Inferring context-dependent computations through linear approximations of prefrontal cortex dynamics

The complex neural population activity of prefrontal cortex (PFC) is a hallmark of cognitive processes. How these rich dynamics emerge and support neural computations is largely unknown. Here, we infer mechanisms underlying the context-dependent selection and integration of sensory inputs by fitting dynamical models to PFC population responses of behaving monkeys. A class of models implementing linear dynamics driven by external inputs accurately captured the PFC responses within each context, achieving performance comparable to models without linear constraints. Two distinct mechanisms of input selection and integration were equally consistent with the data. One implemented context-dependent recurrent dynamics, as previously proposed, and relied on transient input amplification. The other relied on the subtle contextual modulation of the inputs, providing quantitative constraints on the attentional effects in sensory areas required to explain flexible PFC responses and behavior. Both mechanisms consistently revealed properties of inputs and recurrent dynamics missing in more simplified, incomplete descriptions of PFC responses. By revealing mechanisms consistent with rich cortical dynamics, our modeling approach provides a principled and general framework to link neural population activity and computation

Primate pre-arcuate cortex actively maintains persistent representations of saccades from plans to outcomes

Dorso-lateral prefrontal cortex is thought to contribute to adaptive behavior by integrating temporally dispersed, behaviorally-relevant factors. Past work has revealed a variety of neural representations preceding actions, which are involved in internal processes like planning, working memory and covert attention. Task-related activity following actions has often been reported, but so far lacks a clear interpretation. We leveraged modified versions of classic oculomotor paradigms and population recordings to show that post-saccadic activity is a dominant signal in dorso-lateral prefrontal cortex that is distinct from pre-saccadic activity. Unlike pre-saccadic activity, post-saccadic activity occurs after each saccade, although its strength and duration are modulated by task context and expected rewards. In contrast to representations preceding actions, which appear to be mixed randomly across neurons, post-saccadic activity results in representations that are highly structured at the single-neuron and population level. Overall, the properties of post-saccadic activity are consistent with those of an action memory, an internal process with a possible role in learning and updating spatial representations

Improving SeNA-CNN by Automating Task Recognition

Catastrophic forgetting arises when a neural network is not capable of preserving the past learned task when learning a new task. There are already some methods proposed to mitigate this problem in arti cial neural networks. In this paper we propose to improve upon our previous state-of-the-art method, SeNA-CNN, such as to enable the automatic recognition in test time of the task to be solved and we experimentally show that it has excellent results. The experiments show the learning of up to 4 di erent tasks with a single network, without forgetting how to solve previous learned tasks.info:eu-repo/semantics/publishedVersio

Are task representations gated in macaque prefrontal cortex?

A recent paper (Flesch et al, 2022) describes behavioural and neural data suggesting that task representations are gated in the prefrontal cortex in both humans and macaques. This short note proposes an alternative explanation for the reported results from the macaque data

A new theoretical framework jointly explains behavioral and neural variability across subjects performing flexible decision-making

The ability to flexibly select and accumulate relevant information to form decisions, while ignoring irrelevant information, is a fundamental component of higher cognition. Yet its neural mechanisms remain unclear. Here we demonstrate that, under assumptions supported by both monkey and rat data, the space of possible network mechanisms to implement this ability is spanned by the combination of three different components, each with specific behavioral and anatomical implications. We further show that existing electrophysiological and modeling data are compatible with the full variety of possible combinations of these components, suggesting that different individuals could use different component combinations. To study variations across subjects, we developed a rat task requiring context-dependent evidence accumulation, and trained many subjects on it. Our task delivers sensory evidence through pulses that have random but precisely known timing, providing high statistical power to characterize each individual’s neural and behavioral responses. Consistent with theoretical predictions, neural and behavioral analysis revealed remarkable heterogeneity across rats, despite uniformly good task performance. The theory further predicts a specific link between behavioral and neural signatures, which was robustly supported in the data. Our results provide a new experimentally-supported theoretical framework to analyze biological and artificial systems performing flexible decision-making tasks, and open the door to the study of individual variability in neural computations underlying higher cognition

Population-level neural correlates of flexible avoidance learning in medial prefrontal cortex

The medial prefrontal cortex (mPFC) has been proposed to link sensory inputs and behavioral outputs to mediate the execution of learned behaviors. However, how such a link is implemented has remained unclear. To measure prefrontal neural correlates of sensory stimuli and learned behaviors, we performed population calcium imaging during a novel tone-signaled active avoidance paradigm in mice. We developed a novel analysis approach based on dimensionality reduction and decoding that allowed us to identify and isolate population activity patterns related the tone stimulus, learned avoidance actions and general motion. While tone-related activity was not informative about behavior, avoidance-related activity was predictive of upcoming avoidance actions. Moreover, avoidance-related activity distinguished between two different learned avoidance actions, consistent with a model in which mPFC contributes to the selection between different goal-directed actions. Overall, our results suggest that mPFC circuit dynamics transform sensory inputs into specific behavioral outputs through distributed population-level computations

Deep-learning-based identification, tracking, pose estimation and behaviour classification of interacting primates and mice in complex environments

The quantification of behaviors of interest from video data is commonly used to study brain function, the effects of pharmacological interventions, and genetic alterations. Existing approaches lack the capability to analyze the behavior of groups of animals in complex environments. We present a novel deep learning architecture for classifying individual and social animal behavior, even in complex environments directly from raw video frames, while requiring no intervention after initial human supervision. Our behavioral classifier is embedded in a pipeline (SIPEC) that performs segmentation, identification, pose-estimation, and classification of complex behavior, outperforming the state of the art. SIPEC successfully recognizes multiple behaviors of freely moving individual mice as well as socially interacting non-human primates in 3D, using data only from simple mono-vision cameras in home-cage setups

Visual Cortex: Seeing Motion

tion and speed brought the orientation of the `equivalent long bar' closer to or further from the orientation of the short bar. These results flatly contradict the bulk the literature on optical imaging, which postulates or reports explicit maps of preferred orientation, spatial frequency and direction [7--11]. According to this literature, a stimulus with a given orientation, spatial frequency and direction should have elicited responses in those pixels that lie at the intersection of the corresponding maps. The results come as less of a surprise, however, if one considers the literature on responses of single neurons. After a debate that raged in the 1970s, it was largely agreed that V1 neurons do not isolate this or that feature of the stimulus. For example, an elegant study by the De Valois group [12] demonstrated that V1 neurons do not encode orientation independently of other features: preferred orientation depends on stimulus spatial frequency as predicted by a simple model b