Tam, W. K. | Search bioRxiv

bioRxiv2022

pyNeurode: a real-time neural signal processing framework

Accurate decoding of neural signals often requires assigning extracellular waveforms acquired on the same electrode to their originating neurons, a process known as spike sorting. While many offline sorters are available, accurate online sorting of spikes with many channels is still a challenging problem. Existing online sorters either use simple algorithms with low accuracy, can only process a handful of channels, or depend on a complex runtime environment that is difficult to set up. We have developed a state-of-the-art online spike sorting platform in Python that enables large-scale, fully automatic real-time spike sorting and decoding on hundreds of channels. Our system is cross-platform and works seamlessly with the Open Ephys suite of open-source hardware and software widely used in many neuroscience laboratories worldwide. It also comes with a user-friendly graphical user interface to monitor the cluster quality, spike waveforms and neuronal firing rate. Our platform has comparable accuracy to offline sorters and can achieve an end-to-end sorting latency of around 160 ms for 128-channel signals. It will be useful for research in fundamental neuroscience, closed-loop feedback neuromodulation and brain-computer interfaces.

neuroscience↗

bioRxiv2021

Analogue representation of a spatial memory by ramp-like neural activity in retrohippocampal cortex

Neurons in the retrohippocampal cortices play crucial roles in spatial memory. Many retrohippocampal neurons have firing fields that are selectively active at specific locations, with memory for rewarded locations associated with reorganisation of these firing fields. Whether this is the sole strategy for representing spatial memories is unclear. Here, we demonstrate that during a spatial memory task retrohippocampal neurons encode location through ramping activity that extends within segments of a linear track approaching and following a reward, with the rewarded location represented by offsets or switches in the slope of the ramping activity. These ramping representations could be maintained independently of trial outcome and cues that mark the reward location, indicating that they result from recall of the track structure. During recordings in an open arena, neurons that generated ramping activity during the spatial memory task were more numerous than grid or border cells, with a majority showing spatial firing that did not meet criteria for classification as grid or border representations. Encoding of rewarded locations through offsets and switches in the slope of ramping activity also emerged in recurrent neural networks trained to solve a similar location memory task. Impaired performance of these networks following disruption of outputs from ramping neurons is consistent with this coding strategy supporting navigation to recalled locations of behavioural significance. We hypothesise that retrohippocampal ramping activity mediates readout of learned models for goal-directed navigation.

neuroscience↗