Initial state randomness improves sequence learning in a model hippocampal network

Abstract

Randomness can be a useful component of computation. Using a computationally minimal, but still biologically based model of the hippocampus, we evaluate the effects of initial state randomization on learning a cognitive problem that requires this brain structure. Greater randomness of initial states leads to more robust performance in simulations of the cognitive task called transverse patterning, a context-dependent discrimination task that we code as a sequence prediction problem. At the conclusion of training, greater initial randomness during training trials also correlates with increased, repetitive firing of select individual neurons, previously named local context neurons. In essence, such repetitively firing neurons recognize subsequences, and previously their presence has been correlated with solving the transverse patterning problem. A more detailed analysis of the simulations across training trials reveals more about initial state randomization. The beneficial effects of initial state randomization derive from enhanced variation, across training trials, of the sequential states of a network. This greater variation is not uniformly present during training; it is largely restricted to the beginning of training and when novel sequences are introduced. Little such variation occurs after extensive or even moderate amounts of training. We explain why variation is high early in training, but not later. This automatic modulation of the initial-state-driven random variation through state space is reminiscent of simulated annealing where modulated randomization encourages a selectively broad search through state space. In contrast to an annealing schedule, the selective occurrence of such a random search here is an emergent property, and the critical randomization occurs during training rather than testing.

The formation of neural codes in the hippocampus: trace conditioning as a prototypical paradigm for studying the random recoding hypothesis

“…The weight of a synapse from pyramidal cell i to the feedback interneuron are updated as Randomization of the initial state, Z(0), (see Shon et al 2002) is part of model fα. On each trial, both training and testing, a · n neurons are activated by a uniform random process.…”

Place from time: Reconstructing position from a distributed representation of temporal context

“…In that model, free recall proceeded by using hippocampal context as a probe for cortical items; in item recognition cortical items were used as a probe to try and recover hippocampal context (see also Dennis & Humphreys, 2001;Schwartz, Howard, Jing, & Kahana, in press). Similarly, a model of sequence learning that has been applied to a number of learning tasks (Levy, 1996;Shon, Wu, Sullivan, & Levy, 2002;Wu & Levy, 1998 argues that one function of the hippocampus is to support local context states that depend on the item presented and the temporal context it is presented in. These local context neurons bridge across temporally proximate item presentations, providing a possible explanation of the temporally-defined associations described by TCM (Kahana, 1996;Howard & Kahana, 1999).…”

Activity affects trace conditioning performance in a minimal hippocampal model

“…If a neuron fires on only one timestep, then that neuron's context length is one. Previous studies [3,6] have demonstrated a correlation between the mean context length of all of the neurons in the network and learning performance.…”