A lower bound on the detectability of nonassociative learning in the local bending reflex of the medicinal leech

Abstract

Studies of neural mechanisms of learning and memory have focused on large changes at identified synapses. However, memory in distributed processing reflexes could involve widely distributed engrams characterized by small changes at every synapse in the network. To investigate this possibility, we used a neural network optimization algorithm to construct distributed engrams for nonassociative conditioning in a model of the local bending reflex of the medicinal leech (Hirudo medicinalis). The model comprised 4 sensory neurons, 10 to 40 interneurons, 8 motor neurons, and up to 480 connections. Synaptic connections in the model were first optimized to reproduce the amplitude and time course of motor neuron synaptic potentials recorded during local bending. This network, which represented the naive state before conditioning, was then reoptimized to the habituated or sensitized state. Following reoptimization, the memory for nonassociative learning was encoded by small changes dispersed across the entire network, and each change made only a small contribution to the learning. Moreover, because the changes were small, resolution of a few tenths of a millivolt, or 3-5% of an average synaptic potential, would be needed to account for half of the nonassociative learning. These results show how difficult distributed engrams can be to detect and provide a likely lower bound on the detectability ofnonassociative learning in this and related networks.

Long-Lasting Reconfiguration of Two Interacting Networks by a Cooperation of Presynaptic and Postsynaptic Plasticity

“…Evidences from a number of studies have indicated that persistent cellular changes induced by past sensory experiences or experimental paradigms of patterned neuronal stimulation can be expressed conjointly at different loci in a network (Frost et al, 1988;Lockery and Sejnowski, 1993;Cleary et al, 1998;Bi and Poo, 1999;Spencer et al, 1999;Staras et al, 1999). However, necessity of a cooperation among such different sites of plasticity has not been investigated.…”

Use of Incremental Adaptation and Habituation Regimens for Mitigating Optokinetic Side Effects

“…This capability for memory is thought to occur at the neurons and at their synaptic connections and, then is distributed throughout the network in a manner consistent with parallel processing (Prescott, 1998;Frost et. al., 1988;Lockery & Sejnowski, 1993). According to Welch's (1978) model of the adaptive process and Reason's (1978) sensory conflict theory, as exposure to the stimulus is repeated (in Reason's model prolonged exposure as well) traces of the stimulus are amassed in a neural storage (i.e.…”

Interactions between Depression and Facilitation within Neural Networks: Updating the Dual-Process Theory of Plasticity

“…So, while memory storage can be considered to occur at discrete loci (i.e., neurons and their synaptic connections), plastic loci occur throughout the network. Memory is distributed, not in the manner held by the aggregate-field hypothesis, but, rather, by one more consistent with parallel distributed processing (Rumelhart and McClelland 1986;Frost et al 1988;Lockery and Sejnowski 1993). Multiple mechanisms can act at a single cellular locus to effect a variable level of change at that locus; at the network level, these variable changes at discrete loci combine in numerous permutations and allow a high degree of behavioral flexibility through learning (e.g., Lockery and Sejnowski 1993;White et al 1993).…”

“…Memory is distributed, not in the manner held by the aggregate-field hypothesis, but, rather, by one more consistent with parallel distributed processing (Rumelhart and McClelland 1986;Frost et al 1988;Lockery and Sejnowski 1993). Multiple mechanisms can act at a single cellular locus to effect a variable level of change at that locus; at the network level, these variable changes at discrete loci combine in numerous permutations and allow a high degree of behavioral flexibility through learning (e.g., Lockery and Sejnowski 1993;White et al 1993). Although much effort is spent investigating the cellular and molecular mechanisms of plasticity, the interactions of these mechanisms both at the cellular level and at the network level cannot be neglected if one's ultimate goal is to explain learning at the behavioral level.…”