Search

© 2019 Elsevier Ltd Trainito et al. use a data-driven approach to robustly identify four cell classes from extracellular spike waveforms recorded in three cortical regions of macaque monkeys. The four cell classes are functionally distinct in terms of firing statistics, response dynamics, and information coding.

Many recent advances in artificial intelligence (AI) are rooted in visual neuroscience. However, ideas from more complicated paradigms like decision-making are less used. Although automated decision-making systems are ubiquitous (driverless cars, pilot support systems, medical diagnosis algorithms etc.), achieving human-level performance in decision making tasks is still a challenge. At the same time, these tasks that are hard for AI are easy for humans. Thus, understanding human brain dynamics during these decision-making tasks and modeling them using deep neural networks could improve AI performance. Here we modelled some of the complex neural interactions during a sensorimotor decision making task. We investigated how brain dynamics flexibly represented and distinguished between sensory processing and categorization in two sensory domains: motion direction and color. We found that neural representations changed depending on context. We also trained deep recurrent neural networks to perform the same tasks as the animals. By comparing brain dynamics with network predictions, we found that computations in different brain areas also changed flexibly depending on context. Color computations appeared to rely more on sensory processing, while motion computations more on abstract categories. Overall, our results shed light to the biological basis of categorization and differences in selectivity and computations in different brain areas., NIMH (Grant R37MH087027)

Many recent advances in artificial intelligence (AI) are rooted in visual neuroscience. However, ideas from more complicated paradigms like decision-making are less used. Although automated decision-making systems are ubiquitous (driverless cars, pilot support systems, medical diagnosis algorithms etc.), achieving human-level performance in decision making tasks is still a challenge. At the same time, these tasks that are hard for AI are easy for humans. Thus, understanding human brain dynamics during these decision-making tasks and modeling them using deep neural networks could improve AI performance. Here we modelled some of the complex neural interactions during a sensorimotor decision making task. We investigated how brain dynamics flexibly represented and distinguished between sensory processing and categorization in two sensory domains: motion direction and color. We found that neural representations changed depending on context. We also trained deep recurrent neural networks to perform the same tasks as the animals. By comparing brain dynamics with network predictions, we found that computations in different brain areas also changed flexibly depending on context. Color computations appeared to rely more on sensory processing, while motion computations more on abstract categories. Overall, our results shed light to the biological basis of categorization and differences in selectivity and computations in different brain areas.

Copyright: © 2020 Ito et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Many large-scale functional connectivity studies have emphasized the importance of communication through increased inter-region correlations during task states. In contrast, local circuit studies have demonstrated that task states primarily reduce correlations among pairs of neurons, likely enhancing their information coding by suppressing shared spontaneous activity. Here we sought to adjudicate between these conflicting perspectives, assessing whether co-active brain regions during task states tend to increase or decrease their correlations. We found that variability and correlations primarily decrease across a variety of cortical regions in two highly distinct data sets: non-human primate spiking data and human functional magnetic resonance imaging data. Moreover, this observed variability and correlation reduction was accompanied by an overall increase in dimensionality (reflecting less information redundancy) during task states, suggesting that decreased correlations increased information coding capacity. We further found in both spiking and neural mass computational models that task-evoked activity increased the stability around a stable attractor, globally quenching neural variability and correlations. Together, our results provide an integrative mechanistic account that encompasses measures of large-scale neural activity, variability, and correlations during resting and task states.

It remains challenging to relate EEG and MEG to underlying circuit processes and comparable experiments on both spatial scales are rare. To close this gap between invasive and non-invasive electrophysiology we developed and recorded human-comparable EEG in macaque monkeys during visual stimulation with colored dynamic random dot patterns. Furthermore, we performed simultaneous microelectrode recordings from 6 areas of macaque cortex and human MEG. Motion direction and color information were accessible in all signals. Tuning of the non-invasive signals was similar to V4 and IT, but not to dorsal and frontal areas. Thus, MEG and EEG were dominated by early visual and ventral stream sources. Source level analysis revealed corresponding information and latency gradients across cortex. We show how information-based methods and monkey EEG can identify analogous properties of visual processing in signals spanning spatial scales from single units to MEG - a valuable framework for relating human and animal studies., National Institute of Mental Health (U.S.) (Grant R37MH087027)

Somewhere along the cortical hierarchy, behaviorally relevant information is distilled from raw sensory inputs. We examined how this transformation progresses along multiple levels of the hierarchy by comparing neural representations in visual, temporal, parietal, and frontal cortices in monkeys categorizing across three visual domains (shape, motion direction, and color). Representations in visual areas middle temporal (MT) and V4 were tightly linked to external sensory inputs. In contrast, lateral prefrontal cortex (PFC) largely represented the abstracted behavioral relevance of stimuli (task rule, motion category, and color category). Intermediate-level areas, including posterior inferotemporal (PIT), lateral intraparietal (LIP), and frontal eye fields (FEF), exhibited mixed representations. While the distribution of sensory information across areas aligned well with classical functional divisions (MT carried stronger motion information, and V4 and PIT carried stronger color and shape information), categorical abstraction did not, suggesting these areas may participate in different networks for stimulus-driven and cognitive functions. Paralleling these representational differences, the dimensionality of neural population activity decreased progressively from sensory to intermediate to frontal cortex. This shows how raw sensory representations are transformed into behaviorally relevant abstractions and suggests that the dimensionality of neural activity in higher cortical regions may be specific to their current task. Keywords: categorization; cognition; prefrontal cortex; posterior parietal cortex; dimensionality, National Institute of Mental Health (U.S.) (Grant 5R37MH087027)

During flexible behavior, multiple brain regions encode sensory inputs, the current task, and choices. It remains unclear how these signals evolve. We simultaneously recorded neuronal activity from six cortical regions [middle temporal area (MT), visual area four (V4), inferior temporal cortex (IT), lateral intraparietal area (LIP), prefrontal cortex (PFC), and frontal eye fields (FEF)] of monkeys reporting the color or motion of stimuli. After a transient bottom-up sweep, there was a top-down flow of sustained task information from frontoparietal to visual cortex. Sensory information flowed from visual to parietal and prefrontal cortex. Choice signals developed simultaneously in frontoparietal regions and travelled to FEF and sensory cortex. This suggests that flexible sensorimotor choices emerge in a frontoparietal network from the integration of opposite flows of sensory and task information., National Institute of Mental Health (U.S.) (Grant 5R37MH087027), Picower Institute for Learning and Memory (Innovation Fund), National Institutes of Health (U.S.) (Grant R00 MH092715), Deutsche Forschungsgemeinschaft. Centre for Integrative Neuroscience (EXC 307)

Theories of autism spectrum disorders (ASD) have focused on altered perceptual integration of sensory features as a possible core deficit. Yet, there is little understanding of the neuronal processing of elementary sensory features in ASD. For typically developed individuals, we previously established a direct link between frequency-specific neural activity and the intensity of a specific sensory feature: Gamma-band activity in the visual cortex increased approximately linearly with the strength of visual motion. Using magnetoencephalography (MEG), we investigated whether in individuals with ASD neural activity reflect the coherence, and thus intensity, of visual motion in a similar fashion. Thirteen adult participants with ASD and 14 control participants performed a motion direction discrimination task with increasing levels of motion coherence. A polynomial regression analysis revealed that gamma-band power increased significantly stronger with motion coherence in ASD compared to controls, suggesting excessive visual activation with increasing stimulus intensity originating from motion-responsive visual areas V3, V6 and hMT/V5. Enhanced neural responses with increasing stimulus intensity suggest an enhanced response gain in ASD. Response gain is controlled by excitatory-inhibitory interactions, which also drive high-frequency oscillations in the gamma-band. Thus, our data suggest that a disturbed excitatory-inhibitory balance underlies enhanced neural responses to coherent motion in ASD.

Simple perceptual decisions are ideally suited for studying the sensorimotor transformations underlying flexible behavior 1 and 2. During perceptual detection, a noisy sensory signal is converted into a behavioral report of the presence or absence of a perceptual experience [3]. Here, we used magnetoencephalography (MEG) to link the dynamics of neural population activity in human motor cortex to perceptual choices in a “yes/no” visual motion detection task. We found that (1) motor response-selective MEG activity in the “gamma” (64–100 Hz) and “beta” (12–36 Hz) frequency ranges predicted subjects' choices several seconds before their overt manual response; (2) this choice-predictive activity built up gradually during stimulus viewing toward both “yes” and “no” choices; and (3) the choice-predictive activity in motor cortex reflected the temporal integral of gamma-band activity in motion-sensitive area MT during stimulus viewing. Because gamma-band activity in MT reflects visual motion strength [4], these findings suggest that, during motion detection, motor plans for both “yes” and “no” choices result from continuously accumulating sensory evidence. We conclude that frequency-specific neural population activity at the cortical output stage of sensorimotor pathways provides a window into the mechanisms underlying perceptual decisions., Volkswagen Foundation (Grant II/80609), Germany. Federal Ministry of Education and Research (01GW0561), German Academy of Sciences Leopoldina (BMBF-LPD 9901/8-136), Hans-Lungwitz-Stiftung, National Institutes of Health (U.S.) (R01-EY16752), Netherlands Organization for Scientific Research, Human Frontier Science Program (Strasbourg, France), European Science Foundation (European Young Investigator Award Program), European Union (IST-2005-027268), European Union (NEST- PATH-043457), European Union (HEALTH-F2-2008-200728 Grant), German Research Foundation (GRK 1247/1)