Techniques for extracting single-trial activity patterns from large-scale neural recordings

Abstract

SummaryLarge, chronically-implanted arrays of microelectrodes are an increasingly common tool for recording from primate cortex, and can provide extracellular recordings from many (order of 100) neurons. While the desire for cortically-based motor prostheses has helped drive their development, such arrays also offer great potential to advance basic neuroscience research. Here we discuss the utility of array recording for the study of neural dynamics. Neural activity often has dynamics beyond that driven directly by the stimulus. While governed by those dynamics, neural responses may nevertheless unfold differently for nominally identical trials, rendering many traditional analysis methods ineffective. We review recent studies -some employing simultaneous recording, some not -indicating that such variability is indeed present both during movement generation, and during the preceding premotor computations. In such cases, large-scale simultaneous recordings have the potential to provide an unprecedented view of neural dynamics at the level of single trials. However, this enterprise will depend not only on techniques for simultaneous recording, but also on the use and further development of analysis techniques that can appropriately reduce the dimensionality of the data, and allow visualization of single-trial neural behavior.

Towards a computational theory of experience

“…One of the crucial aspects in assigning activity a formal role in explaining phenomenal experience is whether activity is construed in the classical sense of dynamical systems (i.e., as the instantaneous state of the system), or as a process extended in time, that is, as a trajectory in the system's instantaneous state space (Mazor and Laurent, 2005;Churchland et al, 2007;Geffen et al, 2009;Yu et al, 2009;Churchland et al, 2010a,b). To observe this distinction, we will refer either to instantaneous activity space or to activity trajectory space where the distinction is called for, and will otherwise use the non-committal expression -activity space.‖ The functional difference between instantaneous states and trajectories will be the subject matter of section 4 and parts of section 5 below.…”

“…However, at several points of the discussion a positive alternative seemed to emerge -namely, construing activity (as well as experience) as a dynamic process eliminates some of the quandaries that beset theories based on instantaneous states (cf., Spivey, 2007). 9 We now develop this line of argument by describing how construing activity as trajectories in the system's instantaneous state space (Mazor and Laurent, 2005;Churchland et al, 2007;Geffen et al, 2009;Yu et al, 2009;Churchland et al, 2010a,b) 10 is a natural starting point for formulating a computational theory of experience.…”

Embedding Multiple Trajectories in Simulated Recurrent Neural Networks in a Self-Organizing Manner

“…First, the generation of complex spatiotemporal patterns of action potentials that underlie motor behavior is assumed to rely on the recurrent nature of motor and premotor cortical circuits (Wessberg et al, 2000;Hahnloser et al, 2002;Churchland et al, 2007;Long and Fee, 2008). Second, it has been proposed that many forms of sensory processing rely on the interaction between incoming stimuli and the internal state of recurrent networks (Mauk and Buonomano, 2004;Durstewitz and Deco, 2008;Rabinovich et al, 2008;Buonomano and Maass, 2009).…”

“…Rich dynamical behaviors, in the form of spatiotemporal patterns of neuronal spikes are observed in vitro (Beggs and Plenz, 2003;Shu et al, 2003;Johnson and Buonomano, 2007) and in vivo (Wessberg et al, 2000;Churchland et al, 2007;Pastalkova et al, 2008), and have been shown to code information about sensory inputs (Laurent, 2002;Broome et al, 2006), motor behaviors (Wessberg et al, 2000;Hahnloser et al, 2002), as well as memory and planning (Euston et al, 2007;Pastalkova et al, 2008). Although it is clear that the neural dynamics that emerges as a result of the recurrent architecture of cortical networks is fundamental to brain function, relatively little is known about how recurrent networks are set up in a manner that support computations, yet avoid pathological states, including runaway excitation and epileptic activity.…”

Information-theoretic metric learning: 2-D linear projections of neural data for visualization

“…A number of unsupervised methods [2] have been explored to analyze the similarity between trials and the evolution of the neural response during trials. Here we have the goal of finding a low-dimensional representation for visualization that preserves similarities among conditions.…”