Context learning in the rodent hippocampus.

We present a Bayesian statistical theory of context learning in the rodent hippocampus. While context is often defined in an experimental setting in relation to specific background cues or task demands, we advance a single, more general notion of context that suffices for a variety of learning phenomena. Specifically, a context is defined as a statistically stationary distribution of experiences, and context learning is defined as the problem of how to form contexts out of groups of experiences that cluster together in time. The challenge of context learning is solving the model selection problem: How many contexts make up the rodent's world? Solving this problem requires balancing two opposing goals: minimize the variability of the distribution of experiences within a context and minimize the likelihood of transitioning between contexts. The theory provides an understanding of why hippocampal place cell remapping sometimes develops gradually over many days of experience and why even consistent landmark differences may need to be relearned after other environmental changes. The theory provides an explanation for progressive performance improvements in serial reversal learning, based on a clear dissociation between the incremental process of context learning and the relatively abrupt context selection process. The impact of partial reinforcement on reversal learning is also addressed. Finally, the theory explains why alternating sequence learning does not consistently result in unique context-dependent sequence representations in hippocampus.

A spin glass model of path integration in rat medial entorhinal cortex.

Electrophysiological recording studies in the dorsocaudal region of medial entorhinal cortex (dMEC) of the rat reveal cells whose spatial firing fields show a remarkably regular hexagonal grid pattern (Fyhn et al., 2004; Hafting et al., 2005). We describe a symmetric, locally connected neural network, or spin glass model, that spontaneously produces a hexagonal grid of activity bumps on a two-dimensional sheet of units. The spatial firing fields of the simulated cells closely resemble those of dMEC cells. A collection of grids with different scales and/or orientations forms a basis set for encoding position. Simulations show that the animal's location can easily be determined from the population activity pattern. Introducing an asymmetry in the model allows the activity bumps to be shifted in any direction, at a rate proportional to velocity, to achieve path integration. Furthermore, information about the structure of the environment can be superimposed on the spatial position signal by modulation of the bump activity levels without significantly interfering with the hexagonal periodicity of firing fields. Our results support the conjecture of Hafting et al. (2005) that an attractor network in dMEC may be the source of path integration information afferent to hippocampus.

Influence of path integration versus environmental orientation on place cell remapping between visually identical environments.

To assess the effects of interactions between angular path integration and visual landmarks on the firing of hippocampal neurons, we recorded from CA1 pyramidal cells as rats foraged in two identical boxes with polarizing internal cues. In the same-orientation condition, following an earlier experiment by Skaggs and McNaughton, the boxes were oriented identically and connected by a corridor. In the opposite-orientation condition, the boxes were abutted by rotating them 90 degrees in opposite directions, so that their orientations differed by 180 degrees . After 16-23 days of pretraining on the same-orientation condition, three rats experienced both conditions in counterbalanced order on each of two consecutive days. On the third day they ran two opposite-orientation trials. Although Skaggs and McNaughton observed stable partial "remapping" of place fields, none of the fields in this experiment remapped in the same-orientation condition. In the opposite-orientation condition, place fields in the first box were isomorphic with those in the same-orientation condition, whereas in the second box the rats eventually exhibited completely different fields. The rats differed as to the trial in which this first occurred. Once the second box exhibited different fields, it continued to do so in all subsequent opposite-orientation trials, yet fields remained the same in subsequent same-orientation trials. The results demonstrate that when animals move actively between environments, and are thus potentially able to maintain their inertial angular orientation, discordance between environmental orientation and the rat's idiothetic direction sense can profoundly affect the hippocampal map-either immediately, or as a result of cumulative experience.

Deforming the hippocampal map.

To investigate conjoint stimulus control over place cells, Fenton et al. (J Gen Physiol 116:191-209, 2000a) recorded while rats foraged in a cylinder with 45 degrees black and white cue cards on the wall. Card centers were 135 degrees apart. In probe trials, the cards were rotated together or apart by 25 degrees . Firing field centers shifted during these trials, stretching and shrinking the cognitive map. Fenton et al. (2000b) described this deformation with an ad hoc vector field equation. We consider what sorts of neural network mechanisms might be capable of accounting for their observations. In an abstract, maximum likelihood formulation, the rat's location is estimated by a conjoint probability density function of landmark positions. In an attractor neural network model, recurrent connections produce a bump of activity over a two-dimensional array of cells; the bump's position is influenced by landmark features such as distances or bearings. If features are chosen with appropriate care, the attractor network and maximum likelihood models yield similar results, in accord with previous demonstrations that recurrent neural networks can efficiently implement maximum likelihood computations (Pouget et al. Neural Comput 10:373-401, 1998; Deneve et al. Nat Neurosci 4:826-831, 2001).