantity that is largely equivalent to the energy.”
BackgroundFace familiarity detection with complex synapses
Li, J., Stefanini, F., Benna, M. K., & Fusi, S. (2022) · iScience · doi:10.1016/j.isci.2022.105856 · Cited by 10 · see citing works
Mechanisms underlying sharpening of visual response dynamics with familiarity
Lim, S. (2019) · eLife · doi:10.7554/elife.44098 · Cited by 11 · see citing works
“Depression in the feedforward connections required to lower average response after learning reasserts the role of feedforward synaptic plasticity suggested for familiarity detection (Bogacz and Brown, 2003; Norman and O’Reilly, 2003; Sohal and Hasselmo, 2000).”
BackgroundIterative Retrieval and Block Coding in Autoassociative and Heteroassociative Memory
Knoblauch, A., & Palm, G. (2019) · Neural Computation · doi:10.1162/neco_a_01247 · Cited by 11 · see citing works
“For example, in some modified memory tasks like familiarity detection, it is known that autoassociative networks can be used in a way to achieve up to (ln 2)/2 ≈ 0.347 bit/synapse (Bogacz et al., 2001; Bogacz & Brown, 2003; Palm & Sommer, 1996).”
InfluentialRepresentation Sharpening Can Explain Perceptual Priming
Moldakarimov, S. B., Bazhenov, M., & B, T. J. S. A. (2009) · Neural Computation · doi:10.1162/neco.2009.04-09-999 · Cited by 11 · see citing works
“Representation sharpening has been simulated in feedforward network models (Sohal & Hasselmo, 2000; Norman & O’Reilly, 2003; Bogacz & Brown, 2003).”
MethodHow Dendrites Affect Online Recognition Memory
Wu, X., Mel, G. C., Strouse, D., & Mel, B. W. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006892 · Cited by 12 · see citing works
Du, K.-L., & Swamy, M. N. S. (2019) · Springer eBooks · doi:10.1007/978-1-4471-7452-3_8 · Cited by 9 · see citing works
Visual working memory and delay activity in highly selective neurons in the inferior temporal cortex
Sigala, N. (2009) · Frontiers in Systems Neuroscience · doi:10.3389/neuro.06.011.2009 · Cited by 9 · see citing works
“The idea that memory can be stored in synaptic weights has also been modelled in papers addressing the mechanisms of repetition suppression (Sohal and Hasselmo, 2000; Bogacz and Brown, 2003).”
Kwon, D., Kim, J., Yoo, S. B. M., & Shim, W. M. (2025) · Nature Communications · doi:10.1038/s41467-025-55833-x · Cited by 10 · see citing works
“…analysis methods, such as communication subspace analysis 79 , could elucidate how neural populations in the hippocampus coordinate with those in other medial temporal lobe (MTL) regions known to compute familiarity and novelty signals 80 – 83 to support naturalistic memory encoding and retrieval.”
An analysis of the mean theta phase of population activity in a model of hippocampal region CA1
Zilli, E. A., & Hasselmo, M. E. (2006) · Network Computation in Neural Systems · doi:10.1080/09548980601028908 · Cited by 8 · see citing works
Recognition Memory: What's New in Novelty Signals?
Brown, M. W. (2009) · Current Biology · doi:10.1016/j.cub.2009.06.031 · Cited by 8 · see citing works
“Computational modelling has shown that a powerful and fast system for detecting the novelty or familiarity of individual items requires surprisingly few neurons; indeed, such modelling has also established that the number of neurons in human perirhinal cortex could have the storage capacity to explain human visual rec…”
BackgroundSelf-citationResponses of neurons in the medial temporal lobe during encoding and recognition of face-scene pairs
Viskontas, I. V., Knowlton, B. J., & Fried, I. (2016) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2016.07.014 · Cited by 8 · see citing works
“Author M anuscript Author M anuscript Author M anuscript Author M anuscript code is one of the basic tenets of most contemporary memory models (Norman and O’Reilly, 2003; Bogacz and Brown, 2003; Meterage et al., 2005).”
BackgroundYonelinas, A. P., Ramey, M. M., & Riddell, C. D. (2024) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780190917982.013.32 · Cited by 7 · see citing works
Du, K.-L., & Swamy, M. N. S. (2013) · Springer eBooks · doi:10.1007/978-1-4471-5571-3_7 · Cited by 7 · see citing works
A neural theory for counting memories
Dasgupta, S., Hattori, D., & Navlakha, S. (2022) · Nature Communications · doi:10.1038/s41467-022-33577-2 · Cited by 6 · see citing works
“…(i) it provides counts that are stimulus-speci fi c; (ii) it has a large storage capacity, that is, it requires only a few synapses per unique item 18 ; (iii) it offers robustness, that is, the ability to generalize counts across noisy versions of the sameitem; and (iv) it is fast and automatic,…”
BackgroundMethodInfluentialThe Role of Hierarchical Dynamical Functions in Coding for Episodic Memory and Cognition
Dannenberg, H., Alexander, A. S., Robinson, J. C., & Hasselmo, M. E. (2019) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01439 · Cited by 7 · see citing works
“…evidence that these responses decrease or increase with repeated presentations of objects (Suzuki et al., 1997; Riches et al., 1991), consistent with models of familiarity recognition in which the responses to familiar objects increase or decrease (Bogacz & Brown, 2003; Sohal & Hasselmo, 2000).”
Regarding the temporal requirements of a hierarchical Willshaw network
Sacramento, J., Burnay, F., & Wichert, A. (2011) · Neural Networks · doi:10.1016/j.neunet.2011.07.005 · Cited by 6 · see citing works
On stability and associative recall of memories in attractor neural networks
Sampath, S., & Srivastava, V. (2020) · PLoS ONE · doi:10.1371/journal.pone.0238054 · Cited by 6 · see citing works
“Refer to [30, 31] for a model of recognition in the perirhinal cortex.”
A neural model of mere exposure: The EXAC mechanism 1, 2
Drogosz, M., & Nowak, A. K. (2006) · Polish Psychological Bulletin · Cited by 6 · see citing works
Multiple Cognitive Abilities from a Single Cortical Algorithm
Forwood, S., Cowell, R. A., Bussey, T. J., & Saksida, L. M. (2012) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00250 · Cited by 6 · see citing works
“…mechanism (Kohonen, 1984) combined with sharpening of stimulus representations proportional to length of exposure to give measures of stimulus novelty has been used by computational models of recognition memory in PRC (Cowell et al., 2006; Bogacz & Brown, 2003; Norman & OʼReilly, 2003).”
MethodSavin, C., Dayan, P., & Lengyel, M. (2011) · Cited by 5 · see citing works
Stern, C. E., & Hasselmo, M. E. (2009) · Elsevier eBooks · doi:10.1016/b978-008045046-9.00784-1 · Cited by 5 · see citing works
Machts, T., Grüb, J., & Nieder, A. (2025) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2025.102794 · Cited by 4 · see citing works
“While numerous computational models of recognition memory have been proposed (Sohal and Hasselmo, 2000; Norman and O'Reilly, 2003; Bogacz and Brown, 2003; Cortes et al., 2010; Tyulmankov et al., 2021), most of these models treat familiarity discrimination as a binary problem for unrelated stimuli —…”
Predictive Coding Model Detects Novelty on Different Levels of Representation Hierarchy
Li, T., Tang, M., & Bogacz, R. (2025) · Neural Computation · doi:10.1162/neco_a_01769 · Cited by 7 · see citing works
“One of these models, the anti-Hebbian model (Bogacz & Brown, 2003a), has been shown to replicate the capacity seen in human recognition memory when presented with input patterns with a correlation structure observed in visual stimuli (Androulidakis et al., 2008; Kazanovich & Borisyuk, 2021; Read et…”
InfluentialSelf-citationFamiliarity recognition and recollection: A neural network model
Вениаминовна, Б. Е., Карпенко, М., Kachalova, L. M., & Terekhin, A. T. (2009) · BIOPHYSICS · doi:10.1134/s0006350909030178 · Cited by 4 · see citing works
Sparse RNNs can support high-capacity classification
Turcu, D., & Abbott, L. F. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010759 · Cited by 4 · see citing works
Outram, A. R., Brown, M. W., Warburton, E. C., & Barker, G. R. I. (2022) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2022.970291 · Cited by 4 · see citing works
“, 1995; Xiang and Brown, 1998) and is consistent with computational modeling predictions that efficient storage in a familiarity discrimination network is only achievable if the learning algorithm includes a term producing synaptic weakening (Bogacz and Brown, 2003).”
BackgroundSelf-citationBinary Willshaw learning yields high synaptic capacity for long-term familiarity memoryPreprint
Wichert, A. (2012) · Cited by 4 · see citing works
Predictive coding model can detect novelty on different levels of representation hierarchyPreprint
Li, T. E., Tang, M., & Bogacz, R. (2024) · bioRxiv · doi:10.1101/2024.06.10.597876 · Cited by 4 · see citing works
“However, published models combining ND with representation learning do not have high capacity when the input patterns have a biologically realistic correlation structure (Bogacz and Brown, 2003b).”
BackgroundMethodInfluentialSelf-citation
- Authors
- Bogacz, R., & Brown, M. W.
- Affiliations
- University of Bristol (GB)
- Corresponding author
- Brown, Malcolm W.
- Venue
- Hippocampus
- Publisher
- Wiley
- Published
- 2003-01
- Volume
- 13
- Issue
- 4
- Pages
- 494-524
- ISSN
- 1050-9631 (ISSN-L)
- 1098-1063
- OpenAlex
- W1976921223
- Cited by
- 134
- References
- 81
- Citations by year
- 2026: 3 · 2025: 12 · 2024: 6 · 2023: 4 · 2022: 7 · 2021: 6 · 2020: 4 · 2019: 7 · 2017: 3 · 2016: 1 · 2015: 5 · 2014: 4 · 2013: 2 · 2012: 7
- FWCI
- 3.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Hippocampus (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- perirhinal cortex, recognition memory, computational model, anti-Hebbian learning, Hebbian learning, synaptic plasticity, object identification, stimulus representation, neural network capacity
- MeSH terms
- Animals, Computer Simulation, Discrimination, Psychological, Humans, Memory, Nerve Net, Synaptic Transmission, Neuronal Plasticity, Neurons, Pattern Recognition, Visual, Parahippocampal Gyrus
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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