Alessandro Treves

Hippocampal remapping and grid realignment in entorhinal cortex

A fundamental property of many associative memory networks is the ability to decorrelate overlapping input patterns before information is stored. In the hippocampus, this neuronal pattern separation is expressed as the tendency of ensembles of place cells to undergo extensive ‘remapping’ in response to changes in the sensory or motivational inputs to the hippocampus. Remapping is expressed under some conditions as a change of firing rates in the presence of a stable place code (‘rate remapping’), and under other conditions as a complete reorganization of the hippocampal place code in which both place and rate of firing take statistically independent values (‘global remapping’). Here we show that the nature of hippocampal remapping can be predicted by ensemble dynamics in place-selective grid cells in the medial entorhinal cortex, one synapse upstream of the hippocampus. Whereas rate remapping is associated with stable grid fields, global remapping is always accompanied by a coordinate shift in the firing vertices of the grid cells. Grid fields of co-localized medial entorhinal cortex cells move and rotate in concert during this realignment. In contrast to the multiple environment-specific representations coded by place cells in the hippocampus, local ensembles of grid cells thus maintain a constant spatial phase structure, allowing position to be represented and updated by the same translation mechanism in all environments encountered by the animal.

The upward bias in measures of information derived from limited data samples

Extracting information measures from limited experimental samples, such as those normally available when using data recorded in vivo from mammalian cortical neurons, is known to be plagued by a systematic error, which tends to bias the estimate upward. We calculate here the average of the bias, under certain conditions, as an asymptotic expansion in the inverse of the size of the data sample. The result agrees with numerical simulations, and is applicable, as an additive correction term, to measurements obtained under such conditions. Moreover, we discuss the implications for measurements obtained through other usual procedures.

Correlations and the encoding of information in the nervous system

Is the information transmitted by an ensemble of neurons determined solely by the number of spikes fired by each cell, or do correlations in the emission of action potentials also play a significant role? We derive a simple formula which enables this question to be answered rigorously for short time–scales. The formula quantifies the corrections to the instantaneous information rate which result from correlations in spike emission between pairs of neurons. The mutual information that the ensemble of neurons conveys about external stimuli can thus be broken down into firing rate and correlation components. This analysis provides fundamental constraints upon the nature of information coding, showing that over short time–scales, correlations cannot dominate information representation, that stimulus–independent correlations may lead to synergy (where the neurons together convey more information than they would if they were considered independently), but that only certain combinations of the different sources of correlation result in significant synergy rather than in redundancy or in negligible effects. This analysis leads to a new quantification procedure which is directly applicable to simultaneous multiple neuron recordings.

Progressive Transformation of Hippocampal Neuronal Representations in “Morphed” Environments

Hippocampal neural codes for different, familiar environments are thought to reflect distinct attractor states, possibly implemented in the recurrent CA3 network. A defining property of an attractor network is its ability to undergo sharp and coherent transitions between pre-established (learned) representations when the inputs to the network are changed. To determine whether hippocampal neuronal ensembles exhibit such discontinuities, we recorded in CA3 and CA1 when a familiar square recording enclosure was morphed in quantifiable steps into a familiar circular enclosure while leaving other inputs constant. We observed a gradual noncoherent progression from the initial to the final network state. In CA3, the transformation was accompanied by significant hysteresis, resulting in more similar end states than when only square and circle were presented. These observations suggest that hippocampal cell assemblies are capable of incremental plastic deformation, with incongruous information being incorporated into pre-existing representations.

What determines the capacity of autoassociative memories in the brain

Threshold-linear (graded response) units approximate the real firing behaviour of pyramidal neurons in a simplified form, suited to the analytical study of large autoassociative networks. Here we extend previous results on threshold-linear networks to a much larger class of models, by considering different connectivities (including full feedback, highly diluted and multilayer feedforward architectures), different forms of Hebbian learning rules, and different distributions of firing rates (including realistic, continuous distributions of rates). This allows an evaluation of the main factors which may affect, in real cortical networks, the capacity for storage and retrieval of discrete firing patterns.In each case a single equation is derived, which determines both αc, the maximum number of retrievable patterns per synapse, and Im, the maximum amount of retrievable information per synapse. It is shown that: 1. Non-speeific effects, such as those usually ascribed to inhibition, or to neuramodulatary afferen...

Mean-field analysis of neuronal spike dynamics

I consider a mean-field description of the dynamics of interacting intergrate-and-fire neuron-like units. The basic dynamical variables are the membrane potential of each (point-like) ‘cell’ and th...

The representational capacity of the distributed encoding of information provided by populations of neurons in primate temporal visual cortex

Abstract It has been shown that it is possible to read, from the firing rates of just a small population of neurons, the code that is used in the macaque temporal lobe visual cortex to distinguish between different faces being looked at. To analyse the information provided by populations of single neurons in the primate temporal cortical visual areas, the responses of a population of 14 neurons to 20 visual stimuli were analysed in a macaque performing a visual fixation task. The population of neurons analysed responded primarily to faces, and the stimuli utilised were all human and monkey faces. Each neuron had its own response profile to the different members of the stimulus set. The mean response of each neuron to each stimulus in the set was calculated from a fraction of the ten trials of data available for every stimulus. From the remaining data, it was possible to calculate, for any population response vector, the relative likelihoods that it had been elicited by each of the stimuli in the set. By comparison with the stimuli actually shown, the mean percentage correct identification was computed and also the mean information about the stimuli, in bits, that the population of neurons carried on a single trial. When the decoding algorithm used for this calculation approximated an optimal, Bayesian estimate of the relative likelihoods, the percentage correct increased from 14% correct (chance was 5% correct) with one neuron to 67% with 14 neurons. The information conveyed by the population of neurons increased approximately linearly from 0.33 bits with one neuron to 2.77 bits with 14 neurons. This leads to the important conclusion that the number of stimuli that can be encoded by a population of neurons in this part of the visual system increases approximately exponentially as the number of cells in the sample increases (in that the log of the number of stimuli increases almost linearly). This is in contrast to a local encoding scheme (of ”grandmother” cells), in which the number of stimuli encoded increases linearly with the number of cells in the sample. Thus one of the potentially important properties of distributed representations, an exponential increase in the number of stimuli that can be represented, has been demonstrated in the brain with this population of neurons. When the algorithm used for estimating stimulus likelihood was as simple as could be easily implemented by neurons receiving the population’s output (based on just the dot product between the population response vector and each mean response vector), it was still found that the 14-neuron population produced 66% correct guesses and conveyed 2.30 bits of information, or 83% of the information that could be extracted with the nearly optimal procedure. It was also shown that, although there was some redundancy in the representation (with each neuron contributing to the information carried by the whole population 60% of the information it carried alone, rather than 100%), this is due to the fact that the number of stimuli in the set was limited (it was 20). The data are consistent with minimal redundancy for sufficiently large and diverse sets of stimuli. The implication for brain connectivity of the distributed encoding scheme, which was demonstrated here in the case of faces, is that a neuron can receive a great deal of information about what is encoded by a large population of neurons if it is able to receive its inputs from a random subset of these neurons, even of limited numbers (e.g. hundreds).

What is the mammalian dentate gyrus good for

In the mammalian hippocampus, the dentate gyrus (DG) is characterized by sparse and powerful unidirectional projections to CA3 pyramidal cells, the so-called mossy fibers (MF). The MF form a distinct type of synapses, rich in zinc, that appear to duplicate, in terms of the information they convey, what CA3 cells already receive from entorhinal cortex layer II cells, which project both to the DG and to CA3. Computational models have hypothesized that the function of the MF is to enforce a new, well-separated pattern of activity onto CA3 cells, to represent a new memory, prevailing over the interference produced by the traces of older memories already stored on CA3 recurrent collateral connections. Although behavioral observations support the notion that the MF are crucial for decorrelating new memory representations from previous ones, a number of findings require that this view be reassessed and articulated more precisely in the spatial and temporal domains. First, neurophysiological recordings indicate that the very sparse dentate activity is concentrated on cells that display multiple but disorderly place fields, unlike both the single fields typical of CA3 and the multiple regular grid-aligned fields of medial entorhinal cortex. Second, neurogenesis is found to occur in the adult DG, leading to new cells that are functionally added to the existing circuitry, and may account for much of its ongoing activity. Third, a comparative analysis suggests that only mammals have evolved a DG, despite some of its features being present also in reptiles, whereas the avian hippocampus seems to have taken a different evolutionary path. Thus, we need to understand both how the mammalian dentate operates, in space and time, and whether evolution, in other vertebrate lineages, has offered alternative solutions to the same computational problems.

The relative advantages of sparse versus distributed encoding for associative neuronal networks in the brain

In some neuronal networks in the brain which are thought to operate as associative memories, a sparse coding of information can enhance the storage capacity. The extent to which this statement is valid in general is discussed here, by considering some simple formal models of associative memory which include different neurobiological constraints. In nets of linear neurons, trained with either a Hebbian (purely incremental) or a Stanton and Sejnowski learning rule, sparse coding increases the number of independent associations that can be stored. When neurons are nonlinear, for a diversity of learning rules, sparse coding may result in an increase in the number of patterns that can be discriminated. The analysis is then used to help interpret recent evidence on the encoding of information in the taste and visual systems, as obtained from recordings in primates. Following the taste pathway, it is found that the breadth of tuning of individual neurons becomes progressively finer, consistent with the idea that...

Theta-paced flickering between place-cell maps in the hippocampus.

The ability to recall discrete memories is thought to depend on the formation of attractor states in recurrent neural networks. In such networks, representations can be reactivated reliably from subsets of the cues that were present when the memory was encoded, at the same time as interference from competing representations is minimized. Theoretical studies have pointed to the recurrent CA3 system of the hippocampus as a possible attractor network. Consistent with predictions from these studies, experiments have shown that place representations in CA3 and downstream CA1 tolerate small changes in the configuration of the environment but switch to uncorrelated representations when dissimilarities become larger. However, the kinetics supporting such network transitions, at the subsecond timescale, is poorly understood. Here we show in rats that instantaneous transformation of the spatial context does not change the hippocampal representation all at once but is followed by temporary bistability in the discharge activity of CA3 ensembles. Rather than sliding through a continuum of intermediate activity states, the CA3 network undergoes a short period of competitive flickering between preformed representations of the past and present environment before settling on the latter. Network flickers are extremely fast, often with complete replacement of the active ensemble from one theta cycle to the next. Within individual cycles, segregation is stronger towards the end, when firing starts to decline, pointing to the theta cycle as a temporal unit for expression of attractor states in the hippocampus. Repetition of pattern-completion processes across successive theta cycles may facilitate error correction and enhance discriminative power in the presence of weak and ambiguous input cues.