Biology subjects

Dann, B.

Publications and source records attributed to Dann, B..

3 recordsLinked to original sources

bioRxiv2021

Information dynamics in neuronal networks of macaque cerebral cortex reflect cognitive state and behavior.

One of the essential functions biological neural networks is the processing of information. This comprises processing sensory information to perceive the environment, up to processing motor information to interact with the environment. Due to methodological concerns, it has been historically unclear how information processing changes during different cognitive or behavioral states, and to what extent information is processed within or between the network of neurons in different brain areas. In this study, we leverage recent advances in the calculation of information dynamics to explore neural-level processing within and between the fronto-parietal areas AIP, F5 and M1 during a delayed grasping task performed by three macaque monkeys. While information processing was high within all areas during all cognitive and behavioral states of the task, inter-areal processing varied widely: during visuo-motor transformation, AIP and F5 formed a reciprocally connected processing unit, while no processing was present between areas during the memory period. Movement execution was processed globally across all areas with a predominance of processing in the feedback direction. Additionally, the fine-scale network structure re-configured at the neuron-level in response to different grasping conditions, despite of no differences in the overall amount of information present. These results suggest that areas dynamically form higher-order processing units according to the cognitive or behavioral demand, and that the information processing network is hierarchically organized at the neuron-level, with the coarse network structure determining the behavioral state and finer changes reflecting different conditions. Significance StatementWhat does it mean to say that the brain "processes information?" Scientists often discuss the brain in terms of information processing - animals take in information from their environment through their senses, and use it to make decisions about how to act in the world. In this work, we use a mathematical framework called information theory to explore how signals from the environment influence brain activity, and how brain activity in turn informs on behaviors. We found that different brain regions processed information in dynamic and flexible ways, with signals flowing up and down the hierarchy of sensory-motor depending on the demands of the moment. This shows how "computation" in the brain can reflect complex behaviors and cognitive states.

neuroscience↗

bioRxiv2020

A general theory of coherence between brain areas

What does neuronal coherence tell us about neuronal communication? Does coherence between field potentials (e.g. LFP, EEG, MEG) reflect spiking entrainment or coupling between oscillators? Is it a mechanism for communication between brain areas, or a byproduct of interareal connectivity and spectral power? We hypothesized that interareal coherence is explained by the fact that outputs from one cortical area give rise to synaptic inputs in the same brain area, and correlated synaptic inputs in another area. Our mathematical analysis demonstrates that coherence between a sending and receiving area is precisely predicted from only two parameters: Interareal connectivity and oscillatory synchronization in the sending area. This model predicts narrow-band coherence even in case of a flat transfer function and in the absence of spiking entrainment in a downstream area, and reproduces frequency-specific Granger-causality patterns between brain areas (gamma feedforward, beta feedback). In general, we find that Granger-causality between field potentials is dominated by oscillatory synchronization in the sending area, whereas spiking entrainment is determined by the resonant properties of the receiver. Our model accurately reproduces LFP-LFP beta-coherence between macaque areas 7B and F5 in the absence of beta phase-locking within area F5. Together, these findings uncover a precise mechanistic model of interareal coherence as a (by)product of connectivity and power.

neuroscience↗

bioRxiv2017

Neural dynamics of variable grasp movement preparation in the macaque fronto-parietal network

Our voluntary grasping actions lie on a continuum between immediate action and waiting for the right moment, depending on the context. Therefore, studying grasping requires investigating how preparation time affects this process. Two macaque monkeys (Macaca mulatta) performed a grasping task with a short instruction followed by an immediate or delayed go cue (0-1300 ms) while we recorded in parallel from neurons in the hand area (F5) of the ventral premotor cortex and the anterior intraparietal area (AIP). Initial population dynamics followed a fixed trajectory in the neural state space unique to each grip type, reflecting unavoidable preparation, then diverged depending on the delay. Although similar types of single unit responses were present in both areas, population activity in AIP stabilized within a unique memory state while F5 activity continued to evolve, tracking subjective anticipation of the go cue. Intriguingly, activity during movement initiation clustered into two trajectory clusters, corresponding to movements that were either as fast as possible or withheld movements, demonstrating a widespread state shift in the fronto-parietal grasping network when movements must be withheld. Our results reveal how dissociation between static and dynamic components of movement preparation as well as differentiation between cortical areas is possible through population level analysis.\n\nSignificance StatementMany of our movements must occur with no warning, while others we can prepare in advance. Yet, its unclear how planning for movements along the spectrum between these two situations differs in the brain. Two macaque monkeys made reach to grasp movements after varying amounts of preparation time while we recorded from premotor and parietal cortex. We found that the initial response to a grasp instruction was specific to the required movement, but not the preparation time, reflecting required processing. However, when more preparation time was given, neural activity achieved unique states that likely related to withholding movements and anticipation of movement, which was more prevalent in premotor cortex, suggesting differing roles of premotor and parietal cortex in grasp planning.

neuroscience↗