Adam Sachs

Attentional Filtering of Visual Information by Neuronal Ensembles in the Primate Lateral Prefrontal Cortex

The activity of neurons in the primate lateral prefrontal cortex (LPFC) is strongly modulated by visual attention. Such a modulation has mostly been documented by averaging the activity of independently recorded neurons over repeated experimental trials. However, in realistic settings, ensembles of simultaneously active LPFC neurons must generate attentional signals on a single-trial basis, despite the individual and correlated variability of neuronal responses. Whether, under these circumstances, the LPFC can reliably generate attentional signals is unclear. Here, we show that the simultaneous activity of neuronal ensembles in the primate LPFC can be reliably decoded to predict the allocation of attention on a single-trial basis. Decoding was sensitive to the noise correlation structure of the ensembles. Additionally, it was resilient to distractors, predictive of behavior, and stable over weeks. Thus, LPFC neuronal ensemble activity can reliably encode attention within behavioral time frames, despite the noisy and correlated nature of neuronal activity.

Correlated variability modifies working memory fidelity in primate prefrontal neuronal ensembles

Significance The working memory (WM)-related activity in the primate prefrontal cortex (PFC) is hypothesized to arise from the structure of the network in which the neurons are embedded. Recent studies have also shown that it is difficult to predict the properties of neuronal ensembles from the properties of individually examined neurons. By recording the activity of neuronal ensembles in the macaque PFC, we found evidence supporting the network origins of WM activity and discovered features of WM coding in neuronal ensembles that were inaccessible in prior single neuron studies. Most notably, we found that correlated firing rate variability between neurons (i.e., noise correlations) can improve WM coding and that neurons not selective for WM can improve WM coding when part of an ensemble. Neurons in the primate lateral prefrontal cortex (LPFC) encode working memory (WM) representations via sustained firing, a phenomenon hypothesized to arise from recurrent dynamics within ensembles of interconnected neurons. Here, we tested this hypothesis by using microelectrode arrays to examine spike count correlations (rsc) in LPFC neuronal ensembles during a spatial WM task. We found a pattern of pairwise rsc during WM maintenance indicative of stronger coupling between similarly tuned neurons and increased inhibition between dissimilarly tuned neurons. We then used a linear decoder to quantify the effects of the high-dimensional rsc structure on information coding in the neuronal ensembles. We found that the rsc structure could facilitate or impair coding, depending on the size of the ensemble and tuning properties of its constituent neurons. A simple optimization procedure demonstrated that near-maximum decoding performance could be achieved using a relatively small number of neurons. These WM-optimized subensembles were more signal correlation (rsignal)-diverse and anatomically dispersed than predicted by the statistics of the full recorded population of neurons, and they often contained neurons that were poorly WM-selective, yet enhanced coding fidelity by shaping the ensemble’s rsc structure. We observed a pattern of rsc between LPFC neurons indicative of recurrent dynamics as a mechanism for WM-related activity and that the rsc structure can increase the fidelity of WM representations. Thus, WM coding in LPFC neuronal ensembles arises from a complex synergy between single neuron coding properties and multidimensional, ensemble-level phenomena.

Single-Trial Decoding of Visual Attention from Local Field Potentials in the Primate Lateral Prefrontal Cortex Is Frequency-Dependent.

Local field potentials (LFPs) are fluctuations of extracellular voltage that may reflect the physiological phenomena occurring within a volume of neural tissue. It is known that the allocation of spatial attention modulates the amplitude of LFPs in visual areas of primates. An issue that remains poorly investigated is whether and how attention modulates LFPs in executive brain areas, such as the lateral prefrontal cortex (LPFC), thought to be involved in the origins of attention. We addressed this issue by recording LFPs from multielectrode arrays implanted in the LPFC of two macaques. We found that the allocation of attention can be reliably decoded on a single-trial basis from ensembles of LFPs with frequencies >60 Hz. Using LFP frequencies <60 Hz, we could not decode the allocation of attention, but we could decode the location of a visual stimulus as well as the endpoint of saccades toward that stimulus. The information contained in the high-frequency LFPs was fully redundant with the information contained in the spiking activity of single neurons recorded from the same electrodes. Moreover, the decoding of attention using γ frequency LFPs was less accurate than using spikes, but it was twice more stable across time. Finally, decorrelating the LFP signals from the different electrodes increased decoding performance in the high frequencies by up to ∼14%. Our findings suggest that LFPs recorded from chronically implanted multielectrode arrays in the LPFC contain information about sensory, cognitive, and motor components of a task in a frequency-dependent manner.

A Quadrantic Bias in Prefrontal Representation of Visual-Mnemonic Space

Single neurons in primate dorsolateral prefrontal cortex (dLPFC) are known to encode working memory (WM) representations of visual space. Psychophysical studies have shown that the horizontal and vertical meridians of the visual field can bias spatial information maintained in WM. However, most studies and models have tacitly assumed that dLPFC neurons represent mnemonic space homogenously. The anatomical organization of these representations has also eluded clear parametric description. We investigated these issues by recording from neuronal ensembles in macaque dLPFC with microelectrode arrays while subjects performed an oculomotor delayed-response task. We found that spatial WM representations in macaque dLPFC are biased by the vertical and horizontal meridians of the visual field, dividing mnemonic space into quadrants. This bias is reflected in single neuron firing rates, neuronal ensemble representations, the spike count correlation structure, and eye movement patterns. We also found that dLPFC representations of mnemonic space cluster anatomically in a nonretinotopic manner that partially reflects the organization of visual space. These results provide an explanation for known WM biases, and reveal novel principles of WM representation in prefrontal neuronal ensembles and across the cortical surface, as well as the need to reconceptualize models of WM to accommodate the observed representational biases.

Realtime phase-amplitude coupling analysis of micro electrode recorded brain signals

Objective To demonstrate a method to calculate phase amplitude coupling (PAC) quickly and robustly for realtime applications. Methods We designed and implemented a multirate PAC algorithm with efficient filter bank processing and efficient computation of PAC for many frequency-pair combinations. We tested the developed algorithm for computing PAC on simulated data and on intraoperative neural recording data obtained during deep brain stimulation (DBS) electrode implantation surgery. Results A combination of parallelized frequency-domain filtering and modulation index for PAC estimation provided robust results that could be calculated in real time on modest computing hardware. Conclusion The standard methods for calculating PAC can be optimized for quick and robust performance. Significance These results demonstrated that PAC can be extracted in real time and is suitable for neurofeedback applications.

A metric-based analysis of the contribution of spike timing to contrast and motion direction coding by single neurons in macaque area MT

Spike timing is thought to contribute to the coding of motion direction information by neurons in macaque area MT. Here, we examined whether spike timing also contributes to the coding of stimulus contrast. We applied a metric-based approach to spike trains fired by MT neurons in response to stimuli that varied in contrast, or direction. We assessed the performance of three metrics, D(spike) and D(product) (containing spike count and timing information), and the spike count metric D(count). We analyzed responses elicited during the first 200 msec of stimulus presentation from 205 neurons. For both contrast and direction, the large majority of neurons showed the highest mutual information using D(spike), followed by D(product), and D(count). This was corroborated by the performance of a theoretical observer model at discriminating contrast and direction using the three metrics. Our results demonstrate that spike timing can contribute to contrast coding in MT neurons, and support previous reports of its potential contribution to direction coding. Furthermore, they suggest that a combination of spike count with periodic and non-periodic spike timing information (contained in D(spike), but not in D(product) and D(count) which are insensitive to spike counts and timing respectively) provides the largest coding advantage in spike trains fired by MT neurons during contrast and direction discrimination.