Specialization and hierarchy are organizing principles for primate cortex, yet there is little direct evidence for how cortical areas are specialized in the temporal domain. We measured timescales of intrinsic fluctuations in spiking activity across areas, and found a hierarchical ordering, with sensory and prefrontal areas exhibiting shorter and longer timescales, respectively. Based on our findings, we suggest that intrinsic timescales reflect areal specialization for task-relevant computations over multiple temporal ranges.

Hierarchy provides a unifying principle for the macroscale organization of anatomical and functional properties across primate cortex, yet microscale bases of specialization across human cortex are poorly understood. Anatomical hierarchy is conventionally informed by invasive tract-tracing measurements, creating a need for a principled proxy measure in humans. Moreover, cortex exhibits marked interareal variation in gene expression, yet organizing principles of cortical transcription remain unclear. We hypothesized that specialization of cortical microcircuitry involves hierarchical gradients of gene expression. We found that a noninvasive neuroimaging measure-MRI-derived T1-weighted/T2-weighted (T1w/T2w) mapping-reliably indexes anatomical hierarchy, and it captures the dominant pattern of transcriptional variation across human cortex. We found hierarchical gradients in expression profiles of genes related to microcircuit function, consistent with monkey microanatomy, and implicated in neuropsychiatric disorders. Our findings identify a hierarchical axis linking cortical transcription and anatomy, along which gradients of microscale properties may contribute to the macroscale specialization of cortical function.

Working memory (WM) is a cognitive function for temporary maintenance and manipulation of information, which requires conversion of stimulus-driven signals into internal representations that are maintained across seconds-long mnemonic delays. Within primate prefrontal cortex (PFC), a critical node of the brain's WM network, neurons show stimulus-selective persistent activity during WM, but many of them exhibit strong temporal dynamics and heterogeneity, raising the questions of whether, and how, neuronal populations in PFC maintain stable mnemonic representations of stimuli during WM. Here we show that despite complex and heterogeneous temporal dynamics in single-neuron activity, PFC activity is endowed with a population-level coding of the mnemonic stimulus that is stable and robust throughout WM maintenance. We applied population-level analyses to hundreds of recorded single neurons from lateral PFC of monkeys performing two seminal tasks that demand parametric WM: oculomotor delayed response and vibrotactile delayed discrimination. We found that the high-dimensional state space of PFC population activity contains a low-dimensional subspace in which stimulus representations are stable across time during the cue and delay epochs, enabling robust and generalizable decoding compared with time-optimized subspaces. To explore potential mechanisms, we applied these same population-level analyses to theoretical neural circuit models of WM activity. Three previously proposed models failed to capture the key population-level features observed empirically. We propose network connectivity properties, implemented in a linear network model, which can underlie these features. This work uncovers stable population-level WM representations in PFC, despite strong temporal neural dynamics, thereby providing insights into neural circuit mechanisms supporting WM.working memory | prefrontal cortex | population coding T he neuronal basis of working memory (WM) in prefrontal cortex (PFC) has been studied for decades through singleneuron recordings from monkeys performing tasks in which a transient sensory stimulus must be held in WM across a secondslong delay to guide a future response. These studies discovered that a key neural correlate of WM in PFC is stimulus-selective persistent activity, i.e., stable elevated firing rates in a subset of neurons, that spans the delay (1). These neurophysiological findings have grounded a leading hypothesis that WM is supported by stable persistent activity patterns in PFC that bridge the gap between stimulus and response epochs. Because the timescales of WM maintenance (several seconds) are longer than typical timescales of neuronal and synaptic integration (∼10-100 ms), mechanisms at the level of neural circuits may be critical for generating WM activity in PFC (2). A leading theoretical framework proposes that PFC circuits subserve WM maintenance through dynamical attractors, i.e., stable fixed points in network activity, generated by strong recurrent connectivity (3, 4).Recent neurophysiologi...

According to reinforcement learning theory of decision making, reward expectation is computed by integrating past rewards with a fixed timescale. By contrast, we found that a wide range of time constants is available across cortical neurons recorded from monkeys performing a competitive game task. By recognizing that reward modulates neural activity multiplicatively, we found that one or two time constants of reward memory can be extracted for each neuron in prefrontal, cingulate, and parietal cortex. These timescales ranged from hundreds of milliseconds to tens of seconds, according to a power-law distribution, which is consistent across areas and reproduced by a “reservoir” neural network model. These neuronal memory timescales were weakly but significantly correlated with those of monkey's decisions. Our findings suggest a flexible memory system, where neural subpopulations with distinct sets of long or short memory timescales may be selectively deployed according to the task demands.

A specific type of the neural networks, the Restricted Boltzmann Machines (RBM), are implemented for classification and feature detection. They are characterized by separate layers of visible and hidden units, which are able to learn efficiently a generative model of the observed data. We study a "hybrid" version of RBM's, in which hidden units are analog and visible units are binary, and we show that the evolution and the thermodynamics of visible units are equivalent to those of a Hopfield network, in which the N visible units are the neurons and the P hidden units are the learned patterns. We apply the method of stochastic stability to derive the thermodynamics of the machine, by considering a formal extension of this technique to the case of multiple sets of stored patterns, which may act as a benchmark for the study of correlated sets. Our results imply that simulating the dynamics of a Hopfield network, requiring the update of N neurons and the storage of N (N − 1)/2 synapses, can be accomplished by a hybrid Boltzmann Machine, requiring the update of N + P neurons but only the storage of N P synapses. In addition, the well known glass transition of the Hopfield network has a counterpart in the Boltzmann Machine: It corresponds to an optimum criterion for selecting the relative sizes of the hidden and visible layers, resolving the trade-off between flexibility and generality of the model. The low storage phase of the Hopfield model corresponds to few hidden units and hence a very constrained RBM, while the spin-glass phase (too many hidden units) corresponds to an overly unconstrained RBM prone to overfitting of the observed data.

Neurons show diverse timescales, so that different parts of a network respond with disparate temporal dynamics. Such diversity is observed both when comparing timescales across brain areas and among cells within local populations; the underlying circuit mechanism remains unknown. We examine conditions under which spatially local connectivity can produce such diverse temporal behavior.In a linear network, timescales are segregated if the eigenvectors of the connectivity matrix are localized to different parts of the network. We develop a framework to predict the shapes of localized eigenvectors. Notably, local connectivity alone is insufficient for separate timescales. However, localization of timescales can be realized by heterogeneity in the connectivity profile, and we demonstrate two classes of network architecture that allow such localization. Our results suggest a framework to relate structural heterogeneity to functional diversity and, beyond neural dynamics, are generally applicable to the relationship between structure and dynamics in biological networks.DOI: http://dx.doi.org/10.7554/eLife.01239.001

In a psychophysics experiment, monkeys were shown a sequence of two to eight images, randomly chosen out of a set of 16, each image followed by a delay interval, the last image in the sequence being a repetition of any (one) of the images shown in the sequence. The monkeys learned to recognize the repetition of an image. The performance level was studied as a function of the number of images separating cue (image that will be repeated) from match for different sequence lengths, as well as at fixed cue-match separation versus length of sequence. These experimental results are interpreted as features of multi-item working memory in the framework of a recurrent neural network. It is shown that a model network can sustain multi-item working memory. Fluctuations due to the finite size of the network, together with a single extra ingredient, related to expectation of reward, account for the dependence of the performance on the cue-position, as well as for the dependence of performance on sequence length for fixed cue-match separation.

The estimation of a density profile from experimental data points is a challenging problem, usually tackled by plotting a histogram. Prior assumptions on the nature of the density, from its smoothness to the specification of its form, allow the design of more accurate estimation procedures, such as Maximum Likelihood. Our aim is to construct a procedure that makes no explicit assumptions, but still providing an accurate estimate of the density. We introduce the self-consistent estimate: the power spectrum of a candidate density is given, and an estimation procedure is constructed on the assumption, to be released a posteriori, that the candidate is correct. The self-consistent estimate is defined as a prior candidate density that precisely reproduces itself. Our main result is to derive the exact expression of the self-consistent estimate for any given dataset, and to study its properties. Applications of the method require neither priors on the form of the density nor the subjective choice of parameters. A cutoff frequency, akin to a bin size or a kernel bandwidth, emerges naturally from the derivation. We apply the self-consistent estimate to artificial data generated from various distributions and show that it reaches the theoretical limit for the scaling of the square error with the dataset size.