Temporal pattern separation in hippocampal neurons through multiplexed neural codes

Abstract

Pattern separation is a central concept in current theories of episodic memory: this computation is thought to support our ability to avoid confusion between similar memories by transforming similar cortical input patterns of neural activity into dissimilar output patterns before their long-term storage in the hippocampus. Because there are many ways one can define patterns of neuronal activity and the similarity between them, pattern separation could in theory be achieved through multiple coding strategies. Using our recently developed assay that evaluates pattern separation in isolated tissue by controlling and recording the input and output spike trains of single hippocampal neurons, we explored neural codes through which pattern separation is performed by systematic testing of different similarity metrics and various time resolutions. We discovered that granule cells, the projection neurons of the dentate gyrus, can exhibit both pattern separation and its opposite computation, pattern convergence, depending on the neural code considered and the statistical structure of the input patterns. Pattern separation is favored when inputs are highly similar, and is achieved through spike time reorganization at short time scales (< 100 ms) as well as through variations in firing rate and burstiness at longer time scales. These multiplexed forms of pattern separation are network phenomena, notably controlled by GABAergic inhibition, that involve many celltypes with input-output transformations that participate in pattern separation to different extents and with complementary neural codes: a rate code for dentate fast-spiking interneurons, a burstiness code for hilar mossy cells and a synchrony code at long time scales for CA3 pyramidal cells. Therefore, the isolated hippocampal circuit itself is capable of performing temporal pattern separation using multiplexed coding strategies that might be essential to optimally disambiguate multimodal mnemonic representations.

Deficits in Behavioral and Neuronal Pattern Separation in Temporal Lobe Epilepsy

“…Patch pipettes were filled with an intracellular solution of the following composition (in mM): 135 K-gluconate, 5 KCl, 0.1 EGTA, 10 HEPES, 20 Na-Phosphocreatine, 2 Mg 2 -ATP, 0.3 Na-GTP, 0.25 CaCl 2 adjusted to pH 7.3 with KOH and 310 mOsm with H 2 O, leading to a 2-5 MΩ pipette resistance in aCSF. Whole-cell patch-clamp recordings of single DG GCs in response to electric stimulation of the outer molecular layer (perforant path) were performed as detailed in Madar et al (2019a) (see Figure 4A ), and the stimulation protocols used to test neural pattern separation were the same as in Madar et al(2019b) (see Figure 4B and 5 ). Briefly, input sets used for stimulation were composed of five (type 1) or ten (type 2 and 3) spiketrains (two seconds long), delivered sequentially (separated by five seconds of pause) and repeated ten or five times, respectively, in order to yield fifty output spiketrains.…”

“…Similarity between spiketrains was assessed as in Madar et al (2019a, b), using three metrics based on dividing spiketrains into time bins of a specific duration τ w : the Pearson’s correlation coefficient (R), the normalized dot product (NDP) and the scaling factor (SF). NDP and SF consider spiketrains as vectors of spike count per bin and measure the angle (NDP is the cosine) and the ratio of norms (SF) between the two vectors.…”

Quantitative properties of a feedback circuit predict frequency-dependent pattern separation

“…6E, inset). We found a significant effect of both inhibitory motif and frequency domain It has recently been emphasized, that the assessment of pattern separation can depend critically on the pattern similarity measure used 8,58 . Therefore we tested the robustness of this result for two alternative similarity measures, namely normalized dot product (NDP, also known as cosine similarity) and pattern overlap (# of coactive/ # of totally active cells; Supplementary Fig.…”

“…However, many neural computations are likely to occur on shorter timescales, such as within individual theta cycles (~100 ms) 19,59 . Indeed, the time window in which correlation is recorded can nontrivially affect the resulting correlation, depending on the timing of spikes within it 58 . We therefore computed the networks pattern separation ability within 100 ms time windows, revealing i) the pattern separation ability within such short timescales and ii) the temporal evolution of pattern separation throughout a 600 ms stimulus presentation ( Fig.…”

Distinct cellular mediators drive the Janus Faces of Toll-like Receptor 4 regulation of network excitability which impacts working memory performance after brain Injury

“…Thus, it is reasonable to propose that the changes in excitability drive underlie the effects on memory function. Indeed, in controls, TLR4 antagonists enhanced excitability which would be expected to compromise sparse neuronal activity and impair working memory performance (Madar et al, 2019;Scharfman and Bernstein, 2015;Wixted et al, 2014). In contrast, when neuronal excitability is increased following brain injury, TLR4 antagonists reduced excitability, potentially aiding in maintaining sparse neuronal activity, which improved working memory.…”