Inferring single-trial neural population dynamics using sequential auto-encoders

Neuroscience is experiencing a revolution in which simultaneous recording of thousands of neurons is revealing population dynamics that are not apparent from single-neuron responses. This structure is typically extracted from data averaged across many trials, but deeper understanding requires studying phenomena detected in single trials, which is challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. We introduce latent factor analysis via dynamical systems, a deep learning method to infer latent dynamics from single-trial neural spiking data. When applied to a variety of macaque and human motor cortical datasets, latent factor analysis via dynamical systems accurately predicts observed behavioral variables, extracts precise firing rate estimates of neural dynamics on single trials, infers perturbations to those dynamics that correlate with behavioral choices, and combines data from non-overlapping recording sessions spanning months to improve inference of underlying dynamics

Episodic sequence memory is supported by a theta-gamma phase code

The meaning we derive from our experiences is not a simple static extraction of the elements but is largely based on the order in which those elements occur. Models propose that sequence encoding is supported by interactions between high-and low-frequency oscillations, such that elements within an experience are represented by neural cell assemblies firing at higher frequencies (gamma) and sequential order is encoded by the specific timing of firing with respect to a lower frequency oscillation (theta). During episodic sequence memory formation in humans, we provide evidence that items in different sequence positions exhibit greater gamma power along distinct phases of a theta oscillation. Furthermore, this segregation is related to successful temporal order memory. Our results provide compelling evidence that memory for order, a core component of an episodic memory, capitalizes on the ubiquitous physiological mechanism of theta-gamma phase-amplitude coupling

A shared neural ensemble links distinct contextual memories encoded close in time

Recent studies suggest the hypothesis that a shared neural ensemble may link distinct memories encoded close in time(1–13). According to the memory allocation hypothesis(1,2), learning triggers a temporary increase in neuronal excitability(14–16) that biases the representation of a subsequent memory to the neuronal ensemble encoding the first memory, such that recall of one memory increases the likelihood of recalling the other memory. Accordingly, we report that the overlap between the hippocampal CA1 ensembles activated by two distinct contexts acquired within a day is higher than when they are separated by a week. Multiple convergent findings indicate that this overlap of neuronal ensembles links two contextual memories. First, fear paired with one context is transferred to a neutral context when the two are acquired within a day but not across a week. Second, the first memory strengthens the second memory within a day but not across a week. Older mice, known to have lower CA1 excitability(16,17), do not show the overlap between ensembles, the transfer of fear between contexts, or the strengthening of the second memory. Finally, in aged animals, increasing cellular excitability and activating a common ensemble of CA1 neurons during two distinct context exposures rescued the deficit in linking memories. Taken together, these findings demonstrate that contextual memories encoded close in time are linked by directing storage into overlapping ensembles. Alteration of these processes by aging could affect the temporal structure of memories, thus impairing efficient recall of related information