Predicting synchronous firing of large neural populations from sequential recordings

A major goal in neuroscience is to understand how populations of neurons code for stimuli or actions. While the number of neurons that can be recorded simultaneously is increasing at a fast pace, in most cases these recordings cannot access a complete population: some neurons that carry relevant information remain unrecorded. In particular, it is hard to simultaneously record all the neurons of the same type in a given area. Recent progress have made possible to profile each recorded neuron in a given area thanks to genetic and physiological tools, and to pool together recordings from neurons of the same type across different experimental sessions. However, it is unclear how to infer the activity of a full population of neurons of the same type from these sequential recordings. Neural networks exhibit collective behaviour, e.g. noise correlations and synchronous activity, that are not directly captured by a conditionally-independent model that would just put together the spike trains from sequential recordings. Here we show that we can infer the activity of a full population of retina ganglion cells from sequential recordings, using a novel method based on copula distributions and maximum entropy modeling. From just the spiking response of each ganglion cell to a repeated stimulus, and a few pairwise recordings, we could predict the noise correlations using copulas, and then the full activity of a large population of ganglion cells of the same type using maximum entropy modeling. Remarkably, we could generalize to predict the population responses to different stimuli with similar light conditions and even to different experiments. We could therefore use our method to construct a very large population merging cells’ responses from different experiments. We predicted that synchronous activity in ganglion cell populations saturates only for patches larger than 1.5mm in radius, beyond what is today experimentally accessible.

Persistence of neuronal representations through time and damage in the hippocampus

How do neurons encode long-term memories? Bilateral imaging of neuronal activity in the mouse hippocampus reveals that, from one day to the next, ~40% of neurons change their responsiveness to cues, but thereafter only 1% of cells change per day. Despite these changes, neuronal responses are resilient to a lack of exposure to a previously completed task or to hippocampus lesions. Unlike individual neurons, the responses of which change after a few days, groups of neurons with inter- and intrahemispheric synchronous activity show stable responses for several weeks. The likelihood that a neuron maintains its responsiveness across days is proportional to the number of neurons with which its activity is synchronous. Information stored in individual neurons is relatively labile, but it can be reliably stored in networks of synchronously active neurons.

From serial to parallel: predicting synchronous firing of large neural populations from sequential recordings

A major goal in neuroscience is to understand how populations of neurons code for stimuli or actions. While the number of neurons that can be recorded simultaneously is increasing at a fast pace, in most cases these recordings cannot access a complete population: some neurons that carry relevant information remain unrecorded. In particular, it is hard to simultaneously record all the neurons of the same type in a given area. Recent progress has made possible to determine the type of each recorded neuron in a given area thanks to genetic and physiological tools. However, it is unclear how to infer the activity of a full population of neurons of the same type from sequential recordings across different experiments. Neural networks exhibit collective behaviour, e.g. noise correlations and synchronous activity, that are not directly captured by a conditionally-independent model that would just pool together the spike trains from sequential recordings. Here we present a method to build population activity from single cell responses taken from sequential recordings, which only requires pairwise recordings to train the model. Our method combines copula distributions and maximum entropy modeling. After training, the model allows us to predict the activity of large populations using only sequential recordings of single cells. We applied this method to a population of ganglion cells, the retinal output, all belonging to the same type. From just the spiking response of each cell to a repeated stimulus, we could predict the full activity of the population. We could then generalize to predict the population responses to different stimuli and even to different experiments. As a result, we were able to use our approach to construct a synthetic model of a very large neuronal population, which uses data combined from multiple experiments. We then predicted the extent of synchronous activity and showed it grew with the number of neurons. This approach is a promising way to infer population activity from sequential recordings in sensory areas.

Persistence of neuronal representations through time and damage in the hippocampus

Memories can persist for decades but how they are stably encoded in individual and groups of neurons is not known. To investigate how a familiar environment is encoded in CA1 neurons over time we implanted bilateral microendoscopes in transgenic mice to image the activity of pyramidal neurons in the hippocampus over weeks. Most of the neurons (90 %) are active every day, however, the response of neurons to specific cues changes across days. Approximately 40 % of place and time cells lose fields between two days; however, on timescales longer than two days the neuronal pattern changes at a rate of 1 % for each additional day. Despite continuous changes, field responses are more resilient, with place/time cells recovering their fields after a 10-day period of no task or following CA1 damage. Recovery of these neuronal patterns is characterized by transient changes in firing fields which ultimately converge to the original representation. Unlike individual neurons, groups of neurons with inter and intrahemispheric synchronous activity form stable place and time fields across days. Neurons whose activity was synchronous with a large group of neurons were more likely to preserve their responses to place or time across multiple days. These results support the view that although task-relevant information stored in individual neurons is relatively labile, it can persist in networks of neurons with synchronized activity spanning both hemispheres.One Sentence SummaryNeuronal representations in networks of neurons with synchronized activity are stable over weeks, even after lack of training or following damage.

Learning Time Patterns

This chapter is important in that it lays a foundation for claims throughout this book that entrainment serves a platform for learning. In this chapter, this idea is developed in the context of learning categories of meter (e.g., duple meter vs. triple meter). The key difference is that entrainment depends on coupling parameters supplied by external driving rhythm force, whereas learning depends on a binding parameter which is strengthened simply by repeated synchronous activity of two or more oscillations. Against a backdrop of evidence indicating that musicians especially possess skill in recognizing metric categories, this chapter develops the coupling–binding distinction with the aim of showing that what people learn when exposed to metrical time patterns are global attractors instilled by learning a variety of different instances in a given metric category.