Place cell discharge is extremely variable during individual passes of the rat through the firing field

Abstract

The idea that the rat hippocampus stores a map of space is based on the existence of ''place cells'' that show ''location-specific'' firing. The discharge of place cells is confined with remarkable precision to a cell-specific part of the environment called the cell's ''firing field.'' We demonstrate here that firing is not nearly as reliable in the time domain as in the positional domain. Discharge during passes through the firing field was compared with a model with Poisson variance of the location-specific firing determined by the time-averaged positional firing rate distribution. Place cells characteristically fire too little or too much compared with expectations from the random model. This fundamental property of place cells is referred to as ''excess firing variance'' and has three main implications: (i) Place cell discharge is not only driven by the summation of many small, asynchronous excitatory synaptic inputs. (ii) Place cell discharge may encode a signal in addition to the current head location. (iii) The excess firing variance helps explain why the errors in computing the rat's position from the simultaneous activity of many place cells are large.How do rodents solve difficult spatial problems? On behavioral grounds, it is believed that rats (and mice) can form map-like representations of their surroundings. Once such a representation is formed, the rat can use it to navigate because the representation contains information about the overall layout or geometry of the surroundings (1-3). Given that a spatial map exists, it is natural to ask how it is organized in the nervous system. That is, where is the map located and how does it operate?Our current understanding of spatial maps is based on the discovery of hippocampal place cells 25 years ago (4). It was observed that individual neurons in the hippocampus (pyramidal cells of CA3 and CA1) (5) show ''location-specific'' firing. A given place cell discharges rapidly only when a rat's head is in a certain part of the environment. Outside this ''firing field'' region, the cell rarely discharges (Fig. 1A). The precise confinement of discharge to firing fields at once suggested that the hippocampus is a key component of the map and that mapping information is in part positional information. In this view, the rat's current location is signaled by the conjoint firing of a set of place cells. As the rat moves, its head leaves the field of some cells and enters the field of others, so that the across-cell firing pattern changes in a characteristic way. It thus is presumed that the rat's head position can be accurately calculated if the current across-cell discharge pattern is known (6, 7).This appealing picture is predicated on a tacit assumptionthat in addition to firing only when the head is in the firing field, that a place cell fires in much the same way each time the head goes through the field along much the same path. It is not uncommon, however, for a robust place cell to be silent as the rat's head passes through the center of its firing fie...

Conjoint Control of Hippocampal Place Cell Firing by Two Visual Stimuli

“…To address this question, we calculated for each cell the similarity between a pair of standard sessions. Since the similarity of firing fields in identical conditions is limited by accuracy of tracking, discrimination of action potentials and nonideal positional firing (Fenton and Muller 1998) using pairs of standard sessions establishes a reasonable upper bound for similarity. Thus, if the vector-field transform does only a fair job of accounting for the distortion induced by reconfiguration, the previously computed value of 1.30 should be lower than the similarity for pairs of standard sessions.…”

“…There are really two issues concerning the nature of the representation. First, it may be true that hippocampal pyramidal cells can encode nonspatial aspects of the environment under more complex circumstances, but, in the pellet-chasing task, the hippocampal pyramidal cells act in many ways as nearly ideal place cells, at least in the spatial domain (Muller 1996; Eichenbaum et al 1999; and see Fenton and Muller 1998, for a discussion of imperfections of place-cell firing in the temporal domain). Thus, in the pellet-chasing task, the firing rate of each place cell decreases with distance from the field centroid and does not vary with head orientation (Muller et al 1994).…”

A Novel Nonparametric Approach for Neural Encoding and Decoding Models of Multimodal Receptive Fields

“…It can be seen that at both macro-and microlevels, the nonparametric BLML model is able to reproduce the recorded activity; the spatial dependence is nicely captured as shown in Figure 6B, along with microlevel bursting, as shown in Figure 6D. Further, it can be seen in the zoom-in view in Figure 6C that there is high variability (Fenton & Muller, 1998;Moser et al, 2008) between the spike counts on different trajectories that pass through the region, even though the λ xy,c is almost constant over this small region. This effect is replicated in simulated activity due to incorporation of spike history.…”

“…However, it is well known that these neurons fire in a non-Poisson fashion and their spiking also depends on their own spiking history. For instance, bursting patterns have been found to be a significant component of hippocampal neuronal spiking activity (Barbieri, Quirk, Frank, Wilson, & Brown, 2001;Fenton & Muller, 1998). Thus, encoding and decoding models that capture spike history dependencies will likely yield better performance (Truccolo, Eden, Fellows, Donoghue, & Brown, 2005;Pillow et al, 2008).…”

Spike Count Reliability and the Poisson Hypothesis

“…Justifying such an assumption will often be difficult. In particular, conclusions of excess variability (Brown et al, 1998;Fenton and Muller, 1998) can perhaps always be attributed to the effects of additional variables that have not been modeled. In this light, a rejection of the minimal Poisson null hypothesis is unusual because it does not only demonstrate that a single model is not a good fit to the data, but that an entire class of models is inappropriate.…”