Learning multiple variable-speed sequences in striatum via cortical tutoring

Abstract

Sparse, sequential patterns of neural activity have been observed in numerous brain areas during timekeeping and motor sequence tasks. Inspired by such observations, we construct a model of the striatum, an all-inhibitory circuit where sequential activity patterns are prominent, addressing the following key challenges: (i) obtaining control over temporal rescaling of the sequence speed, with the ability to generalize to new speeds; (ii) facilitating flexible expression of distinct sequences via selective activation, concatenation, and recycling of specific subsequences; and (iii) enabling the biologically plausible learning of sequences, consistent with the decoupling of learning and execution suggested by lesion studies showing that cortical circuits are necessary for learning, but that subcortical circuits are sufficient to drive learned behaviors. The same mechanisms that we describe can also be applied to circuits with both excitatory and inhibitory populations, and hence may underlie general features of sequential neural activity pattern generation in the brain.DOI: http://dx.doi.org/10.7554/eLife.26084.001

Learning spatiotemporal signals using a recurrent spiking network that discretizes time

“…The read-out neurons learn spike sequences in a supervised manner, and the supervisor sequence is encoded into the read-out weight matrix. Bringing together elements from different studies [18,22,32,37], our model exploits a clock-like dynamics encoded in the RNN to learn a mapping to read-out neurons so as to perform the computational task of learning and replaying spatiotemporal sequences. We illustrated the application of our scheme on a simple non-Markovian state transition sequence, a combination of two such simple sequences, and a time series with more complex dynamics from bird singing.…”

“…Another example is sequential dynamics, where longer time scales are obtained by clusters of neurons that activate each other in a sequence. This sequential dynamics can emerge by a specific connectivity in the excitatory neurons [16,17] or in the inhibitory neurons [18,19]. However, it is unclear how the brain learns these dynamics, as most of the current approaches use non biologically plausible ways to set or "train" the synaptic weights.…”

Flexible sensorimotor computations through rapid reconfiguration of cortical dynamics

“…Third, although we assumed that recurrent interactions were fixed during our experiment, it is almost certain that synaptic plasticity plays a key role as the network learns to incorporate context-dependent inputs (Kleim et al 1998;Pascual-Leone et al 1995;Yang et al 2014;Xu et al 2009) . Finally, the persistent separation of neural trajectories observed in DMFC allowed for a dynamical account which did not require invocation of "hidden" network states to explain timing behavior (Buonomano & Merzenich 1995;Karmarkar & Buonomano 2007;Murray & Escola 2017) or contextual control (Stokes et al 2013) . However, it is possible that factors not measured by extracellular recording (e.g., short-term synaptic plasticity) contribute to both contextual control and timing behavior in RSG and similar tasks.…”

Expectation-induced modulation of metastable activity underlies faster coding of sensory stimuli

“…Network models with clustered architecture provide a parsimonious explanation for the state sequences that have been observed ubiquitously in alert mammalian cortex, during both task engagement 17,18,46,47 and inter-trial periods. 11,39,40 In addition, this type of models accounts for various physiological observations such as stimulus-induced reduction of trial-to-trial variability 11,14,15,48 , neural dimensionality 16 , and firing rate multistability 11 (see also 49,50 ). In particular, models with metastable attractors have been used to explain the state sequences observed in rodent gustatory cortex during taste processing and decision making 11,45,51 .…”