Bachelor of Science, University of New Brunswick (2010)
Doctor of Philosophy, University of Ottawa (2017)
Cellular and network mechanisms may generate sparse coding of sequential object encounters in hippocampal-like circuits.
The localization of distinct landmarks plays a crucial role in encoding new spatial memories. In mammals, this function is performed by hippocampal neurons that sparsely encode an animal's location relative to surrounding objects. Similarly, the dorsal lateral pallium (DL) is essential for spatial learning in teleost fish. The DL of weakly electric gymnotiform fish receives both electrosensory and visual input from the preglomerular nucleus (PG), which has been hypothesized to encode the temporal sequence of electrosensory or visual landmark/food encounters. Here, we show that DL neurons in the Apteronotid fish and in the Carassius auratus (goldfish) have a hyperpolarized resting membrane potential combined with a high and dynamic spike threshold that increases following each spike. Current-evoked spikes in DL cells are followed by a strong small-conductance calcium-activated potassium channel (SK) mediated after-hyperpolarizing potential (AHP). Together, these properties prevent high frequency and continuous spiking. The resulting sparseness of discharge and dynamic threshold suggest that DL neurons meet theoretical requirements for generating spatial memory engrams by decoding the landmark/food encounter sequences encoded by PG neurons. Thus, DL neurons in teleost fish may provide a promising, simple system to study the core cell and network mechanisms underlying spatial memory.Significance Statement To our knowledge, this is first study of the intrinsic physiology of teleost pallial (DL) neurons. Their biophysical properties demonstrate that DL neurons are sparse coders with a dynamic spike threshold leading us to suggest that they can transform time-stamped input into spatial location during navigation. The concept of local attractors (bumps) that potentially move 'across' local recurrent networks has been prominent in the neuroscience theory literature. We propose that the relatively simple and experimentally accessible DL of teleosts may be the best preparation to examine this idea experimentally and to investigate the properties of local (excitatory) recurrent networks whose cells are endowed with, e.g., slow spike threshold adaptation dynamics.
View details for DOI 10.1523/ENEURO.0108-19.2019
View details for PubMedID 31324676
- Analog Signaling With the "Digital" Molecular Switch CaMKII FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2018; 12
Analog Signaling With the "Digital" Molecular Switch CaMKII.
Frontiers in computational neuroscience
2018; 12: 92
Molecular switches, such as the protein kinase CaMKII, play a fundamental role in cell signaling by decoding inputs into either high or low states of activity; because the high activation state can be turned on and persist after the input ceases, these switches have earned a reputation as "digital." Although this on/off, binary perspective has been valuable for understanding long timescale synaptic plasticity, accumulating experimental evidence suggests that the CaMKII switch can also control plasticity on short timescales. To investigate this idea further, a non-autonomous, nonlinear ordinary differential equation, representative of a general bistable molecular switch, is analyzed. The results suggest that switch activity in regions surrounding either the high- or low-stable states of activation could act as a reliable analog signal, whose short timescale fluctuations relative to equilibrium track instantaneous input frequency. The model makes intriguing predictions and is validated against previous work demonstrating its suitability as a minimal representation of switch dynamics; in combination with existing experimental evidence, the theory suggests a multiplexed encoding of instantaneous frequency information over short timescales, with integration of total activity over longer timescales.
View details for PubMedID 30524260
View details for PubMedCentralID PMC6262075