Abstract
The postsynaptic potentials of pyramidal neurons have a non-Gaussian amplitude distribution with a heavy tail in both hippocampus and neocortex. Such distributions of synaptic weights were recently shown to generate spontaneous internal noise optimal for spike propagation in recurrent cortical circuits. However, whether this internal noise generation by heavy-tailed weight distributions is possible for and beneficial to other computational functions remains unknown. To clarify this point, we construct an associative memory (AM) network model of spiking neurons that stores multiple memory patterns in a connection matrix with a lognormal weight distribution. In AM networks, non-retrieved memory patterns generate a cross-talk noise that severely disturbs memory recall. We demonstrate that neurons encoding a retrieved memory pattern and those encoding non-retrieved memory patterns have different subthreshold membrane-potential distributions in our model. Consequently, the probability of responding to inputs at strong synapses increases for the encoding neurons, whereas it decreases for the non-encoding neurons. Our results imply that heavy-tailed distributions of connection weights can generate noise useful for AM recall.
Highlights
The organization of neuronal wiring determines the flow of information in neural circuits and has significant implications for functions of the circuits
These features include the complex topology of neuronal wiring, and the nonGaussian nature of the distributions of amplitudes of excitatory postsynaptic potentials (EPSPs), which is typically represented by the presence of long tails in the distributions
We found that a heavy-tailed weight distribution of Hebbian synapses creates memory-specific subthreshold membrane potential fluctuations to regulate the stochastic dynamics of memory retrieval
Summary
The organization of neuronal wiring determines the flow of information in neural circuits and has significant implications for functions of the circuits. A number of recent studies revealed the non-random features of neuronal wiring in cortical circuits (Markram, 1997; Kalisman et al, 2005; Song et al, 2005; Yoshimura et al, 2005; Koulakov et al, 2009; Lefort et al, 2009; Yassin et al, 2010; Perin et al, 2011) These features include the complex topology of neuronal wiring, and the nonGaussian nature of the distributions of amplitudes of excitatory postsynaptic potentials (EPSPs), which is typically represented by the presence of long tails ( called “heavy tails”) in the distributions. Hereafter we call the EPSP amplitude the “synaptic weight” from one neuron to the other
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