Cognitive maps as expectations learned across episodes – a model of the two dentate gyrus blades
How can the hippocampal system transition from episodic one-shot learning to a multi-shot learning regime and what is the utility of the resultant neural representations? This talk will explore the role of the dentate gyrus (DG) anatomy in this context. The canonical DG model suggests it performs pattern separation. More recent experimental results challenge this standard model, suggesting DG function is more complex and also supports the precise binding of objects and events to space and the integration of information across episodes. Very recent studies attribute pattern separation and pattern integration to anatomically distinct parts of the DG (the suprapyramidal blade vs the infrapyramidal blade). We propose a computational model that investigates this distinction. In the model the two processing streams (potentially localized in separate blades) contribute to the storage of distinct episodic memories, and the integration of information across episodes, respectively. The latter forms generalized expectations across episodes, eventually forming a cognitive map. We train the model with two data sets, MNIST and plausible entorhinal cortex inputs. The comparison between the two streams allows for the calculation of a prediction error, which can drive the storage of poorly predicted memories and the forgetting of well-predicted memories. We suggest that differential processing across the DG aids in the iterative construction of spatial cognitive maps to serve the generation of location-dependent expectations, while at the same time preserving episodic memory traces of idiosyncratic events.
Epilepsy, memory and pattern separation in the dentate gyrus
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Neural Circuit Mechanisms of Pattern Separation in the Dentate Gyrus
The ability to discriminate different sensory patterns by disentangling their neural representations is an important property of neural networks. While a variety of learning rules are known to be highly effective at fine-tuning synapses to achieve this, less is known about how different cell types in the brain can facilitate this process by providing architectural priors that bias the network towards sparse, selective, and discriminable representations. We studied this by simulating a neuronal network modelled on the dentate gyrus—an area characterised by sparse activity associated with pattern separation in spatial memory tasks. To test the contribution of different cell types to these functions, we presented the model with a wide dynamic range of input patterns and systematically added or removed different circuit elements. We found that recruiting feedback inhibition indirectly via recurrent excitatory neurons proved particularly helpful in disentangling patterns, and show that simple alignment principles for excitatory and inhibitory connections are a highly effective strategy.
Converging mechanisms of epileptogenesis after brain injury
Traumatic brain injury (TBI), a leading cause of acquired epilepsy, results in primary cellular injury as well as secondary neurophysiological and inflammatory responses which contribute to epileptogenesis. I will present our recent studies identifying a role for neuro-immune interactions, specifically, the innate immune receptor Toll-like receptor 4 (TLR4), in enhancing network excitability and cell loss in hippocampal dentate gyrus early after concussive brain injury. I will describe results indicating that the transient post-traumatic increases in dentate neurogenesis which occurs during the same early post-injury period augments dentate network excitability and epileptogenesis. I will provide evidence for the beneficial effects of targeting TLR4 and neurogenesis early after brain injury in limiting epileptogenesis. We will discuss potential mechanisms for convergence of the post-traumatic neuro-immune and neurogenic changes and the implications for therapies to reduce neurological deficits and epilepsy after brain injury.
Capacitance clamp - artificial capacitance in biological neurons via dynamic clamp
A basic time scale in neural dynamics from single cells to the network level is the membrane time constant - set by a neuron’s input resistance and its capacitance. Interestingly, the membrane capacitance appears to be more dynamic than previously assumed with implications for neural function and pathology. Indeed, altered membrane capacitance has been observed in reaction to physiological changes like neural swelling, but also in ageing and Alzheimer's disease. Importantly, according to theory, even small changes of the capacitance can affect neuronal signal processing, e.g. increase network synchronization or facilitate transmission of high frequencies. In experiment, robust methods to modify the capacitance of a neuron have been missing. Here, we present the capacitance clamp - an electrophysiological method for capacitance control based on an unconventional application of the dynamic clamp. In its original form, dynamic clamp mimics additional synaptic or ionic conductances by injecting their respective currents. Whereas a conductance directly governs a current, the membrane capacitance determines how fast the voltage responds to a current. Accordingly, capacitance clamp mimics an altered capacitance by injecting a dynamic current that slows down or speeds up the voltage response (Fig 1 A). For the required dynamic current, the experimenter only has to specify the original cell and the desired target capacitance. In particular, capacitance clamp requires no detailed model of present conductances and thus can be applied in every excitable cell. To validate the capacitance clamp, we performed numerical simulations of the protocol and applied it to modify the capacitance of cultured neurons. First, we simulated capacitance clamp in conductance based neuron models and analysed impedance and firing frequency to verify the altered capacitance. Second, in dentate gyrus granule cells from rats, we could reliably control the capacitance in a range of 75 to 200% of the original capacitance and observed pronounced changes in the shape of the action potentials: increasing the capacitance reduced after-hyperpolarization amplitudes and slowed down repolarization. To conclude, we present a novel tool for electrophysiology: the capacitance clamp provides reliable control over the capacitance of a neuron and thereby opens a new way to study the temporal dynamics of excitable cells.
Interneuron desynchronization and breakdown of long-term place cell stability in temporal lobe epilepsy
Temporal lobe epilepsy is associated with memory deficits but the circuit mechanisms underlying these cognitive disabilities are not understood. We used electrophysiological recordings, open-source wire-free miniaturized microscopy and computational modeling to probe these deficits in a model of temporal lobe epilepsy. We find desynchronization of dentate gyrus interneurons with CA1 interneurons during theta oscillations and a loss of precision and stability of place fields. We also find that emergence of place cell dysfunction is delayed, providing a potential temporal window for treatments. Computation modeling shows that desynchronization rather than interneuron cell loss can drive place cell dysfunction. Future studies will uncover cell types driving these changes and transcriptional changes that may be driving dysfunction.