Cortex

Daniel Levenstein discusses spontaneous activity in the hippocampus and cortex, especially during sleep, and its relationship to learning, memory and navigation. He explains how AI models can help investigate these dynamics, alongside questions about model systems, cognitive maps and the relationship between neuroscience experiments and theory.

Current machine learning systems consume vastly more energy than biological brains. Neuromorphic systems aim to overcome this difference by mimicking the brain’s information coding via discrete voltage spikes. However, it remains unclear how both artificial and natural networks of spiking neurons can learn energy-efficient information processing strategies. Here we propose Predictive Coding Light (PCL), a recurrent hierarchical spiking neural network for unsupervised representation learning. In contrast to previous predictive coding approaches, PCL does not transmit prediction errors to higher

…What are the contributions of the cortex, basal ganglia, and thalamus to behavior? In addressing these questions, we often ascribe function to each area as if it were an independent processing unit. However, we know from the anatomy that the cortex, basal ganglia, and thalamus, are ma

…abundance of lateral connections in cortex. Because they are universal approximators, they can be trained to match behavior in common neuroscience tasks, and their hidden state can be compared to neural recordings. Despite the biological inspiration, many features of real neurons are abstracted away (e.g. morphology, neuromodulators, excitability dynamics, short term plasticity). While many of these properties were included as add-ons to standard RNN models,

A major organizing principle of the brain proposes that the cortex, basal ganglia, and thalamus are interconnected in parallel recurrent loops. According to this model, the two principal basal ganglia pathways – termed direct and indirect – are thought to excite and inhibit cortical activity via their outputs to thalamus. Here, we test this model by simultaneously recording activity across cortex and striatum, while inhibiting the direct or indirect basal ganglia pathway as mice perform an evidence-accumulation task in which each manipulation produces opposing decision biases. Inconsistent wit

With the recent success of Transformers in the field of automatic natural language processing [1], it has become clear that we are able to create complex, high-performance neural network architectures that can extract, combine and transform information to meet a specific objective, such as predicting the next word in a text. However, to function, Transformers need to receive a context of all N elements of the past sequence (N = 2k to 32k tokens) at each time step, causing the number of computations to grow quadratically (N^2) with the length of the sequence [1]. This "perfect" memory of a co

…in the Brainstem → Thalamus → Cortex axis. This organization results in the formation of barrels and intervening "spaces" known as septa. Our in-vivo silicon probe recordings, conducted simultan

The wide-spread connectivity of the claustrum with the cortex has suggested that it is involved in higher-cognitive functions. However, in mouse recent findings suggest a topographical organization with the cortex and a sensory regionalization of the claustrum. Our earlier work reports important differences in the organizational principals of mouse and primate cortical connectivity. Hence, we have undertaken extensive examination of the projections between claustrum and cortex in macaque using retrograde tracing. Surprisingly, we find that these connections do not obey the exponential distance

…and an absence of this gene in the Cortex and CA1 regio

…and wave discharges (SWDs) in the cortex and thalamus. Seizure expression varied among animals, with slow (4 – 5 Hz) and/or fast (6 - 8 Hz) SWDs present. Slow 4-5 Hz SWDs occurred during passive wakefulness and were abundant du

…transferred from the hippocampus to cortex. But with the advance of recording techniques, it appears that many memories are learned and represented in multiple brain regions simultaneously. Moreover, representations change dynamically while the behavior of animals remains stable. Here, we propose a population perspective on memory consolidation that reproduces many features of this “representational drift”. We develop an analytically tractable model for the dynamics of memory engrams that is structurally similar to a recurren

…hippocampus, amygdala, thalamus, and cortex It has been hypothesized that these distributed engrams are functionally connected [2] This neural network can be specifically reactivated, resulting in the recall of the original memory [3]However, how the interaction among multiple regions facilitates the consolidation, o

…from dorsal hippocampus and overlaying cortex of 13 male Long-Evans rats before and after LPS injection. Sleep-wake states were scored for cortex and hippocampus, and analyzed using a 3-D power-spectrum-based state-space approach. Additionally, aperiodic

…transferred from the hippocampus to cortex and between cortical areas for long-term storage [1]. But with the advance of neuronal recording techniques, it has become apparent that most variables of the outside world are learned and represented in multiple brain regions simultaneously. In some brain regions, these representations change dynamically while the behavior of the animal remains stable [2]. Here, we propose a population perspective on memory consolidation that reproduces many features of this “representational drift”

…of the functional organization of cortex – as m

…been predominantly attributed to the cortex. Recent experiments, however, have concluded that the thalamus and other subcortical structures participate in this function. A comprehensive computational framework to link neural dynamics and cognition in the context of large-scale subcortical-cortical circuits is lacking. In this computational study, we elucidated the dynamical mechanisms by which the cortex, thalamus and other subcortical structures jointly contribute to planning. Recurrent circuitry in the cort

…circuits in the hippocampus and cortex [3]. Our model predicts that the

A variety of algorithms have been proposed to account for goal-directed planning, operationalized as sensitivity to reward revaluation i.e. flexibility in the face of reward changes. These algorithms use different representations which trade off computational cost with flexibility in the face of changes in the world. In particular, model-based (MB) reinforcement learning uses tree-search through 1-step relational representations that are often thought of as ‘semantic’. In contrast, models like the successor representation (SR) store representations of sequential experience. These algorithms ca

The interaction between spontaneously and externally evoked neuronal activity is fundamental for a functional brain. Increasing evidence suggests that bursts of high-power oscillations in the 15-30 Hz beta-band represent activation of resting state networks and can mask perception of external cues. Yet demonstration of the effect of beta power modulation on perception in real-time is missing, and little is known about the underlying mechanism. In this talk I will present the methods we developed to fill this gap together with our recent results. We used a closed-loop stimulus-intensity adjustm

…in the hippocampus and the entorhinal cortex store and exchange information during slow and theta oscillations. We equate the storage and sharing of information to low level, or primiti

…realistic model of the monkey visual cortex. Work on one of the input layers (4Ca) of the primary visual cortex (V1) is now nearly complete, and I would like to share some of what I have learned with the community. After a brief overview of the model and its capabilities, I would like to focus on three sets of results that represent three different aspects of the modeling. They are: (i) emergent E-I dynamics in local circuits; (ii) how visual cortical neurons acquire their ability to detect edges and directions of motio

According to the theory of predictive processing, expectations modulate neural activity so as to optimize the processing of sensory inputs expected in the current environment. While there is accumulating evidence that the brain indeed operates under this principle, most of the attention has been placed on mechanisms that rely on static coding properties of neurons. The potential contribution of dynamical features, such as those reflected in the evolution of neural population dynamics, has thus far been overlooked. In this talk, I will present evidence for a novel mechanism for predictive proce

Many regions of the human brain conduct highly specific functions, such as recognizing faces, understanding language, and thinking about other people’s thoughts. Why might this domain specific organization be a good design strategy for brains, and what is the origin of domain specificity in the first place? In this talk, I will present recent work testing whether the segregation of face and object perception in human brains emerges naturally from an optimization for both tasks. We trained artificial neural networks on face and object recognition, and found that networks were able to perform bo

…neuroimaging studies suggest insular cortex plays a central role in interoception, yet the cellular and circuit mechanisms of its involvement remain unclear. We developed a microprism-based cellular imaging approach to monitor insular cortex activity in behaving mice across different physiological need states. We combine this imaging approach with manipulations of peripheral physiology, circuit-mapping, cell type-specific and circuit-specific manipulation approaches t