Biology subjects

Panichello, M.

Publications and source records attributed to Panichello, M..

4 recordsLinked to original sources

bioRxiv2026

Condition-Dependent Noise Correlations without Condition-Dependent Spike Counts

The ability of the brain to encode information and control behavior depends on the coordinated activity of large and distributed neuronal populations. Correlations in neuronal spiking activity across trials of the same condition, or noise correlations (NCs), have been interpreted as a reflection of shared synaptic connectivity and as a contributing factor to the information capacity of neuronal populations. The impact of NCs on coding is most often considered in populations of neurons exhibiting robust condition-dependent information in their spike counts (SCs). However, theoretical work suggests that NCs could provide a source of condition-dependent information separate from SCs. We examined the activity of large neuronal populations in prefrontal cortex of macaques while they performed a spatial delayed response task composed of visual, memory, and motor epochs. We found that pairs of neurons that displayed visual, memory, and motor selectivity in their SCs often exhibited selectivity in their NCs, independent of spike count. However, we also found that pairs of neurons without SC selectivity during the different task epochs nonetheless exhibited condition-dependent NCs. Moreover, we found that the prevalence of condition-dependent NCs were largely comparable across neuronal pairs with or without SC selectivity. These results demonstrate that correlated variability in spiking activity can be condition-dependent even in the absence of condition-dependent SCs.

neuroscience↗

bioRxiv2026

A System for Live Sorting of Neuronal Spiking Activity from Large-scale Recordings

Online monitoring of neuronal activity has tremendous value for closed-loop control in both experimental and clinical settings, yet established methods rely primarily on thresholded neural activity, rather than sorted single-neuron spikes. The recent introduction of large-scale electrophysiological tools has greatly expanded single-neuron recording capacity in animals and humans highlighting the need to sort spikes during data collection. Here, we describe a system for live spike sorting (LSS) populations of hundreds of neurons with millisecond-scale latency. Using neurophysiological recordings from macaque visual cortex with Neuropixels probes, we show that LSS closely replicates the temporal responses and tuning of single neurons obtained using offline sorting. We further show that decoding neural signals with LSS achieves the same performance as that obtained from offline sorting. Lastly, we demonstrate the capacity of LSS to enable closed-loop interventions based on the activity of specific neuronal subclasses.

neuroscience↗

bioRxiv2023

The neural basis of swap errors in working memory

When making decisions in a cluttered world, humans and other animals often have to hold multiple items in memory at once - such as the different items on a shopping list. Psychophysical experiments in humans and other animals have shown remembered stimuli can sometimes become confused, with participants reporting chimeric stimuli composed of features from different stimuli. In particular, subjects will often make "swap errors" where they misattribute a feature from one object as belonging to another object. While swap errors have been described behaviorally, their neural mechanisms are unknown. Here, we elucidate these neural mechanisms through trial-by-trial analysis of neural population recordings from posterior and frontal brain regions while monkeys perform two multi-stimulus working memory tasks. In these tasks, monkeys were cued to report the color of an item that either was previously shown at a corresponding location (requiring selection from working memory) or will be shown at the corresponding location (requiring attention to a position). Animals made swap errors in both tasks. In the neural data, we find evidence that the neural correlates of swap errors emerged when correctly remembered information is selected incorrectly from working memory. This led to a representation of the distractor color as if it were the target color, underlying the eventual swap error. We did not find consistent evidence that swap errors arose from misinterpretation of the cue or errors during encoding or storage in working memory. These results suggest an alternative to established views on the neural origins of swap errors, and highlight selection from and manipulation in working memory as crucial - yet surprisingly brittle - neural processes.

neuroscience↗

bioRxiv2020

Delay-period activity in frontal, parietal, and occipital cortex tracks different attractor dynamics in visual working memory

One important neural hallmark of working memory is persistent elevated delay-period activity in frontal and parietal cortex. In human fMRI, delay-period BOLD activity in frontal and parietal cortex increases monotonically with memory load and asymptotes at an individuals capacity. Previous work has demonstrated that frontal and parietal delay-period activity correlates with the decline in behavioral memory precision observed with increasing memory load. However, because memory precision can be influenced by a variety of factors, it remains unclear what cognitive processes underlie persistent activity in frontal and parietal cortex. Recent psychophysical work has shown that attractor dynamics bias memory representations toward a few stable representations and reduce the effects of internal noise. From this perspective, imprecision in memory results from both drift towards stable attractor states and random diffusion. Here we asked whether delay-period BOLD activity in frontal and parietal cortex might be explained, in part, by these attractor dynamics. We analyzed data from an existing experiment in which subjects performed delayed recall for line orientation, at different loads, during fMRI scanning. We modeled subjects behavior using a discrete attractor model, and calculated within-subject correlation between frontal and parietal delay-period activity and estimated sources of memory error (drift and diffusion). We found that although increases in frontal and parietal activity were associated with increases in both diffusion and drift, diffusion explained the most variance in frontal and parietal delay-period activity. In comparison, a subsequent whole-brain regression analysis showed that drift rather than diffusion explained the most variance in delay-period activity in lateral occipital cortex. These results provide a new interpretation for the function of frontal, parietal, and occipital delay-period activity in working memory.

neuroscience↗