Optimal learning rules for familiarity detection

Greve, Andrea; Sterratt, David C.; Donaldson, David I.; Willshaw, David; Rossum, Mark C. W. van

doi:10.1007/s00422-008-0275-4

Cited by 8 publications

(16 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although this is not a very elegant solution, as it requires a multiplication operation and a duplication of the synaptic weights, it does show that the network energy is not a purely theoretical quantity. (For other network implementations that read out the network energy, see, e.g., Bogacz, Brown, & Giraud-Carrier, 2001;Greve et al, 2009. ) The time derivative of the energy can be easily calculated in neural circuits once the energy has been extracted, for instance, using short-term synaptic depression (Puccini, Sanchez-Vives, & Compte, 2007).…”

Section: Network Setupmentioning

confidence: 97%

“…It can be shown that of all local additive learning rules, rule 2.1 is optimal, as it provides the highest capacity in the limit of large N, M (Greve et al, 2009). During the subsequent test phase, the network's performance is evaluated.…”

Section: Network Setupmentioning

confidence: 99%

“…In neuroimaging studies using event-related potentials (ERPs), familiarity is linked to a frontal ERP modulation that occurs around 300 to 500 ms after stimulus presentation, while recollection evokes a parietal ERP modulation 500 to 800 ms after stimulus presentation (Rugg et al, 1998;Rugg & Yonelinas, 2003;Greve, Sterratt, Donaldson, Willshaw, & van Rossum, 2009). Hence, the speed of processing of familiarity discrimination is faster than recollection.…”

Section: Introductionmentioning

confidence: 98%

“…It is only more recently that familiarity discrimination has been studied (Bogacz & Brown, 2003;Metter, Myers, & Gluck, 2005;Yakovlev, Amit, Romani, & Hochstein, 2008;Greve et al, 2009). It has been found that the capacity for familiarity discrimination in associative memory networks is much greater than that for recollection.…”

Section: Introductionmentioning

confidence: 98%

“…It has been found that the capacity for familiarity discrimination in associative memory networks is much greater than that for recollection. Under a wide range of conditions, familiarity capacity is proportional to the number of synapses within the network (Bogacz & Brown, 2003;Greve et al, 2009), whereas the capacity for recollection is merely proportional to the square root of the number of synapses (i.e., the number of neurons in a fully connected network; Amit, 1989). Intuitively this difference in capacity is easily understood.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Dynamics and Robustness of Familiarity Memory

et al. 2010

View full text Add to dashboard Cite

When presented with an item or a face, one might have a sense of recognition without the ability to recall when or where the stimulus has been encountered before. This sense of recognition is called familiarity memory. Following previous computational studies of familiarity memory, we investigate the dynamical properties of familiarity discrimination and contrast two different familiarity discriminators: one based on the energy of the neural network and the other based on the time derivative of the energy. We show how the familiarity signal decays rapidly after stimulus presentation. For both discriminators, we calculate the capacity using mean field analysis. Compared to recall capacity (the classical associative memory in Hopfield nets), both the energy and the slope discriminators have bigger capacity, yet the energy-based discriminator has a higher capacity than one based on its time derivative. Finally, both discriminators are found to have a different noise dependence.

show abstract

Section: Network Setupmentioning

confidence: 97%

Section: Network Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dynamics and Robustness of Familiarity Memory

et al. 2010

View full text Add to dashboard Cite

show abstract

A single‐trace dual‐process model of episodic memory: A novel computational account of familiarity and recollection

Greve¹,

Donaldson²,

Rossum³

2009

Hippocampus

Self Cite

View full text Add to dashboard Cite

ABSTRACT:Dual-process theories of episodic memory state that retrieval is contingent on two independent processes: familiarity (providing a sense of oldness) and recollection (recovering events and their context). A variety of studies have reported distinct neural signatures for familiarity and recollection, supporting dual-process theory. One outstanding question is whether these signatures reflect the activation of distinct memory traces or the operation of different retrieval mechanisms on a single memory trace. We present a computational model that uses a single neuronal network to store memory traces, but two distinct and independent retrieval processes access the memory. The model is capable of performing familiarity and recollection-based discrimination between old and new patterns, demonstrating that dual-process models need not to rely on multiple independent memory traces, but can use a single trace. Importantly, our putative familiarity and recollection processes exhibit distinct characteristics analogous to those found in empirical data; they diverge in capacity and sensitivity to sparse and correlated patterns, exhibit distinct ROC curves, and account for performance on both item and associative recognition tests. The demonstration that a single-trace, dual-process model can account for a range of empirical findings highlights the importance of distinguishing between neuronal processes and the neuronal representations on which they operate. V V C 2009 Wiley-Liss, Inc.

show abstract

Binary Willshaw learning yields high synaptic capacity for long-term familiarity memory

Sacramento

Wichert

2012

Biol Cybern

View full text Add to dashboard Cite

In this study, we investigate from a computational perspective the efficiency of the Willshaw synaptic update rule in the context of familiarity discrimination, a binary-answer, memory-related task that has been linked through psychophysical experiments with modified neural activity patterns in the prefrontal and perirhinal cortex regions. Our motivation for recovering this well-known learning prescription is two-fold: first, the switch-like nature of the induced synaptic bonds, as there is evidence that biological synaptic transitions might occur in a discrete stepwise fashion. Second, the possibility that in the mammalian brain, unused, silent synapses might be pruned in the long-term. Besides the usual pattern and network capacities, we calculate the synaptic capacity of the model, a recently proposed measure where only the functional subset of synapses is taken into account. We find that in terms of network capacity, Willshaw learning is strongly affected by the pattern coding rates, which have to be kept fixed and very low at any time to achieve a non-zero capacity in the large network limit. The information carried per functional synapse, however, diverges and is comparable to that of the pattern association case, even for more realistic moderately low activity levels that are a function of network size.

show abstract

Optimal learning rules for familiarity detection

Cited by 8 publications

References 18 publications

Dynamics and Robustness of Familiarity Memory

Dynamics and Robustness of Familiarity Memory

A single‐trace dual‐process model of episodic memory: A novel computational account of familiarity and recollection

Binary Willshaw learning yields high synaptic capacity for long-term familiarity memory

Contact Info

Product

Resources

About