On Associative Memories

1987

Citations in the corpus

• Structural Synaptic Plasticity Has High Memory Capacity and Can Explain Graded Amnesia, Catastrophic Forgetting, and the Spacing Effect (2014, neutral): “For example, C~0:69 bit per synapse (bps) for networks of binary synapses [26,44], or C~0:72 bps for real-valued synaptic weights [45,46].”
• Optimal synaptic learning in non-linear associative memory (2010, unknown): “…learning models of the Hopfield type [30], [58], [32], [33], [53], [34], [35], [36], [37], simple non-linear learning models of the Willshaw type [24], [23], [25], [59], [26], [28], [18], [29], and BCPNN-type models employing suboptimal Bayesian heuristics [38], [39], [40], [41], [42], [43], [6].”
• Zip nets: Efficient associative computation with binary synapses (2010, unknown): “Previous work on non-linear learning models is scarce and mostly focused on a very simple model employing binary synapses, the so-called Steinbuch or Willshaw model with clipped Hebbian learning [19], [20], [21], [22], [23], [24], [25], [26], [27], [28] (but see also [29], [30], [31], [32]).”
• Neural Associative Memory with Optimal Bayesian Learning (2011, unknown): “Exact potential distributions are well known and can be used to compute optimal firing thresholds (Palm, 1980; Buckingham & Willshaw, 1992, 1993; Knoblauch, 2008; Knoblauch et al., 2010).”
• Does Spike-Timing-Dependent Synaptic Plasticity Couple or Decouple Neurons Firing in Synchrony? (2012, unknown): “Consistent with many attractor neural network models of memory (Marr, 1971; Palm, 1980; Hopfield, 1982; Lansner, 2009; Knoblauch, 2011), these ideas imply the prediction that synchronized neurons should organize into bidirectionally connected cell ensembles, where the presence of a strong synapse fr”
• Memory Capacities for Synaptic and Structural Plasticity (2010, unknown): “…much more efficient by any standards because nonlinearities in the neural transfer function and in the superposition of memory traces keep the crosstalk small, even if the number of stored patterns scales almost with the square of the dimension of the patterns (Willshaw et al., 1969; Palm, 1980).”
• Efficient Associative Computation with Discrete Synapses (2016, unknown): “…model employing binary synapses, the so-called Steinbuch or Willshaw model with clipped Hebbian learning (Steinbuch, 1961; Willshaw et al., 1969; Palm, 1980; Golomb, Rubin, & Sompolinsky, 1990; Palm, 1991; Nadal, 1991; Graham & Willshaw, 1997; Sommer & Palm, 1999; Knoblauch, 2005; Knoblauch,…”