Many animals rely on an internal heading representation when navigating in varied environments. How this representation is linked to the sensory cues that define different surroundings is unclear. In the fly brain, heading is represented by 'compass' neurons that innervate a ring-shaped structure known as the ellipsoid body. Each compass neuron receives inputs from 'ring' neurons that are selective for particular visual features; this combination provides an ideal substrate for the extraction of directional information from a visual scene. Here we combine two-photon calcium imaging and optogenetics in tethered flying flies with circuit modelling, and show how the correlated activity of compass and visual neurons drives plasticity, which flexibly transforms two-dimensional visual cues into a stable heading representation. We also describe how this plasticity enables the fly to convert a partial heading representation, established from orienting within part of a novel setting, into a complete heading representation. Our results provide mechanistic insight into the memory-related computations that are essential for flexible navigation in varied surroundings.

Hippocampal place cells encode the animal's spatial position. However, it is unknown how different long-range sensory systems affect spatial representations. Here we alternated usage of vision and echolocation in Egyptian fruit bats while recording from single neurons in hippocampal areas CA1 and subiculum. Bats flew back and forth along a linear flight track, employing echolocation in darkness or vision in light. Hippocampal representations remapped between vision and echolocation via two kinds of remapping: subiculum neurons turned on or off, while CA1 neurons shifted their place fields. Interneurons also exhibited strong remapping. Finally, hippocampal place fields were sharper under vision than echolocation, matching the superior sensory resolution of vision over echolocation. Simulating several theoretical models of place-cells suggested that combining sensory information and path integration best explains the experimental sharpening data. In summary, here we show sensory-based global remapping in a mammal, suggesting that the hippocampus does not contain an abstract spatial map but rather a 'cognitive atlas', with multiple maps for different sensory modalities.

Place cells in the CA1 region of the hippocampus express location-specific firing despite receiving a steady barrage of heterogeneously tuned excitatory inputs that should compromise output dynamic range and timing. We examined the role of synaptic inhibition in countering the deleterious effects of off-target excitation. Intracellular recordings in behaving mice demonstrate that bimodal excitation drives place cells, while unimodal excitation drives weaker or no spatial tuning in interneurons. Optogenetic hyperpolarization of interneurons had spatially uniform effects on place cell membrane potential dynamics, substantially reducing spatial selectivity. These data and a computational model suggest that spatially uniform inhibitory conductance enhances rate coding in place cells by suppressing out-of-field excitation and by limiting dendritic amplification. Similarly, we observed that inhibitory suppression of phasic noise generated by out-of-field excitation enhances temporal coding by expanding the range of theta phase precession. Thus, spatially uniform inhibition allows proficient and flexible coding in hippocampal CA1 by suppressing heterogeneously tuned excitation.

Neurons in multiple brain regions fire trains of action potentials anticipating specific movements, but this 'preparatory activity' has not been systematically compared across behavioral tasks. We compared preparatory activity in auditory and tactile delayed-response tasks in male mice. Skilled, directional licking was the motor output. The anterior lateral motor cortex (ALM) is necessary for motor planning in both tasks. Multiple features of ALM preparatory activity during the delay epoch were similar across tasks. First, majority of neurons showed direction-selective activity and spatially intermingled neurons were selective for either movement direction. Second, many cells showed mixed coding of sensory stimulus and licking direction, with a bias toward licking direction. Third, delay activity was monotonic and low-dimensional. Fourth, pairs of neurons with similar direction selectivity showed high spike-count correlations. Our study forms the foundation to analyze the neural circuit mechanisms underlying preparatory activity in a genetically tractable model organism.Short-term memories link events separated in time. Neurons in frontal cortex fire trains of action potentials anticipating specific movements, often seconds before the movement. This 'preparatory activity' has been observed in multiple brain regions, but has rarely been compared systematically across behavioral tasks in the same brain region. To identify common features of preparatory activity, we developed and compared preparatory activity in auditory and tactile delayed-response tasks in mice. The same cortical area is necessary for both tasks. Multiple features of preparatory activity, measured with high-density silicon probes, were similar across tasks. We find that preparatory activity is low-dimensional and monotonic. Our study forms the foundation to analyze the circuit mechanisms underlying preparatory activity in a genetically tractable model organism.

Empirical estimates of the dimensionality of neural population activity are often much lower than the population size. Similar phenomena are also observed in trained and designed neural network models. These experimental and computational results suggest that mapping low-dimensional dynamics to high-dimensional neural space is a common feature of cortical computation. Despite the ubiquity of this observation, the constraints arising from such mapping are poorly understood. Here we consider a specific example of mapping low-dimensional dynamics to high-dimensional neural activity-the neural engineering framework. We analytically solve the framework for the classic ring model-a neural network encoding a static or dynamic angular variable. Our results provide a complete characterization of the success and failure modes for this model. Based on similarities between this and other frameworks, we speculate that these results could apply to more general scenarios.

Hippocampal area CA3 is thought to play a central role in memory formation and retrieval. Although various network mechanisms have been hypothesized to mediate these computations, direct evidence is lacking. Using intracellular membrane potential recordings from CA3 neurons and optogenetic manipulations in behaving mice we found that place field activity is produced by a symmetric form of Behavioral Timescale Synaptic Plasticity (BTSP) at recurrent synaptic connections among CA3 principal neurons but not at synapses from the dentate gyrus (DG). Additional manipulations revealed that excitatory input from the entorhinal cortex (EC) but not DG was required to update place cell activity based on the animal’s movement. These data were captured by a computational model that used BTSP and an external updating input to produce attractor dynamics under online learning conditions. Additional theoretical results demonstrate the enhanced memory storage capacity of such networks, particularly in the face of correlated input patterns. The evidence sheds light on the cellular and circuit mechanisms of learning and memory formation in the hippocampus.

The dilemma that neurotheorists face is that (1) detailed biophysical models that can be constrained by direct measurements, while being of great importance, offer no immediate insights into cognitive processes in the brain, and (2) high-level abstract cognitive models, on the other hand, while relevant for understanding behavior, are largely detached from neuronal processes and typically have many free, experimentally unconstrained parameters that have to be tuned to a particular data set and, hence, cannot be readily generalized to other experimental paradigms. In this contribution, we propose a set of "first principles" for neurally inspired cognitive modeling of memory retrieval that has no biologically unconstrained parameters and can be analyzed mathematically both at neuronal and cognitive levels. We apply this framework to the classical cognitive paradigm of free recall. We show that the resulting model accounts well for puzzling behavioral data on human participants and makes predictions that could potentially be tested with neurophysiological recording techniques.

The brain plans and executes volitional movements. The underlying patterns of neural population activity have been explored in the context of movements of the eyes, limbs, tongue, and head in nonhuman primates and rodents. How do networks of neurons produce the slow neural dynamics that prepare specific movements and the fast dynamics that ultimately initiate these movements? Recent work exploits rapid and calibrated perturbations of neural activity to test specific dynamical systems models that are capable of producing the observed neural activity. These joint experimental and computational studies show that cortical dynamics during motor planning reflect fixed points of neural activity (attractors). Subcortical control signals reshape and move attractors over multiple timescales, causing commitment to specific actions and rapid transitions to movement execution. Experiments in rodents are beginning to reveal how these algorithms are implemented at the level of brain-wide neural circuits. Expected final online publication date for the , Volume 45 is July 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

The brain plans and executes volitional movements. The underlying patterns of neural population activity have been explored in the context of movements of the eyes, limbs, tongue, and head in nonhuman primates and rodents. How do networks of neurons produce the slow neural dynamics that prepare specific movements and the fast dynamics that ultimately initiate these movements? Recent work exploits rapid and calibrated perturbations of neural activity to test specific dynamical systems models that are capable of producing the observed neural activity. These joint experimental and computational studies show that cortical dynamics during motor planning reflect fixed points of neural activity (attractors). Subcortical control signals reshape and move attractors over multiple timescales, causing commitment to specific actions and rapid transitions to movement execution. Experiments in rodents are beginning to reveal how these algorithms are implemented at the level of brain-wide neural circuits.

Human memory can store large amount of information. Nevertheless, recalling is often a challenging task. In a classical free recall paradigm, where participants are asked to repeat a briefly presented list of words, people make mistakes for lists as short as 5 words. We present a model for memory retrieval based on a Hopfield neural network where transition between items are determined by similarities in their long-term memory representations. Meanfield analysis of the model reveals stable states of the network corresponding (1) to single memory representations and (2) intersection between memory representations. We show that oscillating feedback inhibition in the presence of noise induces transitions between these states triggering the retrieval of different memories. The network dynamics qualitatively predicts the distribution of time intervals required to recall new memory items observed in experiments. It shows that items having larger number of neurons in their representation are statistically easier to recall and reveals possible bottlenecks in our ability of retrieving memories. Overall, we propose a neural network model of information retrieval broadly compatible with experimental observations and is consistent with our recent graphical model (Romani et al., 2013).