Abstract
Multiple brain regions are able to learn and express temporal sequences, and this functionality is an essential component of learning and memory. We propose a substrate for such representations via a network model that learns and recalls discrete sequences of variable order and duration. The model consists of a network of spiking neurons placed in a modular microcolumn based architecture. Learning is performed via a biophysically realistic learning rule that depends on synaptic 'eligibility traces'. Before training, the network contains no memory of any particular sequence. After training, presentation of only the first element in that sequence is sufficient for the network to recall an entire learned representation of the sequence. An extended version of the model also demonstrates the ability to successfully learn and recall non-Markovian sequences. This model provides a possible framework for biologically plausible sequence learning and memory, in agreement with recent experimental results.
Highlights
Long as time flows in one direction, nature itself is fundamentally sequential
A model for sequence learning based on modular architecture and eligibility trace learning
While the results shown in this work were obtained using a two-trace learning (TTL) rule, the network can be trained with a learning rule based on a single trace (Gavornik et al, 2009; Gavornik and Shouval, 2011), and the results are similar to those demonstrated here
Summary
Long as time flows in one direction, nature itself is fundamentally sequential. To operate in this reality, the brain needs to think, plan, and take action in a temporally ordered fashion. When you sing a song, hit a baseball, or even utter a word, you are engaging in sequential activity. You are engaging in sequential recall of a learned activity – your actions have ’a’ temporal order and duration but ’the’ temporal order and duration which you learned. Recent evidence has shown that such learned representations can exist in cortical circuits (Gavornik and Bear, 2014; Xu et al, 2012; Cooke et al, 2015; Eagleman and Dragoi, 2012; Yin et al, 2008), begging the question: through what sort of circuits and learning paradigms can these representations arise?
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