Energy-based General Sequential Episodic Memory Networks at the Adiabatic Limit

The General Associative Memory Model (GAMM) has a constant state-dependant energy surface that leads the output dynamics to fixed points, retrieving single memories from a collection of memories that can be asynchronously preloaded. We introduce a new class of General Sequential Episodic Memory Models (GSEMM) that, in the adiabatic limit, exhibit temporally changing energy surface, leading to a series of meta-stable states that are sequential episodic memories. The dynamic energy surface is enabled by newly introduced asymmetric synapses with signal propagation delays in the network's hidden layer. We study the theoretical and empirical properties of two memory models from the GSEMM class, differing in their activation functions. LISEM has non-linearities in the feature layer, whereas DSEM has non-linearity in the hidden layer. In principle, DSEM has a storage capacity that grows exponentially with the number of neurons in the network. We introduce a learning rule for the synapses based on the energy minimization principle and show it can learn single memories and their sequential relationships online. This rule is similar to the Hebbian learning algorithm and Spike-Timing Dependent Plasticity (STDP), which describe conditions under which synapses between neurons change strength. Thus, GSEMM combines the static and dynamic properties of episodic memory under a single theoretical framework and bridges neuroscience, machine learning, and artificial intelligence

Associative EPSP-Spike Potentiation Induced by Pairing Orthodromic and Antidromic Stimulation in Rat Hippocampal Slices

ter washing out the AP5, the same stimulation resulted in population spike increases. This suggests that activation of the N-methyl-D-aspartate (NMDA) subtype of glutamate receptor is necessary for the induction of this form of E-S potentiation. 5. Application of 10 µM picrotoxin and/or 10 µM bicuculline, which block inhibition mediated by g-aminobutyric acid-A (GABA A ) receptors, did not 11/22/94 3 reduce antidromically-conditioned E-S potentiation. Thus, plasticity in GABA A -mediated inhibition cannot account for the increased population spike amplitude. 6. E-S potentiation did not increase the amplitude of either extracellular or intracellular EPSP's recorded at the cell body. 11/22/94 4 INTRODUCTION Long-term potentiation (LTP) is a persistent increase in evoked responses following a high-frequency synaptic input (Bliss & Lømo, 1973). LTP manifests both a synaptic component which increases intra

The effect of neural adaptation on population coding accuracy

Most neurons in the primary visual cortex initially respond vigorously when a preferred stimulus is presented, but adapt as stimulation continues. The functional consequences of adaptation are unclear. Typically a reduction of firing rate would reduce single neuron accuracy as less spikes are available for decoding, but it has been suggested that on the population level, adaptation increases coding accuracy. This question requires careful analysis as adaptation not only changes the firing rates of neurons, but also the neural variability and correlations between neurons, which affect coding accuracy as well. We calculate the coding accuracy using a computational model that implements two forms of adaptation: spike frequency adaptation and synaptic adaptation in the form of short-term synaptic plasticity. We find that the net effect of adaptation is subtle and heterogeneous. Depending on adaptation mechanism and test stimulus, adaptation can either increase or decrease coding accuracy. We discuss the neurophysiological and psychophysical implications of the findings and relate it to published experimental data.</p