Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model's feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison

Size matters: How scaling affects the interaction between grid and border cells

Many hippocampal cell types are characterized by a progressive increase in scale along the dorsal-to-ventral axis, such as in the cases of head-direction, grid and place cells. Also located in the medial entorhinal cortex (MEC), border cells would be expected to benefit from such scale modulations. However, this phenomenon has not been experimentally observed. Grid cells in the MEC of mammals integrate velocity related signals to map the environment with characteristic hexagonal tessellation patterns. Due to the noisy nature of these input signals, path integration processes tend to accumulate errors as animals explore the environment, leading to a loss of grid-like activity. It has been suggested that border-to-grid cells' associations minimize the accumulated grid cells' error when rodents explore enclosures. Thus, the border-grid interaction for error minimization is a suitable scenario to study the effects of border cell scaling within the context of spatial representation. In this study, we computationally address the question of (i) border cells' scale from the perspective of their role in maintaining the regularity of grid cells' firing fields, as well as (ii) what are the underlying mechanisms of grid-border associations relative to the scales of both grid and border cells. Our results suggest that for optimal contribution to grid cells' error minimization, border cells should express smaller firing fields relative to those of the associated grid cells, which is consistent with the hypothesis of border cells functioning as spatial anchoring signals.The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement no (341196) cDAC. H2020-SocSMC (socSMC-641321H2020-FETPROACT- 2014)

Spatially segregated feedforward and feedback neurons support differential odor processing in the lateral entorhinal cortex

The lateral entorhinal cortex (LEC) computes and transfers olfactory information from the olfactory bulb to the hippocampus. Here we established LEC connectivity to upstream and downstream brain regions to understand how the LEC processes olfactory information. We report that, in layer II (LII), reelin- and calbindin-positive (RE(+) and CB(+)) neurons constitute two major excitatory cell types that are electrophysiologically distinct and differentially connected. RE(+) neurons convey information to the hippocampus, while CB(+) neurons project to the olfactory cortex and the olfactory bulb. In vivo calcium imaging revealed that RE(+) neurons responded with higher selectivity to specific odors than CB(+) neurons and GABAergic neurons. At the population level, odor discrimination was significantly better for RE(+) than CB(+) neurons, and was lowest for GABAergic neurons. Thus, we identified in LII of the LEC anatomically and functionally distinct neuronal subpopulations that engage differentially in feedforward and feedback signaling during odor processing