Anatoli Gorchetchnikov

Neuromodulation, theta rhythm and rat spatial navigation

Cholinergic and GABAergic innervation of the hippocampus plays an important role in human memory function and rat spatial navigation. Drugs which block acetylcholine receptors or enhance GABA receptor activation cause striking impairments in the encoding of new information. Lesions of the cholinergic innervation of the hippocampus reduce the amplitude of hippocampal theta rhythm and cause impairments in spatial navigation tasks, including the Morris water maze, eight-arm radial maze, spatial reversal and delayed alternation. Here, we review previous work on the role of cholinergic modulation in memory function, and we present a new model of the hippocampus and entorhinal cortex describing the interaction of these regions for goal-directed spatial navigation in behavioral tasks. These mechanisms require separate functional phases for: (1) encoding of pathways without interference from retrieval, and (2) retrieval of pathways for guiding selection of the next movement. We present analysis exploring how phasic changes in physiological variables during hippocampal theta rhythm could provide these different phases and enhance spatial navigation function.

Modeling goal-directed spatial navigation in the rat based on physiological data from the hippocampal formation

We investigated the importance of hippocampal theta oscillations and the significance of phase differences of theta modulation in the cortical regions that are involved in goal-directed spatial navigation. Our models used representations of entorhinal cortex layer III (ECIII), hippocampus and prefrontal cortex (PFC) to guide movements of a virtual rat in a virtual environment. The model encoded representations of the environment through long-term potentiation of excitatory recurrent connections between sequentially spiking place cells in ECIII and CA3. This encoding required buffering of place cell activity, which was achieved by a short-term memory (STM) in EC that was regulated by theta modulation and allowed synchronized reactivation with encoding phases in ECIII and CA3. Inhibition at a specific theta phase deactivated the oldest item in the buffer when new input was presented to a full STM buffer. A 180 degrees phase difference separated retrieval and encoding in ECIII and CA3, which enabled us to simulate data on theta phase precession of place cells. Retrieval of known paths was elicited in ECIII by input at the retrieval phase from PFC working memory for goal location, requiring strict theta phase relationships with PFC. Known locations adjacent to the virtual rat were retrieved in CA3. Together, input from ECIII and CA3 activated predictive spiking in cells in CA1 for the next desired place on a shortest path to a goal. Consistent with data, place cell activity in CA1 and CA3 showed smaller place fields than in ECIII.

Grid cell hexagonal patterns formed by fast self‐organized learning within entorhinal cortex

Grid cells in the dorsal segment of the medial entorhinal cortex (dMEC) show remarkable hexagonal activity patterns, at multiple spatial scales, during spatial navigation. It has previously been shown how a self‐organizing map can convert firing patterns across entorhinal grid cells into hippocampal place cells that are capable of representing much larger spatial scales. Can grid cell firing fields also arise during navigation through learning within a self‐organizing map? This article describes a simple and general mathematical property of the trigonometry of spatial navigation which favors hexagonal patterns. The article also develops a neural model that can learn to exploit this trigonometric relationship. This GRIDSmap self‐organizing map model converts path integration signals into hexagonal grid cell patterns of multiple scales. GRIDSmap creates only grid cell firing patterns with the observed hexagonal structure, predicts how these hexagonal patterns can be learned from experience, and can process biologically plausible neural input and output signals during navigation. These results support an emerging unified computational framework based on a hierarchy of self‐organizing maps for explaining how entorhinal‐hippocampal interactions support spatial navigation.

From Synapses to Circuitry: Using Memristive Memory to Explore the Electronic Brain

In a synchronous digital platform for building large cognitive models, memristive nanodevices form dense, resistive memories that can be placed close to conventional processing circuitry. Through adaptive transformations, the devices can interact with the world in real time.

Space, time and learning in the hippocampus: How fine spatial and temporal scales are expanded into population codes for behavioral control

The hippocampus participates in multiple functions, including spatial navigation, adaptive timing and declarative (notably, episodic) memory. How does it carry out these particular functions? The present article proposes that hippocampal spatial and temporal processing are carried out by parallel circuits within entorhinal cortex, dentate gyrus and CA3 that are variations of the same circuit design. In particular, interactions between these brain regions transform fine spatial and temporal scales into population codes that are capable of representing the much larger spatial and temporal scales that are needed to control adaptive behaviors. Previous models of adaptively timed learning propose how a spectrum of cells tuned to brief but different delays are combined and modulated by learning to create a population code for controlling goal-oriented behaviors that span hundreds of milliseconds or even seconds. Here it is proposed how projections from entorhinal grid cells can undergo a similar learning process to create hippocampal place cells that can cover a space of many meters that are needed to control navigational behaviors. The suggested homology between spatial and temporal processing may clarify how spatial and temporal information may be integrated into an episodic memory. The model proposes how a path integration process activates a spatial map of grid cells. Path integration has a limited spatial capacity, and must be reset periodically, leading to the observed grid cell periodicity. Integration-to-map transformations have been proposed to exist in other brain systems. These include cortical mechanisms for numerical representation in the parietal cortex. As in the grid-to-place cell spatial expansion, the analog representation of number is extended by additional mechanisms to represent much larger numbers. The model also suggests how visual landmarks may influence grid cell activities via feedback projections from hippocampal place cells to the entorhinal cortex.

Switching between gamma and theta: Dynamic network control using subthreshold electric fields

We implemented an experimentally observed orthogonal arrangement of theta and gamma generation circuitry in septotemporal and lamellar dimensions is a two-dimensional model of hippocampus. The model includes three types of cells: pyramidal, basket, and oriens lacunosum-moleculare (OLM) neurons. In this reduced model, application of continuous electric fields allowed us to switch between theta, gamma and mixed theta-gamma regimes without additional pharmacological manipulation. Electric field effects on individual neurons were modeled based on experimental data. Network simulation results predict a flexible experimental technique, which would employ adaptive subthreshold electric fields to continuously modulate neuronal ensemble activity, and can be used for testing cognitive correlates of oscillatory rhythms as well as for suppressing epileptiform activity.

A biophysical implementation of a bidirectional graph search algorithm to solve multiple goal navigation tasks

The model presented here extends formal analysis (Hasselmo et al., Neural Networks, 15, pp. 689-707, 2002b) and abstract modelling (Gorchetchnikov and Hasselmo, Neurocomputing, 44-46, pp. 423-427, 2002a) of interactions within the hippocampal area (or other cortical areas), which can be flexibly used to navigate toward any arbitrary goal or multiple goals that change on a trial-by-trial basis. The algorithm is a version of a bidirectional breadth-first graph search implemented in simulated neurons using two flows of neural activity. The new model changes the continuous firing rate neuronal representations (Gorchetchnikov and Hasselmo 2002a) to more detailed compartmental versions with realistic parameters, while preserving the qualitative properties analysed previously (Hasselmo et al., 2002b, Gorchetchnikov and Hasselmo 2002a). The case of multiple goals being present in the environment is studied in this paper. The first set of simulations tests the algorithm in the selection of the closest goal. A small difference in distance between the simulated animal and different goals is sufficient for a correct selection. The second set of simulations studies the behaviour of the model when the goals have different saliences. A small salience-based difference between firing rates of the cells providing goal-related input to the model is sufficient for the selection of a more salient goal. This behaviour was tested in three types of environments: a linear track, a T-maze and an open field. Further investigation of quantitative properties of the model should allow it to handle cases when the exact location of the goal is uncertain.

Review and unification of learning framework in Cog Ex Machina platform for memristive neuromorphic hardware

Realizing adaptive brain functions subserving perception, cognition, and motor behavior on biological temporal and spatial scales remains out of reach for even the fastest computers. Newly introduced memristive hardware approaches open the opportunity to implement dense, low-power synaptic memories of up to 1015 bits per square centimeter. Memristors have the unique property of “remembering” the past history of their stimulation in their resistive state and do not require power to maintain their memory, making them ideal candidates to implement large arrays of plastic synapses supporting learning in neural models. Over the past decades, many learning rules have been proposed in the literature to explain how neural activity shapes synaptic connections to support adaptive behavior. To ensure an optimal implementation of a large variety of learning rules in hardware, some general and easily parameterized form of learning rule must be designed. This general form learning equation would allow instantiation of multiple learning rules through different parameterizations, without rewiring the hardware. This paper characterizes a subset of local learning rules amenable to implementation in memristive hardware. The analyzed rules belong to four broad classes: Hebb rule derivatives with various methods for gating learning and decay, Threshold rule variations including the covariance and BCM families, Input reconstruction-based learning rules, and Explicit temporal trace-based rules.

KInNeSS: A modular framework for computational neuroscience

Making use of very detailed neurophysiological, anatomical, and behavioral data to build biologically-realistic computational models of animal behavior is often a difficult task. Until recently, many software packages have tried to resolve this mismatched granularity with different approaches. This paper presents KInNeSS, the KDE Integrated NeuroSimulation Software environment, as an alternative solution to bridge the gap between data and model behavior. This open source neural simulation software package provides an expandable framework incorporating features such as ease of use, scalability, an XML based schema, and multiple levels of granularity within a modern object oriented programming design. KInNeSS is best suited to simulate networks of hundreds to thousands of branched multi-compartmental neurons with biophysical properties such as membrane potential, voltage-gated and ligand-gated channels, the presence of gap junctions or ionic diffusion, neuromodulation channel gating, the mechanism for habituative or depressive synapses, axonal delays, and synaptic plasticity. KInNeSS outputs include compartment membrane voltage, spikes, local-field potentials, and current source densities, as well as visualization of the behavior of a simulated agent. An explanation of the modeling philosophy and plug-in development is also presented. Further development of KInNeSS is ongoing with the ultimate goal of creating a modular framework that will help researchers across different disciplines to effectively collaborate using a modern neural simulation platform.

A model of hippocampal circuitry mediating goal-driven navigation in a familiar environment

Abstract Considerable data demonstrates a role for the hippocampus in spatial navigation. Here, we present a detailed model of how the components of hippocampal circuitry might guide movement toward flexible goal locations in a familiar environment. The model contains the following features: (1) route planning is based on the spread of activation; (2) the spread of activation is gated by environmental constraints; (3) multiple goals are visited sequentially; (4) spatial representation is goal-independent; (5) place cells predict the future position by about one θ-cycle; and (6) the model selects the shorter path among alternatives. This model can be further extended to address complex navigational functions.