The race to learn: Spike timing and STDP can coordinate learning and recall in CA3

Abstract

The CA3 region of the hippocampus has long been proposed as an autoassociative network performing pattern completion on known inputs. The dentate gyrus (DG) region is often proposed as a network performing the complementary function of pattern separation. Neural models of pattern completion and separation generally designate explicit learning phases to encode new information and assume an ideal fixed threshold at which to stop learning new patterns and begin recalling known patterns. Memory systems are significantly more complex in practice, with the degree of memory recall depending on context-specific goals. Here, we present our spike-timing separation and completion (STSC) model of the entorhinal cortex (EC), DG, and CA3 network, ascribing to each region a role similar to that in existing models but adding a temporal dimension by using a spiking neural network. Simulation results demonstrate that (a) spike-timing dependent plasticity in the EC-CA3 synapses provides a pattern completion ability without recurrent CA3 connections, (b) the race between activation of CA3 cells via EC-CA3 synapses and activation of the same cells via DG-CA3 synapses distinguishes novel from known inputs, and (c) modulation of the EC-CA3 synapses adjusts the learned versus test input similarity required to evoke a direct CA3 response prior to any DG activity, thereby adjusting the pattern completion threshold. These mechanisms suggest that spike timing can arbitrate between learning and recall based on the novelty of each individual input, ensuring control of the learn-recall decision resides in the same subsystem as the learned memories themselves. The proposed modulatory signal does not override this decision but biases the system toward either learning or recall. The model provides an explanation for empirical observations that a reduction in novelty produces a corresponding reduction in the latency of responses in CA3 and CA1.

Why Variability Facilitates Spinal Learning

“…Signal noise may improve the learning by allowing the enhancement of those synaptic connections that occur in a spike timing dependent plasticity (STDP) manner. This synaptic modification is based on the concept that neuronal synapses are either strengthened or weakened depending on the temporal order of synaptic firing (Song et al, 2000; Taylor et al, 2009; Nolan et al, 2010). Theoretically by repeating a motor task such as stepping STDP reinforces or weakens selected sensorimotor synapses and results in a neural network functioning with less variability.…”

Brain-inspired sensor fusion for navigating robots

“…We will implement a full neural network model of the weighting algorithms. The conversion to a neural rather than mathematical model will provide access to beneficial neural computational processes such as "The Race to Learn", a process by which new sensory input is gradually learnt in a robust manner by a neural loop called the trisynaptic loop [27]. Such a process may provide a neurally-based method of implementing the autonomous threshold adjustment discussed previously.…”

“…Underpinning the capability of rats to flourish in such varied environments are a range of sophisticated neural functions that are performed in a brain region called the entorhinal-hippocampal formation, which consists of the entorhinal cortex (EC) and hippocampus [27]. These brain regions are thought to be involved in a number of critical functions; sensor fusion, variable pattern discrimination to suit perceptually distinctive or bland environments and detection of inconsistent sensory input through pattern completion.…”

Sensory Feedback, Error Correction, and Remapping in a Multiple Oscillator Model of Place-Cell Activity

“…We showed that one way to encode the phase target is by locking the stimulus timing to the phase target (Figure 9). While we did not model the learning process that may perform this phase encoding, previous models have demonstrated encoding and recall in theta-modulated networks (Jensen et al, 1996; Wallenstein and Hasselmo, 1997; Hasselmo et al, 2002; Igarashi et al, 2007) using timing-based learning rules such as STDP (Lengyel et al, 2005; Câteau et al, 2008; Nolan et al, 2010). Additionally, our demonstration of stimulus timing-based phase-code retrieval using a spiking oscillator showed that effective encoding may rely on producing a stimulus representation with a trailing hyperpolarization.…”